From f69080ae3019c809cb680e909cdff926b3ed502d Mon Sep 17 00:00:00 2001
From: Christopher Bays <cmbays91@gmail.com>
Date: Tue, 23 Jun 2026 01:30:06 -0400
Subject: [PATCH 1/2] =?UTF-8?q?feat(adapters):=20#470=20=E2=80=94=20raw=5F?=
 =?UTF-8?q?dag=20nodes=20+=20node=5Fmap.compiled=20(N=E2=86=921,=20omit-on?=
 =?UTF-8?q?-ambiguous)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

S2 of the raw⇄compiled DAG spine (epic #468). Delivers the raw structural
DAG NODES + the N→1 compiled⇄raw correspondence, projected once from the
assembled SourceMap into code_map. Edges + node_map.raw (1→N, observed) are
S3 (#471) and are deliberately ABSENT here — never faked.

What landed (in src/adapters/render.rs, beside CodeMapPayload/gather_raw_zones):
- RawDagNodePayload { id, role: cte|terminal|zone, is_zone, presence } +
  RawDagPayload { nodes } + NodeMapPayload { compiled } — Serialize-only
  projections, omit-when-empty so pre-#470 goldens stay byte-stable.
- build_raw_dag_and_node_map: every node a SOUND literal raw_code region —
  verbatim WITH CTEs (via the SHARED #469/#473 masking scan, presence
  compiled_in), the #448 zone entries (honest 3-state presence), and a
  WITH-less model → exactly ONE terminal raw node (mirrors from_cte_graph's
  terminal synthesis) so node_map.compiled stays total.
- node_map.compiled (compiledId → rawId, N→1): verbatim-CTE identity, the
  WITH-less terminal, else a unique structural-containment fold to the ONE
  template zone (the {% for %}-fanned CTEs). unique_containing_zone returns
  None on zero-or-multiple → the key is OMITTED, never a guessed rawId.

The three honesty rules:
- omit-on-ambiguous: a compiled node with no UNIQUE raw origin omits its key.
- sound literal nodes: every raw node is a real lexical region of raw_code
  (never a predicted/fabricated node).
- WITH-less terminal totality + fanned→one-template: one terminal raw node
  for a WITH-less model; all fanned compiled CTEs map back to the ONE zone.
A pruned {% if is_incremental() %} zone is compiled_out (NEVER compiled_in).

TDD (tests before impl, render.rs test module):
nodes-sound · with-less-single-terminal · fanned-nodes-map-to-one-template ·
macro-CTE-no-unique-origin-omits · ambiguous-two-zones-omits ·
incremental-guard-compiled_out · no-raw-structure-omits-both (byte-stable) ·
serialize-wire-shape.

Dogfood fixture: incremental-showcase GOLDEN — a SYNTHETIC manifest whose
fct_events wraps an inner CTE inside {% if is_incremental() %} and whose
compiled_code is the fresh-build shape (guard PRUNED), so the regenerated
golden shows the verbatim `base` CTE as compiled_in and the pruned guard as a
first-class compiled_out zone raw node. Hand-authored compiled_code (no real
dbt compile) → no root_path/username leak; listed in MANIFEST.toml,
synthetic_only.

Goldens: all example reports flipped (code_map JSON grew with raw_dag.nodes +
node_map.compiled); regenerated + byte-verified. jaffle-shop chrome snapshot
accepted (additive raw_dag/node_map only). Source-map completeness guard,
fixture-manifest gate, fmt, clippy --all-targets --locked, full nextest, BDD,
cargo doc -D warnings, cargo deny, crap4rs all green.

Closes #470

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0199AmBCec5kyVEEF1Qd7TSF
---
 .github/workflows/ci.yml                      |    24 +
 examples/comments-showcase-report.html        |     2 +-
 examples/diff-showcase-report.html            |     2 +-
 examples/explore-macro/tests.html             |     2 +-
 examples/explore-seed/tests.html              |     2 +-
 examples/explore/tests.html                   |     2 +-
 examples/incremental-showcase-report.html     | 11920 ++++++++++++++++
 examples/jaffle-shop-report.html              |     2 +-
 examples/playground-report.html               |     2 +-
 examples/prdiff-minidag-report.html           |     2 +-
 examples/seed-showcase-report.html            |     2 +-
 src/adapters/render.rs                        |   588 +-
 tests/fixtures/MANIFEST.toml                  |    74 +
 .../incremental-showcase-current.json         |    96 +
 .../incremental-showcase-pr-diff.patch        |     6 +
 .../models/marts/fct_events.sql               |    15 +
 .../models/staging/stg_events.sql             |     1 +
 ...egration__rendered_chrome_jaffle_shop.snap |     2 +-
 18 files changed, 12733 insertions(+), 11 deletions(-)
 create mode 100644 examples/incremental-showcase-report.html
 create mode 100644 tests/fixtures/incremental-showcase-current.json
 create mode 100644 tests/fixtures/incremental-showcase-pr-diff.patch
 create mode 100644 tests/fixtures/incremental-showcase-source/models/marts/fct_events.sql
 create mode 100644 tests/fixtures/incremental-showcase-source/models/staging/stg_events.sql

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5af168c..bbaaffe 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -779,6 +779,30 @@ jobs:
             output: examples/seed-showcase-report.html
             project_root: tests/fixtures/seed-showcase-source
             experimental: "1"
+          # cute-dbt#470 (epic #468, S2) — the GOLDEN raw-DAG incremental
+          # showcase. A dedicated minimal SYNTHETIC manifest (one incremental
+          # model + its staging dep) whose `fct_events` raw_code wraps an inner
+          # CTE inside a `{% if is_incremental() %}` guard, and whose
+          # compiled_code is the FRESH-build shape (the guard body PRUNED). So
+          # the regenerated golden exercises the raw structural DAG: the
+          # verbatim `base` CTE as a `compiled_in` raw node, and the pruned
+          # incremental guard as a first-class `compiled_out` zone raw node
+          # (never falsely "compiled") — the honest incremental-CTE visibility
+          # the spine was built for. node_map.compiled folds `base → base`
+          # (identity); the pruned zone is type-incapable of a fold target.
+          # Rendered in --pr-diff mode (a synthetic one-line edit to the model
+          # file marks it in-scope). No baseline — the showcase is --pr-diff
+          # only. The compiled manifest is hand-authored (the fresh-build
+          # compile), never from a real `dbt compile`, so it leaks no
+          # root_path/username (synthetic-only fixture invariant).
+          - name: incremental-showcase
+            mode: report
+            current: tests/fixtures/incremental-showcase-current.json
+            baseline: ""
+            pr_diff: tests/fixtures/incremental-showcase-pr-diff.patch
+            output: examples/incremental-showcase-report.html
+            project_root: tests/fixtures/incremental-showcase-source
+            experimental: ""
           # cute-dbt#404 (epic #352) — the GOLDEN PR-scope lineage mini-DAG.
           # A dedicated minimal manifest with a CONNECTOR PATH
           # (stg_orders → int_order_items → fct_orders): stg_orders and
diff --git a/examples/comments-showcase-report.html b/examples/comments-showcase-report.html
index ca7ca01..a97917b 100644
--- a/examples/comments-showcase-report.html
+++ b/examples/comments-showcase-report.html
@@ -2634,7 +2634,7 @@ <h2>Expected</h2>
 <div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
 </div>
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"fct_orders","path":"models/marts/fct_orders.sql","dag":{"nodes":[{"id":"items","role":"import"},{"id":"(final select)","label":"fct_orders.sql","role":"final"}],"edges":[{"from":"items","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select customer_id, count(*) as order_count, sum(amount) as gross_revenue from items group by 1","items":"items as (\n  select customer_id, amount from int_order_items\n)"},"code_map":{"compiled":"with items as (\n  select customer_id, amount from int_order_items\n)\nselect customer_id, count(*) as order_count, sum(amount) as gross_revenue from items group by 1","node_spans":{"(final select)":{"start":{"line":4,"col":1,"byte":68},"end":{"line":4,"col":96,"byte":163}},"items":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":67}}},"column_spans":{"(final 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sum(amount) as gross_revenue\nfrom {{ ref('int_order_items') }}\ngroup by 1","sql_diff":{"lines":[{"kind":"context","text":"select","emphasis":null},{"kind":"context","text":"  customer_id,","emphasis":null},{"kind":"context","text":"  count(*) as order_count,","emphasis":null},{"kind":"removed","text":"  sum(amount) as revenue","emphasis":null},{"kind":"added","text":"  sum(amount) as gross_revenue","emphasis":[17,23]},{"kind":"context","text":"from {{ ref('int_order_items') }}","emphasis":null},{"kind":"context","text":"group by 1","emphasis":null}]},"model_yaml":{"path":"models/marts/_fct_orders_tests.yml","raw":"  - name: fct_orders\n    config:\n      materialized: incremental","diff":{"lines":[{"kind":"added","text":"  - name: fct_orders","emphasis":null},{"kind":"added","text":"    config:","emphasis":null},{"kind":"added","text":"      materialized: 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}","given":[{"input":"ref('int_order_items')","bound_to_node":"items","table":{"columns":["amount","customer_id","order_id"],"rows":[{"cells":[{"display":"12","key":{"t":"number","v":"12"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"8","key":{"t":"number","v":"8"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"amount":12,"customer_id":7,"order_id":1},{"amount":8,"customer_id":7,"order_id":2}],"format":"dict"}],"expected":{"table":{"columns":["customer_id","gross_revenue","order_count"],"rows":[{"cells":[{"display":"7","key":{"t":"number","v":"7"}},{"display":"20","key":{"t":"number","v":"20"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"customer_id":7,"gross_revenue":20,"order_count":2}],"format":"dict"}}],"is_recursive":false,"axes":{"body":true,"config":true,"unit_test":true},"config_file":"models/marts/_fct_orders_tests.yml","state":"modified"},{"name":"stg_orders","path":"models/staging/stg_orders.sql","dag":{"nodes":[{"id":"src","role":"import"},{"id":"(final 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order_id, customer_id, status, amount","emphasis":[32,40]},{"kind":"context","text":"  from {{ source('raw', 'orders') }}","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"select * from src","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[{"id":"unit_test.shop.stg_orders.test_status_kept","name":"test_status_kept","target_model":"stg_orders","changed":false,"defined_in":"models/staging/_stg_orders_tests.yml","authoring_yaml":"  - name: test_status_kept\n    model: stg_orders\n    given:\n      - input: source('raw', 'orders')\n        rows:\n          - { order_id: 1, customer_id: 7, status: placed, amount: 12 }\n    expect:\n      rows:\n        - { order_id: 1, customer_id: 7, status: placed, amount: 12 }","given":[{"input":"source('raw', 'orders')","table":{"columns":["amount","customer_id","order_id","status"],"rows":[{"cells":[{"display":"12","key":{"t":"number","v":"12"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"placed","key":{"t":"str","v":"placed"}}]}]},"rows":[{"amount":12,"customer_id":7,"order_id":1,"status":"placed"}],"format":"dict"}],"expected":{"table":{"columns":["amount","customer_id","order_id","status"],"rows":[{"cells":[{"display":"12","key":{"t":"number","v":"12"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"placed","key":{"t":"str","v":"placed"}}]}]},"rows":[{"amount":12,"customer_id":7,"order_id":1,"status":"placed"}],"format":"dict"}}],"is_recursive":false,"axes":{"body":true,"config":false,"unit_test":true},"state":"modified"}],"pr_dag":{"graph":{"nodes":[{"id":"model.shop.fct_orders","name":"fct_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1},{"id":"model.shop.int_order_items","name":"int_order_items","state":"modified","is_connector":true,"lines_added":0,"lines_removed":0},{"id":"model.shop.stg_orders","name":"stg_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1}],"edges":[{"from":"model.shop.int_order_items","to":"model.shop.fct_orders"},{"from":"model.shop.stg_orders","to":"model.shop.int_order_items"}]},"modified_count":2,"connector_count":1,"halo_count":0,"deleted_count":0,"collapsed":false,"by_axis":{"body":{"graph":{"nodes":[{"id":"model.shop.fct_orders","name":"fct_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1},{"id":"model.shop.int_order_items","name":"int_order_items","state":"modified","is_connector":true,"lines_added":0,"lines_removed":0},{"id":"model.shop.stg_orders","name":"stg_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1}],"edges":[{"from":"model.shop.int_order_items","to":"model.shop.fct_orders"},{"from":"model.shop.stg_orders","to":"model.shop.int_order_items"}]},"modified_count":2,"connector_count":1,"halo_count":0,"deleted_count":0,"collapsed":false},"config":{"graph":{"nodes":[{"id":"model.shop.fct_orders","name":"fct_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1},{"id":"model.shop.int_order_items","name":"int_order_items","state":"modified","is_connector":false,"is_halo":true,"lines_added":0,"lines_removed":0}],"edges":[{"from":"model.shop.int_order_items","to":"model.shop.fct_orders"}]},"modified_count":1,"connector_count":0,"halo_count":1,"deleted_count":0,"collapsed":false},"unit_test":{"graph":{"nodes":[{"id":"model.shop.fct_orders","name":"fct_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1},{"id":"model.shop.int_order_items","name":"int_order_items","state":"modified","is_connector":true,"lines_added":0,"lines_removed":0},{"id":"model.shop.stg_orders","name":"stg_orders","state":"modified","is_connector":false,"lines_added":1,"lines_removed":1}],"edges":[{"from":"model.shop.int_order_items","to":"model.shop.fct_orders"},{"from":"model.shop.stg_orders","to":"model.shop.int_order_items"}]},"modified_count":2,"connector_count":1,"halo_count":0,"deleted_count":0,"collapsed":false}}},"pr_comments":{"by_model":[{"model":"model.shop.fct_orders","model_path":"models/marts/fct_orders.sql","count":2,"threads":[{"model":"model.shop.fct_orders","path":"models/marts/fct_orders.sql","original_line":9,"side":"Right","within_hunk":false,"resolved":false,"outdated":true,"comments":[{"author":null,"body":"This grouping changed since I commented — please re-check the rollup grain."}]},{"model":"model.shop.fct_orders","path":"models/marts/fct_orders.sql","line":4,"original_line":4,"side":"Right","within_hunk":true,"resolved":true,"outdated":false,"comments":[{"author":"octocat","body":"nit: `gross_revenue` reads clearer than `revenue` — thanks for the rename."}]}]},{"model":"model.shop.stg_orders","model_path":"models/staging/stg_orders.sql","count":1,"threads":[{"model":"model.shop.stg_orders","path":"models/staging/stg_orders.sql","line":2,"original_line":2,"side":"Right","within_hunk":true,"resolved":false,"outdated":false,"comments":[{"author":"octocat","body":"Adding `status` here is great — can we confirm it is non-null in the source?"},{"author":"data-reviewer","body":"Good call. The source guarantees it, so no coalesce needed."}]}]}],"total":3}}</script>
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grouping changed since I commented — please re-check the rollup grain."}]},{"model":"model.shop.fct_orders","path":"models/marts/fct_orders.sql","line":4,"original_line":4,"side":"Right","within_hunk":true,"resolved":true,"outdated":false,"comments":[{"author":"octocat","body":"nit: `gross_revenue` reads clearer than `revenue` — thanks for the rename."}]}]},{"model":"model.shop.stg_orders","model_path":"models/staging/stg_orders.sql","count":1,"threads":[{"model":"model.shop.stg_orders","path":"models/staging/stg_orders.sql","line":2,"original_line":2,"side":"Right","within_hunk":true,"resolved":false,"outdated":false,"comments":[{"author":"octocat","body":"Adding `status` here is great — can we confirm it is non-null in the source?"},{"author":"data-reviewer","body":"Good call. The source guarantees it, so no coalesce needed."}]}]}],"total":3}}</script>
 
 <script>
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diff --git a/examples/diff-showcase-report.html b/examples/diff-showcase-report.html
index 1e416fa..fcc69a2 100644
--- a/examples/diff-showcase-report.html
+++ b/examples/diff-showcase-report.html
@@ -3197,7 +3197,7 @@ <h2>Project definition changed</h2>
 </section>
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last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , 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select)\u001f*":{"start":{"line":105,"col":8,"byte":2688},"end":{"line":105,"col":9,"byte":2689}},"condition_aggregates\u001fcondition_code":{"start":{"line":20,"col":9,"byte":465},"end":{"line":20,"col":23,"byte":479}},"condition_aggregates\u001fcondition_description":{"start":{"line":21,"col":11,"byte":490},"end":{"line":21,"col":32,"byte":511}},"condition_aggregates\u001fencounter_count":{"start":{"line":25,"col":11,"byte":727},"end":{"line":25,"col":58,"byte":774}},"condition_aggregates\u001ffirst_occurrence_date":{"start":{"line":22,"col":11,"byte":522},"end":{"line":22,"col":61,"byte":572}},"condition_aggregates\u001flast_occurrence_date":{"start":{"line":23,"col":11,"byte":583},"end":{"line":23,"col":90,"byte":662}},"condition_aggregates\u001fpatient_count":{"start":{"line":24,"col":11,"byte":673},"end":{"line":24,"col":54,"byte":716}},"conditions_source\u001fcondition_code":{"start":{"line":8,"col":14,"byte":139},"end":{"line":8,"col":58,"byte":183}},"conditions_source\u001fcondition_description":{"start":{"line":9,"col":11,"byte":194},"end":{"line":9,"col":32,"byte":215}},"conditions_source\u001fcondition_end_date":{"start":{"line":13,"col":11,"byte":301},"end":{"line":13,"col":29,"byte":319}},"conditions_source\u001fcondition_start_date":{"start":{"line":12,"col":11,"byte":270},"end":{"line":12,"col":31,"byte":290}},"conditions_source\u001fencounter_id":{"start":{"line":11,"col":11,"byte":247},"end":{"line":11,"col":23,"byte":259}},"conditions_source\u001fpatient_id":{"start":{"line":10,"col":11,"byte":226},"end":{"line":10,"col":21,"byte":236}},"deduped\u001fcondition_code":{"start":{"line":33,"col":9,"byte":949},"end":{"line":33,"col":23,"byte":963}},"deduped\u001fcondition_description":{"start":{"line":34,"col":11,"byte":974},"end":{"line":34,"col":32,"byte":995}},"deduped\u001fencounter_count":{"start":{"line":38,"col":11,"byte":1093},"end":{"line":38,"col":26,"byte":1108}},"deduped\u001ffirst_occurrence_date":{"start":{"line":35,"col":11,"byte":1006},"end":{"line":35,"col":32,"byte":1027}},"deduped\u001flast_occurrence_date":{"start":{"line":36,"col":11,"byte":1038},"end":{"line":36,"col":31,"byte":1058}},"deduped\u001fpatient_count":{"start":{"line":37,"col":11,"byte":1069},"end":{"line":37,"col":24,"byte":1082}},"deduped\u001frn":{"start":{"line":39,"col":11,"byte":1119},"end":{"line":42,"col":16,"byte":1263}},"sequenced\u001fcondition_code":{"start":{"line":52,"col":11,"byte":1496},"end":{"line":52,"col":25,"byte":1510}},"sequenced\u001fcondition_description":{"start":{"line":55,"col":11,"byte":1544},"end":{"line":55,"col":32,"byte":1565}},"sequenced\u001fcondition_key":{"start":{"line":49,"col":9,"byte":1401},"end":{"line":49,"col":69,"byte":1461}},"sequenced\u001fencounter_count":{"start":{"line":61,"col":11,"byte":1683},"end":{"line":61,"col":26,"byte":1698}},"sequenced\u001ffirst_occurrence_date":{"start":{"line":56,"col":11,"byte":1576},"end":{"line":56,"col":32,"byte":1597}},"sequenced\u001flast_occurrence_date":{"start":{"line":57,"col":11,"byte":1608},"end":{"line":57,"col":31,"byte":1628}},"sequenced\u001fpatient_count":{"start":{"line":60,"col":11,"byte":1659},"end":{"line":60,"col":24,"byte":1672}},"unknown_member\u001fcondition_code":{"start":{"line":71,"col":11,"byte":1864},"end":{"line":71,"col":38,"byte":1891}},"unknown_member\u001fcondition_description":{"start":{"line":72,"col":11,"byte":1902},"end":{"line":72,"col":55,"byte":1946}},"unknown_member\u001fcondition_key":{"start":{"line":70,"col":10,"byte":1835},"end":{"line":70,"col":28,"byte":1853}},"unknown_member\u001fencounter_count":{"start":{"line":76,"col":11,"byte":2093},"end":{"line":76,"col":31,"byte":2113}},"unknown_member\u001ffirst_occurrence_date":{"start":{"line":73,"col":16,"byte":1962},"end":{"line":73,"col":54,"byte":2000}},"unknown_member\u001flast_occurrence_date":{"start":{"line":74,"col":16,"byte":2016},"end":{"line":74,"col":53,"byte":2053}},"unknown_member\u001fpatient_count":{"start":{"line":75,"col":11,"byte":2064},"end":{"line":75,"col":29,"byte":2082}}},"raw_node_spans":{"condition_aggregates":{"start":{"line":18,"col":1,"byte":932},"end":{"line":28,"col":2,"byte":1366}},"conditions_source":{"start":{"line":5,"col":6,"byte":534},"end":{"line":16,"col":2,"byte":929}},"deduped":{"start":{"line":31,"col":1,"byte":1429},"end":{"line":44,"col":2,"byte":1807}},"final":{"start":{"line":79,"col":1,"byte":2630},"end":{"line":103,"col":2,"byte":3191}},"sequenced":{"start":{"line":46,"col":1,"byte":1810},"end":{"line":65,"col":2,"byte":2247}},"unknown_member":{"start":{"line":68,"col":1,"byte":2307},"end":{"line":77,"col":2,"byte":2627}}},"raw_column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":3200},"end":{"line":105,"col":9,"byte":3201}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: dim_conditions\n    description: |\n      Master condition dimension for healthcare analytics.\n      One row per unique ICD/SNOMED condition code, deduplicated from staging.\n      Use for filtering and grouping by condition across fact tables.\n\n      Use cases:\n      - Condition-based cohort analysis\n      - Disease prevalence tracking\n      - Condition frequency and distribution reporting\n      - Join target for condition-related facts\n\n    config:\n      tags: ['analytics', 'marts', 'dimension']\n    meta:\n      grain: condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Condition lookup and filtering\"\n        - \"Cohort analysis by diagnosis\"\n        - \"Disease prevalence reporting\"\n      example_queries:\n        - name: \"Top 10 most common conditions\"\n          description: \"Identify the most prevalent conditions in the patient population\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , patient_count\n                , encounter_count\n            from main_marts.dim_conditions\n            order by patient_count desc\n            limit 10\n\n        - name: \"Conditions by encounter frequency\"\n          description: \"Find conditions that drive the most healthcare utilization\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , encounter_count\n                , patient_count\n                , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\n            from main_marts.dim_conditions\n            where patient_count > 0\n            order by encounter_count desc\n            limit 20\n\n        - name: \"Recently diagnosed conditions\"\n          description: \"Find conditions first diagnosed in the last year\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , first_occurrence_date\n                , last_occurrence_date\n                , patient_count\n            from main_marts.dim_conditions\n            where first_occurrence_date >= current_date - interval '1 year'\n            order by first_occurrence_date desc\n\n    columns:\n      - name: condition_key\n        description: Surrogate key (row_number)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_code\n        description: ICD/SNOMED code (natural key)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_description\n        description: Full condition name/description\n        data_tests:\n          - not_null\n\n      - name: first_occurrence_date\n        description: Earliest date this condition was diagnosed in the dataset\n\n      - name: last_occurrence_date\n        description: Most recent date this condition was diagnosed\n\n      - name: patient_count\n        description: Number of unique patients with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Total number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. Valid range is years 0001-2147483\n    (integer max 2,147,483,647). Safe for all practical date ranges.\n\n    Note on day_of_week: dbt_date uses US convention (Sunday=1, Saturday=7),\n    NOT ISO 8601 (Monday=1). Weekend and holiday logic accounts for this.\n#}\n\nwith date_spine as (\n    {{ dbt_date.get_date_dimension('1909-01-01', '2025-12-31') }}\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_date\n    description: >\n      Calendar dimension covering 1909-01-01 to 2025-12-31.\n      Provides date attributes, day/week/month/quarter/year hierarchies,\n      weekend flags, and US holiday identification.\n    data_tests:\n      # Row count validation - covers 117 years of dates (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 42000\n          config:\n            severity: warn\n    columns:\n      - name: date_key\n        description: Surrogate key in YYYYMMDD integer format (e.g., 20240115)\n        data_tests:\n          - unique\n          - not_null\n          # Date key must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 19090101\n              max_value: 20251231\n              config:\n                severity: warn\n\n      - name: date_actual\n        description: The actual date value\n        data_tests:\n          - unique\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1909-01-01'::date\"\n              max_value: \"'2025-12-31'::date\"\n              config:\n                severity: warn\n\n      - name: day_of_week\n        description: Day of week number (US convention - Sunday=1, Saturday=7)\n        data_tests:\n          - not_null\n          # Day of week must be 1-7 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 7\n              config:\n                severity: warn\n\n      - name: day_name\n        description: Full day name (e.g., Monday, Tuesday)\n        data_tests:\n          - not_null\n\n      - name: day_name_short\n        description: Abbreviated day name (e.g., Mon, Tue)\n        data_tests:\n          - not_null\n\n      - name: day_of_month\n        description: Day of the month (1-31)\n        data_tests:\n          - not_null\n          # Day of month must be 1-31 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 31\n              config:\n                severity: warn\n\n      - name: day_of_year\n        description: Day of the year (1-366)\n        data_tests:\n          - not_null\n          # Day of year must be 1-366 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 366\n              config:\n                severity: warn\n\n      - name: week_of_year\n        description: Week of the year (1-53)\n        data_tests:\n          - not_null\n          # Week of year must be 1-53 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 53\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: month_name\n        description: Full month name (e.g., January, February)\n        data_tests:\n          - not_null\n\n      - name: month_name_short\n        description: Abbreviated month name (e.g., Jan, Feb)\n        data_tests:\n          - not_null\n\n      - name: quarter_actual\n        description: Quarter number (1-4)\n        data_tests:\n          - not_null\n          # Quarter must be 1-4 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 4\n              config:\n                severity: warn\n\n      - name: quarter_name\n        description: Quarter name (Q1, Q2, Q3, Q4)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['Q1', 'Q2', 'Q3', 'Q4']\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: is_weekend\n        description: Boolean flag indicating if date falls on Saturday or Sunday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: is_us_holiday\n        description: Boolean flag indicating if date is a recognized US holiday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: holiday_name\n        description: Name of the US holiday (null if not a holiday)"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_id.8ca9bc4237"}],"documented":true},"organization_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_key.a539e5f42a"},{"kind":"unique","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"}],"documented":true},"organization_name":{"description":"Name of the healthcare organization","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_name","model":"{{ get_where_subquery(ref('dim_organizations')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_organizations_organization_name.7fb64e1577"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_organizations')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_organizations_revenue__0__revenue_is_not_null.3e63617295"}],"documented":true},"state":{"description":"State (full state 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_organizations\n    description: >\n      Organization dimension containing healthcare facilities.\n      Includes an unknown member row (organization_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 800\n          config:\n            severity: warn\n    columns:\n      - name: organization_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: organization_id\n        description: Natural organization identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_name\n        description: Name of the healthcare organization\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: revenue\n        description: Total revenue\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from \u003c= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_patients\n    description: >\n      Patient dimension with SCD Type 2 history tracking.\n      Each row represents a specific version of a patient's record.\n      Includes an unknown member row (patient_key = -1) for handling missing lookups.\n\n      SCD Type 2 columns: patient_key (surrogate), patient_id (natural),\n      valid_from, valid_to, is_current.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_key\n        description: Surrogate key (unique per version, -1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_id\n        description: Natural patient identifier (UUID), same across SCD2 versions\n        data_tests:\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: full_name\n        description: Patient's full name (first + last)\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          # Date range validation - exclude unknown member (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"birth_date is not null and patient_key != -1\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: age_years\n        description: Current age or age at death (in years)\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"age_years is not null\"\n              config:\n                severity: warn\n\n      - name: is_deceased\n        description: Boolean flag indicating if patient has died\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"patient_key != -1\"\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: valid_from\n        description: Date when this SCD2 version became active\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: valid_to\n        description: Date when this SCD2 version was superseded (null = current)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"valid_to is not null\"\n              config:\n                severity: warn\n\n      - name: is_current\n        description: Boolean flag indicating if this is the current version\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_providers","path":"models/marts/core/dim_providers.sql","description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_providers.sql","role":"final"}],"edges":[{"from":"providers","to":"sequenced","edge_type":"from"},{"from":"organizations","to":"sequenced","edge_type":"left"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":76,"col":1,"byte":1766},"end":{"line":76,"col":20,"byte":1785}},"final":{"start":{"line":70,"col":1,"byte":1677},"end":{"line":74,"col":2,"byte":1764}},"organizations":{"start":{"line":7,"col":1,"byte":93},"end":{"line":12,"col":2,"byte":226}},"providers":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":90}},"sequenced":{"start":{"line":14,"col":1,"byte":229},"end":{"line":48,"col":2,"byte":972}},"unknown_member":{"start":{"line":51,"col":1,"byte":1032},"end":{"line":68,"col":2,"byte":1674}}},"column_spans":{"(final select)\u001f*":{"start":{"line":76,"col":8,"byte":1773},"end":{"line":76,"col":9,"byte":1774}},"organizations\u001forganization_id":{"start":{"line":10,"col":11,"byte":158},"end":{"line":10,"col":26,"byte":173}},"organizations\u001forganization_key":{"start":{"line":9,"col":9,"byte":131},"end":{"line":9,"col":25,"byte":147}},"providers\u001f*":{"start":{"line":4,"col":12,"byte":33},"end":{"line":4,"col":13,"byte":34}},"sequenced\u001f_loaded_at":{"start":{"line":43,"col":11,"byte":838},"end":{"line":43,"col":42,"byte":869}},"sequenced\u001faddress":{"start":{"line":32,"col":11,"byte":654},"end":{"line":32,"col":20,"byte":663}},"sequenced\u001fcity":{"start":{"line":33,"col":11,"byte":674},"end":{"line":33,"col":17,"byte":680}},"sequenced\u001fgender":{"start":{"line":28,"col":11,"byte":592},"end":{"line":28,"col":19,"byte":600}},"sequenced\u001flatitude":{"start":{"line":36,"col":11,"byte":730},"end":{"line":36,"col":21,"byte":740}},"sequenced\u001flongitude":{"start":{"line":37,"col":11,"byte":751},"end":{"line":37,"col":22,"byte":762}},"sequenced\u001forganization_id":{"start":{"line":24,"col":11,"byte":515},"end":{"line":24,"col":28,"byte":532}},"sequenced\u001forganization_key":{"start":{"line":23,"col":11,"byte":486},"end":{"line":23,"col":29,"byte":504}},"sequenced\u001fprovider_id":{"start":{"line":20,"col":11,"byte":425},"end":{"line":20,"col":24,"byte":438}},"sequenced\u001fprovider_key":{"start":{"line":17,"col":9,"byte":332},"end":{"line":17,"col":67,"byte":390}},"sequenced\u001fprovider_name":{"start":{"line":27,"col":11,"byte":566},"end":{"line":27,"col":26,"byte":581}},"sequenced\u001fspecialty":{"start":{"line":29,"col":11,"byte":611},"end":{"line":29,"col":22,"byte":622}},"sequenced\u001fstate":{"start":{"line":34,"col":11,"byte":691},"end":{"line":34,"col":18,"byte":698}},"sequenced\u001futilization":{"start":{"line":40,"col":11,"byte":793},"end":{"line":40,"col":24,"byte":806}},"sequenced\u001fzip_code":{"start":{"line":35,"col":11,"byte":709},"end":{"line":35,"col":21,"byte":719}},"unknown_member\u001f_loaded_at":{"start":{"line":67,"col":11,"byte":1641},"end":{"line":67,"col":42,"byte":1672}},"unknown_member\u001faddress":{"start":{"line":60,"col":16,"byte":1345},"end":{"line":60,"col":43,"byte":1372}},"unknown_member\u001fcity":{"start":{"line":61,"col":16,"byte":1388},"end":{"line":61,"col":40,"byte":1412}},"unknown_member\u001fgender":{"start":{"line":58,"col":16,"byte":1258},"end":{"line":58,"col":42,"byte":1284}},"unknown_member\u001flatitude":{"start":{"line":64,"col":16,"byte":1513},"end":{"line":64,"col":43,"byte":1540}},"unknown_member\u001flongitude":{"start":{"line":65,"col":16,"byte":1556},"end":{"line":65,"col":44,"byte":1584}},"unknown_member\u001forganization_id":{"start":{"line":56,"col":11,"byte":1168},"end":{"line":56,"col":39,"byte":1196}},"unknown_member\u001forganization_key":{"start":{"line":55,"col":12,"byte":1136},"end":{"line":55,"col":33,"byte":1157}},"unknown_member\u001fprovider_id":{"start":{"line":54,"col":11,"byte":1100},"end":{"line":54,"col":35,"byte":1124}},"unknown_member\u001fprovider_key":{"start":{"line":53,"col":10,"byte":1072},"end":{"line":53,"col":27,"byte":1089}},"unknown_member\u001fprovider_name":{"start":{"line":57,"col":11,"byte":1207},"end":{"line":57,"col":46,"byte":1242}},"unknown_member\u001fspecialty":{"start":{"line":59,"col":16,"byte":1300},"end":{"line":59,"col":45,"byte":1329}},"unknown_member\u001fstate":{"start":{"line":62,"col":16,"byte":1428},"end":{"line":62,"col":41,"byte":1453}},"unknown_member\u001futilization":{"start":{"line":66,"col":16,"byte":1600},"end":{"line":66,"col":46,"byte":1630}},"unknown_member\u001fzip_code":{"start":{"line":63,"col":16,"byte":1469},"end":{"line":63,"col":44,"byte":1497}}},"raw_node_spans":{"final":{"start":{"line":70,"col":1,"byte":1740},"end":{"line":74,"col":2,"byte":1827}},"organizations":{"start":{"line":7,"col":1,"byte":167},"end":{"line":12,"col":2,"byte":289}},"providers":{"start":{"line":3,"col":6,"byte":94},"end":{"line":5,"col":2,"byte":164}},"sequenced":{"start":{"line":14,"col":1,"byte":292},"end":{"line":48,"col":2,"byte":1035}},"unknown_member":{"start":{"line":51,"col":1,"byte":1095},"end":{"line":68,"col":2,"byte":1737}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"gender":{"description":"Provider gender (M or F)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('dim_providers')) }}","values":["M","F"]},"node_id":"test.healthcare_analytics.accepted_values_dim_providers_gender__M__F.248d04b44f"}],"documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_id.c650c07852"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_key.d5b2be228b"},{"kind":"relationships","kwargs":{"column_name":"organization_key","field":"organization_key","model":"{{ get_where_subquery(ref('dim_providers')) }}","to":"ref('dim_organizations')"},"node_id":"test.healthcare_analytics.relationships_dim_providers_organization_key__organization_key__ref_dim_organizations_.905f82d6ac"}],"documented":true},"provider_id":{"description":"Natural provider identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"provider_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_provider_id.2f5a85b6ff"}],"documented":true},"provider_key":{"description":"Surrogate key (-1 for unknown 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count","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"utilization","min_value":0,"model":"{{ get_where_subquery(ref('dim_providers')) }}","row_condition":"utilization is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_providers_utilization__0__utilization_is_not_null.175a27ff73"}],"documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__providers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"providers"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_organizations"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"organizations"}},"column":"organization_key"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"organization_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"provider_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"provider_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"gender"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"gender"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"specialty"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"specialty"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"latitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"latitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"longitude"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"longitude"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"providers"}},"column":"utilization"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"utilization"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='provider_key'\n    )\n}}\n\nwith providers as (\n    select * from {{ ref('stg_synthea__providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from {{ ref('dim_organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_providers\n    description: >\n      Provider dimension containing healthcare providers (doctors, nurses, etc.).\n      Includes an unknown member row (provider_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 4000\n          config:\n            severity: warn\n    columns:\n      - name: provider_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n        data_tests:\n          - not_null\n\n      - name: gender\n        description: Provider gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"provider_key != -1\"\n\n      - name: specialty\n        description: Medical specialty\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_providers","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('dim_organizations'), ref('stg_synthea__providers')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_condition_cohorts.sql","role":"final"}],"edges":[{"from":"conditions_source","to":"condition_windows","edge_type":"from"},{"from":"condition_windows","to":"with_dimensions","edge_type":"from"},{"from":"patients","to":"with_dimensions","edge_type":"left"},{"from":"dim_conditions","to":"with_dimensions","edge_type":"left"},{"from":"with_dimensions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_windows":"condition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n)","conditions_source":"conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n)","dim_conditions":"dim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , 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condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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select)"}},"column":"first_diagnosis_year"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"is_active"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"is_active"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_condition_cohorts\n    description: |\n      Patient-condition cohort analysis for disease management and population health.\n      One row per unique patient-condition combination with timeline and encounter details.\n      Grain: (patient_id, condition_code)\n\n      Use cases:\n      - Disease management program enrollment\n      - Cohort identification for clinical trials\n      - Chronic condition tracking\n      - Care gap analysis\n      - Risk stratification by condition\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Disease management programs\"\n        - \"Cohort identification\"\n        - \"Chronic condition tracking\"\n        - \"Care gap analysis\"\n      example_queries:\n        - name: \"Largest condition cohorts\"\n          description: \"Find conditions with the most patients for population health programs\"\n          sql: |\n            select\n                fcc.condition_code\n                , fcc.condition_description\n                , count(distinct fcc.patient_id) as patient_count\n                , sum(fcc.encounter_count) as total_encounters\n                , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\n                , sum(case when fcc.is_active then 1 else 0 end) as active_patients\n            from main_marts.fct_condition_cohorts fcc\n            group by 1, 2\n            order by patient_count desc\n            limit 15\n\n        - name: \"Chronic condition patients (long duration)\"\n          description: \"Identify patients with long-standing conditions for care management\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fcc.condition_description\n                , fcc.first_diagnosis_date\n                , fcc.months_with_condition\n                , fcc.encounter_count\n                , fcc.is_active\n            from main_marts.fct_condition_cohorts fcc\n            join main_marts.dim_patients p\n                on fcc.patient_key = p.patient_key\n            where fcc.months_with_condition >= 24\n                and fcc.is_active = true\n            order by fcc.months_with_condition desc\n            limit 50\n\n        - name: \"Patients with multiple conditions\"\n          description: \"Find patients with comorbidities for complex care programs\"\n          sql: |\n            with patient_condition_counts as (\n                select\n                    patient_id\n                    , patient_key\n                    , count(distinct condition_code) as condition_count\n                    , sum(case when is_active then 1 else 0 end) as active_condition_count\n                from main_marts.fct_condition_cohorts\n                group by 1, 2\n            )\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , pcc.condition_count\n                , pcc.active_condition_count\n            from patient_condition_counts pcc\n            join main_marts.dim_patients p\n                on pcc.patient_key = p.patient_key\n            where pcc.condition_count >= 3\n            order by pcc.condition_count desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - condition_code\n          config:\n            severity: error\n    columns:\n      - name: condition_cohort_key\n        description: Surrogate key (MD5 hash of patient_id + condition_code)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n        data_tests:\n          - not_null\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n        data_tests:\n          - not_null\n\n      - name: last_diagnosis_date\n        description: Date of most recent diagnosis (null if first occurrence only)\n\n      - name: first_diagnosis_year\n        description: Year of first diagnosis\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: months_with_condition\n        description: Months between first and last diagnosis (0 if single occurrence)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: is_active\n        description: True if condition has no end date (currently active)\n        data_tests:\n          - not_null\n          - accepted_values:\n              arguments:\n                values: [true, false]\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_cost_analysis\n    description: |\n      Detailed cost breakdown for financial analysis and revenue cycle management.\n      One row per encounter with cost allocation details across payer and patient.\n      Grain: encounter_id\n\n      Use cases:\n      - Financial performance reporting\n      - Payer mix analysis\n      - Patient out-of-pocket tracking\n      - Cost driver identification\n      - Revenue cycle optimization\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: encounter_id\n      owner: healthcare_analytics\n      uses:\n        - \"Financial reporting\"\n        - \"Payer mix analysis\"\n        - \"Cost allocation tracking\"\n        - \"Revenue cycle management\"\n      example_queries:\n        - name: \"Cost breakdown by payer\"\n          description: \"Analyze cost distribution across different insurance payers\"\n          sql: |\n            select\n                py.payer_name\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            group by py.payer_name\n            order by total_cost desc\n\n        - name: \"Monthly cost trends\"\n          description: \"Track cost trends over time for financial forecasting\"\n          sql: |\n            select\n                strftime(fca.encounter_date, '%Y-%m') as year_month\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            where fca.year_actual >= 2023\n            group by 1\n            order by 1 desc\n\n        - name: \"Cost by encounter type\"\n          description: \"Compare costs across different encounter classes\"\n          sql: |\n            select\n                fca.encounter_class\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost\n                , round(avg(fca.procedure_count), 2) as avg_procedures\n                , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            group by fca.encounter_class\n            order by total_cost desc\n\n        - name: \"High-cost encounters\"\n          description: \"Identify individual high-cost encounters for review\"\n          sql: |\n            select\n                fca.encounter_id\n                , fca.encounter_date\n                , fca.encounter_class\n                , p.first_name || ' ' || p.last_name as patient_name\n                , py.payer_name\n                , fca.total_cost\n                , fca.payer_coverage\n                , fca.patient_responsibility\n                , fca.procedure_count\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_patients p\n                on fca.patient_key = p.patient_key\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            order by fca.total_cost desc\n            limit 25\n\n    columns:\n      - name: cost_analysis_key\n        description: Surrogate key (MD5 hash of encounter_id)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('fct_encounters')\n                field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: payer_key\n        description: Foreign key to dim_payers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_payers')\n                field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n\n      - name: encounter_date_key\n        description: Foreign key to dim_date\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: year_actual\n        description: Calendar year of encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, etc.)\n\n      - name: total_cost\n        description: Total claim cost\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: coverage_pct\n        description: Percentage covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: patient_cost_pct\n        description: Percentage owed by patient (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: cost_per_procedure\n        description: Average cost per procedure (null if no procedures)\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final 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},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp 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from {{ ref('dim_payers') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters\n    description: >\n      Encounter fact table containing healthcare visits/encounters.\n      Links to patient, provider, organization, and payer dimensions.\n      Includes date keys, costs, and derived duration.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 40000\n          config:\n            severity: warn\n    columns:\n      - name: encounter_key\n        description: Surrogate key for the encounter\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_id\n        description: Natural encounter identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_providers')\n              field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: payer_key\n        description: Foreign key to dim_payers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_payers')\n              field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_start_date_key\n        description: Foreign key to dim_date for encounter start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"encounter_start_date_key != -1\"\n\n      - name: encounter_end_date_key\n        description: Foreign key to dim_date for encounter end\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, emergency, etc.)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_code\n        description: SNOMED CT code for the encounter type\n\n      - name: encounter_description\n        description: Description of the encounter type\n\n      - name: reason_code\n        description: SNOMED CT code for the reason of visit\n\n      - name: reason_description\n        description: Description of the reason for visit\n\n      - name: encounter_start_at\n        description: Start timestamp of the encounter\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              config:\n                severity: warn\n\n      - name: encounter_end_at\n        description: End timestamp of the encounter\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              row_condition: \"encounter_end_at is not null\"\n              config:\n                severity: warn\n\n      - name: duration_minutes\n        description: Duration of encounter in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: base_encounter_cost\n        description: Base cost of the encounter\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"base_encounter_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Total claim cost\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: payer_coverage\n        description: Amount covered by payer\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: patient_responsibility\n        description: Amount patient is responsible for (total_claim_cost - payer_coverage)\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: patient_age_at_encounter\n        description: Patient's age at the time of the encounter\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"patient_age_at_encounter is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"unique_key","value":"encounter_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"constraint.primary_key[encounter_key]","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"constraint","value":"primary_key on encounter_key"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":13,"col":1,"byte":233},"end":{"line":22,"col":1,"byte":450}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":11,"col":2,"byte":231}}},"raw_zones":[{"kind":"for_loop","start":{"line":16,"col":9,"byte":304},"end":{"line":18,"col":21,"byte":428},"presence":"structural","node_id":"source"},{"kind":"incremental_guard","start":{"line":29,"col":1,"byte":594},"end":{"line":31,"col":12,"byte":691},"presence":"structural","node_id":"(final select)"}],"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":14,"col":5,"byte":244},"end":{"line":14,"col":17,"byte":256}},"(final select)\u001fencounter_type":{"start":{"line":16,"col":7,"byte":280},"end":{"line":16,"col":21,"byte":294}},"(final select)\u001fpatient_id":{"start":{"line":15,"col":7,"byte":263},"end":{"line":15,"col":17,"byte":273}},"(final select)\u001fstart_time":{"start":{"line":17,"col":7,"byte":301},"end":{"line":17,"col":17,"byte":311}},"(final select)\u001ftotal_cost_rounded":{"start":{"line":18,"col":7,"byte":318},"end":{"line":18,"col":25,"byte":336}},"source\u001fencounter_id":{"start":{"line":5,"col":9,"byte":38},"end":{"line":5,"col":21,"byte":50}},"source\u001fencounter_type":{"start":{"line":7,"col":11,"byte":82},"end":{"line":7,"col":25,"byte":96}},"source\u001fpatient_id":{"start":{"line":6,"col":11,"byte":61},"end":{"line":6,"col":21,"byte":71}},"source\u001fstart_time":{"start":{"line":8,"col":11,"byte":107},"end":{"line":8,"col":21,"byte":117}},"source\u001ftotal_cost_rounded":{"start":{"line":9,"col":11,"byte":128},"end":{"line":9,"col":53,"byte":170}}},"raw_node_spans":{"source":{"start":{"line":3,"col":6,"byte":185},"end":{"line":13,"col":2,"byte":476}}},"raw_column_spans":{"(final select)\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}},"source\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost_rounded"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost_rounded"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        {% for cost_col in rounded_cost_columns %}\n        , round({{ cost_col }}, 2) as {{ cost_col }}_rounded\n        {% endfor %}\n    from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}\n","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":false,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final 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}}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_monthly_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.274b5b6532"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_class.d503677b5a"}],"documented":true},"encounter_count":{"description":"Number of encounters in the month","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_encounter_count__1.03132101e0"},{"kind":"not_null","kwargs":{"column_name":"encounter_count","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_encounter_count.5046b087e4"}],"documented":true},"month_actual":{"description":"Month number (1-12)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"month_actual","max_value":12,"min_value":1,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_month_actual__12__1.f650fcabb6"},{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_month_actual.82083ad465"}],"documented":true},"total_claim_cost":{"description":"Sum of total claim costs","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_claim_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_claim_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_claim_cost__0__total_claim_cost_is_not_null.09195ba1c6"}],"documented":true},"total_patient_responsibility":{"description":"Sum of patient responsibility amounts","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"total_patient_responsibility","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}","row_condition":"total_patient_responsibility is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_monthly_total_patient_responsibility__0__total_patient_responsibility_is_not_null.e824258649"}],"documented":true},"total_payer_coverage":{"description":"Sum of payer coverage 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}}","regex":"^\\d{4}-\\d{2}$"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_match_regex_fct_encounters_monthly_year_month___d_4_d_2_.6604a418e4"},{"kind":"not_null","kwargs":{"column_name":"year_month","model":"{{ get_where_subquery(ref('fct_encounters_monthly')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_monthly_year_month.b8b2866771"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_start_date_key"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_start_date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"encounter_class"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_class"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"total_claim_cost"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"total_claim_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"patient_responsibility"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_responsibility"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_encounters"}},"column":"duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"date_key"},"to_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_start_date_key"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_start_date_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"encounter_class"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"encounter_class"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"total_claim_cost"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"total_claim_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"payer_coverage"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"payer_coverage"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"patient_responsibility"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"patient_responsibility"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters"}},"column":"duration_minutes"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"duration_minutes"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"year_month"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"year_month"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"encounters_with_date"}},"column":"year_actual"},"to_col":{"sc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  , year_actual\n        , month_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_monthly\n    description: >\n      Monthly aggregated encounter statistics by encounter class.\n      Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 100\n          config:\n            severity: warn\n    columns:\n      - name: year_month\n        description: Year-month string (YYYY-MM format)\n        data_tests:\n          - not_null\n          # Pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{4}-\\\\d{2}$\"\n              config:\n                severity: warn\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_monthly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_month, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_yearly\n    description: >\n      Yearly aggregated encounter statistics by encounter class.\n      Provides counts, costs, patient metrics, and encounters per patient ratio.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 10\n          config:\n            severity: warn\n    columns:\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: encounters_per_patient\n        description: Average number of encounters per patient\n        data_tests:\n          # Ratio must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"encounters_per_patient is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"conditions","role":"import"},{"id":"encounters_with_year","role":"transform"},{"id":"conditions_per_patient_year","role":"transform"},{"id":"patient_year_agg","role":"transform"},{"id":"with_conditions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_patient_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_year","edge_type":"from"},{"from":"dim_date","to":"encounters_with_year","edge_type":"inner"},{"from":"procedures","to":"encounters_with_year","edge_type":"left"},{"from":"conditions","to":"conditions_per_patient_year","edge_type":"from"},{"from":"encounters_with_year","to":"patient_year_agg","edge_type":"from"},{"from":"patient_year_agg","to":"with_conditions","edge_type":"from"},{"from":"conditions_per_patient_year","to":"with_conditions","edge_type":"left"},{"from":"with_conditions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","conditions":"conditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n)","encounters_with_year":"encounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final 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dimensions for foreign key lookups.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from {{ ref('stg_synthea__conditions') }}\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_patient_summary\n    description: |\n      Annual patient-level aggregations for outcomes tracking and population health analysis.\n      One row per patient per calendar year with comprehensive utilization and cost metrics.\n      Grain: (patient_id, year_actual)\n\n      Use cases:\n      - Patient outcomes tracking over time\n      - Annual cost summaries and trends\n      - Cohort analysis and patient stratification\n      - Health trajectory visualization\n      - High-cost patient identification\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + year_actual\n      owner: healthcare_analytics\n      uses:\n        - \"Population health reporting\"\n        - \"Cost and utilization analysis\"\n        - \"Patient stratification\"\n        - \"Year-over-year trending\"\n      example_queries:\n        - name: \"Top 10 costliest patients by year\"\n          description: \"Identify high-cost patients for care management programs\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fps.year_actual\n                , fps.total_cost\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.payer_coverage_pct\n            from main_marts.fct_patient_summary fps\n            join main_marts.dim_patients p\n                on fps.patient_key = p.patient_key\n            where fps.year_actual = 2024\n            order by fps.total_cost desc\n            limit 10\n\n        - name: \"Patient count and cost by age group\"\n          description: \"Analyze patient population distribution and costs by age cohort\"\n          sql: |\n            select\n                case\n                    when fps.patient_age_at_year_end < 18 then '0-17'\n                    when fps.patient_age_at_year_end < 35 then '18-34'\n                    when fps.patient_age_at_year_end < 50 then '35-49'\n                    when fps.patient_age_at_year_end < 65 then '50-64'\n                    else '65+'\n                end as age_group\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_cost\n                , round(avg(fps.encounter_count), 2) as avg_encounters\n            from main_marts.fct_patient_summary fps\n            where fps.year_actual = 2024\n            group by 1\n            order by 1\n\n        - name: \"Cost and utilization trends over time\"\n          description: \"Year-over-year comparison of costs and utilization metrics\"\n          sql: |\n            select\n                fps.year_actual\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\n                , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_population_cost\n                , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_patient_summary fps\n            group by fps.year_actual\n            order by fps.year_actual desc\n\n        - name: \"High utilization patients\"\n          description: \"Find patients with above-average encounters and conditions\"\n          sql: |\n            with averages as (\n                select\n                    avg(encounter_count) as avg_encounters\n                    , avg(condition_count) as avg_conditions\n                from main_marts.fct_patient_summary\n                where year_actual = 2024\n            )\n            select\n                fps.patient_id\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.total_cost\n                , fps.unique_providers\n            from main_marts.fct_patient_summary fps\n            cross join averages a\n            where fps.year_actual = 2024\n                and fps.encounter_count > a.avg_encounters * 2\n                and fps.condition_count > a.avg_conditions\n            order by fps.total_cost desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - year_actual\n          config:\n            severity: error\n    columns:\n      - name: patient_summary_key\n        description: Surrogate key (MD5 hash of patient_id + year_actual)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: Calendar year of the summary\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: patient_age_at_year_end\n        description: Patient age as of December 31 of the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: condition_count\n        description: Number of distinct conditions diagnosed in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000000\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient (copay + coinsurance)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage_pct\n        description: Percentage of costs covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: unique_providers\n        description: Number of distinct providers seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 1000\n              config:\n                severity: error\n\n      - name: unique_organizations\n        description: Number of distinct organizations visited\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_patient_summary_patient_summary_key.e532e1ff45"]},"evidence":[{"label":"unique_key","value":"patient_summary_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"constraint.primary_key[(patient_id, year_actual)]","verdict":{"status":"covered","by":["test.healthcare_analytics.dbt_utils_unique_combination_of_columns_fct_patient_summary_patient_id__year_actual.cdb3418166"]},"evidence":[{"label":"constraint","value":"primary_key on (patient_id, year_actual)"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_metrics","path":"models/marts/analytics/fct_provider_metrics.sql","description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"provider_month_agg","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_provider_metrics.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"procedures","to":"encounters_with_date","edge_type":"left"},{"from":"encounters_with_date","to":"provider_month_agg","edge_type":"from"},{"from":"provider_month_agg","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , 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year-month","emphasis":null},{"kind":"context","text":"provider_month_agg as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- counts","emphasis":null},{"kind":"context","text":"        , count(*) as encounter_count","emphasis":null},{"kind":"context","text":"        , count(distinct e.patient_id) as unique_patient_count","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- patient metrics","emphasis":null},{"kind":"context","text":"        , avg(e.patient_age_at_encounter) as avg_patient_age","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- encounter metrics","emphasis":null},{"kind":"context","text":"        , avg(e.duration_minutes) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- procedure metrics","emphasis":null},{"kind":"removed","text":"        , sum(e.procedure_count) as procedure_total","emphasis":[36,51]},{"kind":"added","text":"        , sum(e.procedure_count) as total_procedures","emphasis":[36,52]},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost metrics","emphasis":null},{"kind":"context","text":"        , sum(e.total_claim_cost) as total_cost","emphasis":null},{"kind":"context","text":"        , sum(e.payer_coverage) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from encounters_with_date as e","emphasis":null},{"kind":"context","text":"    group by","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- time grain","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"        , year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: counts","emphasis":null},{"kind":"context","text":"        , encounter_count","emphasis":null},{"kind":"context","text":"        , unique_patient_count","emphasis":null},{"kind":"context","text":"        , total_procedures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: averages","emphasis":null},{"kind":"context","text":"        , round(avg_patient_age, 2) as avg_patient_age","emphasis":null},{"kind":"context","text":"        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: costs","emphasis":null},{"kind":"context","text":"        , coalesce(total_cost, 0) as total_cost","emphasis":null},{"kind":"context","text":"        , coalesce(total_payer_coverage, 0) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: derived ratios","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when encounter_count > 0","emphasis":null},{"kind":"context","text":"            then round(total_cost / encounter_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when unique_patient_count > 0","emphasis":null},{"kind":"context","text":"            then round(cast(encounter_count as decimal) / unique_patient_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from provider_month_agg","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null}]},"model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_provider_metrics\n    description: |\n      Monthly provider-level utilization and performance metrics.\n      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.\n      Grain: (provider_id, year_actual, month_actual)\n\n      Use cases:\n      - Provider performance tracking\n      - Workload distribution analysis\n      - Cost efficiency comparisons\n      - Capacity planning and staffing\n      - Quality improvement initiatives\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: provider_id + year_month_str\n      owner: healthcare_analytics\n      uses:\n        - \"Provider productivity reporting\"\n        - \"Workload balancing\"\n        - \"Cost efficiency analysis\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Top providers by patient volume\"\n          description: \"Identify highest-volume providers for capacity planning\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            order by total_encounters desc\n            limit 15\n\n        - name: \"Provider metrics trend by month\"\n          description: \"Track provider productivity over time\"\n          sql: |\n            select\n                fpm.year_month_str\n                , count(distinct fpm.provider_id) as active_providers\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\n            from main_marts.fct_provider_metrics fpm\n            where fpm.year_actual >= 2023\n            group by fpm.year_month_str\n            order by fpm.year_month_str desc\n\n        - name: \"Provider efficiency comparison\"\n          description: \"Compare providers by cost and volume efficiency\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , round(sum(fpm.total_cost), 2) as total_cost\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\n                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            having sum(fpm.encounter_count) >= 10\n            order by avg_cost_per_encounter\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - provider_id\n              - year_actual\n              - month_actual\n          config:\n            severity: error\n    columns:\n      - name: provider_metrics_key\n        description: Surrogate key (MD5 hash of provider_id + year_month_str)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: year_actual\n        description: Calendar year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Calendar month (1-12)\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 12\n              config:\n                severity: error\n\n      - name: year_month_str\n        description: Year-month string (YYYY-MM)\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: unique_patient_count\n        description: Number of distinct patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_patient_age\n        description: Average age of patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: avg_encounter_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_procedures\n        description: Total procedures performed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the month\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_cost_per_encounter\n        description: Average cost per encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounters_per_patient_avg\n        description: Average encounters per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded","diff":{"lines":[{"kind":"context","text":"  - name: fct_provider_metrics","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      Monthly provider-level utilization and performance metrics.","emphasis":null},{"kind":"removed","text":"      One row per provider per month.","emphasis":[31,36]},{"kind":"added","text":"      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.","emphasis":[31,97]},{"kind":"context","text":"      Grain: (provider_id, year_actual, month_actual)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      Use cases:","emphasis":null},{"kind":"context","text":"      - Provider performance tracking","emphasis":null},{"kind":"context","text":"      - Workload distribution analysis","emphasis":null},{"kind":"context","text":"      - Cost efficiency comparisons","emphasis":null},{"kind":"context","text":"      - Capacity planning and staffing","emphasis":null},{"kind":"context","text":"      - Quality improvement initiatives","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    config:","emphasis":null},{"kind":"context","text":"      tags: ['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    meta:","emphasis":null},{"kind":"context","text":"      grain: provider_id + year_month_str","emphasis":null},{"kind":"context","text":"      owner: healthcare_analytics","emphasis":null},{"kind":"context","text":"      uses:","emphasis":null},{"kind":"context","text":"        - \"Provider productivity reporting\"","emphasis":null},{"kind":"context","text":"        - \"Workload balancing\"","emphasis":null},{"kind":"context","text":"        - \"Cost efficiency analysis\"","emphasis":null},{"kind":"context","text":"        - \"Capacity planning\"","emphasis":null},{"kind":"context","text":"      example_queries:","emphasis":null},{"kind":"context","text":"        - name: \"Top providers by patient volume\"","emphasis":null},{"kind":"context","text":"          description: \"Identify highest-volume providers for capacity planning\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            order by total_encounters desc","emphasis":null},{"kind":"context","text":"            limit 15","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider metrics trend by month\"","emphasis":null},{"kind":"context","text":"          description: \"Track provider productivity over time\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                fpm.year_month_str","emphasis":null},{"kind":"context","text":"                , count(distinct fpm.provider_id) as active_providers","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            where fpm.year_actual >= 2023","emphasis":null},{"kind":"context","text":"            group by fpm.year_month_str","emphasis":null},{"kind":"context","text":"            order by fpm.year_month_str desc","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider efficiency comparison\"","emphasis":null},{"kind":"context","text":"          description: \"Compare providers by cost and volume efficiency\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_cost), 2) as total_cost","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            having sum(fpm.encounter_count) >= 10","emphasis":null},{"kind":"context","text":"            order by avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    data_tests:","emphasis":null},{"kind":"context","text":"      # Grain test: enforce unique combination","emphasis":null},{"kind":"context","text":"      - dbt_utils.unique_combination_of_columns:","emphasis":null},{"kind":"context","text":"          arguments:","emphasis":null},{"kind":"context","text":"            combination_of_columns:","emphasis":null},{"kind":"context","text":"              - provider_id","emphasis":null},{"kind":"context","text":"              - year_actual","emphasis":null},{"kind":"context","text":"              - month_actual","emphasis":null},{"kind":"context","text":"          config:","emphasis":null},{"kind":"context","text":"            severity: error","emphasis":null},{"kind":"context","text":"    columns:","emphasis":null},{"kind":"context","text":"      - name: provider_metrics_key","emphasis":null},{"kind":"context","text":"        description: Surrogate key (MD5 hash of provider_id + year_month_str)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - unique","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_providers","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - relationships:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                to: ref('dim_providers')","emphasis":null},{"kind":"context","text":"                field: provider_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_id","emphasis":null},{"kind":"context","text":"        description: Natural provider identifier","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_organizations","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_id","emphasis":null},{"kind":"context","text":"        description: Natural organization identifier","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_actual","emphasis":null},{"kind":"context","text":"        description: Calendar year","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1900","emphasis":null},{"kind":"context","text":"                max_value: 2100","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: month_actual","emphasis":null},{"kind":"context","text":"        description: Calendar month (1-12)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1","emphasis":null},{"kind":"context","text":"                max_value: 12","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_month_str","emphasis":null},{"kind":"context","text":"        description: Year-month string (YYYY-MM)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounter_count","emphasis":null},{"kind":"context","text":"        description: Number of encounters in the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: unique_patient_count","emphasis":null},{"kind":"context","text":"        description: Number of distinct patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_patient_age","emphasis":null},{"kind":"context","text":"        description: Average age of patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"                max_value: 150","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"        description: Average encounter duration in minutes","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_procedures","emphasis":null},{"kind":"context","text":"        description: Total procedures performed","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_cost","emphasis":null},{"kind":"context","text":"        description: Total claim cost for the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_payer_coverage","emphasis":null},{"kind":"context","text":"        description: Amount covered by insurance","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"        description: Average cost per encounter","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"        description: Average encounters per patient","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: _loaded_at","emphasis":null},{"kind":"context","text":"        description: Timestamp when record was loaded","emphasis":null}]}},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_metrics","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602"]},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602","causes":["where-filtered — only rows matching `year_actual >= 2024` are checked; the grain is unverified outside the filter","limit: 100 — failing-row reporting caps at 100 rows; the test still trips on the first failure, but the failure surface is truncated"]}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_scorecard","path":"models/marts/analytics/fct_provider_scorecard.sql","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id"},"code_map":{"compiled":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":7,"col":21,"byte":250}}},"column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":4,"col":7,"byte":106},"end":{"line":4,"col":46,"byte":145}},"(final select)\u001fencounter_count":{"start":{"line":5,"col":7,"byte":152},"end":{"line":5,"col":34,"byte":179}},"(final select)\u001fprovider_id":{"start":{"line":3,"col":5,"byte":88},"end":{"line":3,"col":16,"byte":99}}},"raw_column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":6,"col":29,"byte":223},"end":{"line":6,"col":46,"byte":240}},"(final select)\u001fencounter_count":{"start":{"line":7,"col":19,"byte":259},"end":{"line":7,"col":34,"byte":274}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_provider_metrics"}},"column":"provider_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"provider_id"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'scorecard']\n    )\n}}\n\n-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom {{ ref('fct_provider_metrics') }}\ngroup by provider_id","sql_diff":{"lines":[{"kind":"added","text":"{{","emphasis":null},{"kind":"added","text":"    config(","emphasis":null},{"kind":"added","text":"        materialized='table',","emphasis":null},{"kind":"added","text":"        tags=['analytics', 'scorecard']","emphasis":null},{"kind":"added","text":"    )","emphasis":null},{"kind":"added","text":"}}","emphasis":null},{"kind":"added","text":"","emphasis":null},{"kind":"added","text":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).","emphasis":null},{"kind":"added","text":"select","emphasis":null},{"kind":"added","text":"    provider_id","emphasis":null},{"kind":"added","text":"    , avg(quality_score) as avg_quality_score","emphasis":null},{"kind":"added","text":"    , count(*) as encounter_count","emphasis":null},{"kind":"added","text":"from {{ ref('fct_provider_metrics') }}","emphasis":null},{"kind":"added","text":"group by provider_id","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_scorecard","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns."}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":false,"unit_test":false},"state":"new"},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}},"raw_node_spans":{"grid":{"start":{"line":19,"col":1,"byte":781},"end":{"line":28,"col":2,"byte":1036}},"months":{"start":{"line":5,"col":6,"byte":536},"end":{"line":11,"col":2,"byte":664}},"states":{"start":{"line":13,"col":1,"byte":667},"end":{"line":17,"col":2,"byte":778}}},"raw_column_spans":{"grid\u001fmonth_key":{"start":{"line":21,"col":76,"byte":877},"end":{"line":21,"col":85,"byte":886}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","data_diff":{"given":[{"ordinal":1,"input":"ref('stg_synthea__patients')","diff":{"columns":[{"name":"patient_id","status":"present"},{"name":"state","status":"present"}],"rows":[{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"new":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"new":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"changed":false},{"old":{"display":"Connecticutt","key":{"t":"str","v":"Connecticutt"}},"new":{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"new":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"changed":false},{"old":{"display":"Vermont","key":{"t":"str","v":"Vermont"}},"new":{"display":"","key":{"t":"null"}},"changed":true}]},{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"new":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]}]}}],"expect":null},"given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not 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medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , 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failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final 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encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","yaml_diff":{"lines":[{"kind":"context","text":"  # LEADING bracket for test 2 — sits between the blank-line","emphasis":null},{"kind":"context","text":"  # terminator above and `- name:` below with no further gap, so the","emphasis":null},{"kind":"context","text":"  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).","emphasis":null},{"kind":"context","text":"  - name: test_mart_dq_summary_zero_quarantined_when_all_valid","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      mart_dq_summary should report zero quarantined_count and 0.00","emphasis":null},{"kind":"context","text":"      quarantine_rate_pct when every upstream row passes data-quality","emphasis":null},{"kind":"context","text":"      validations. Uses sql format for the GIVEN mocks and dict format","emphasis":null},{"kind":"context","text":"      for the EXPECT — dict expects only compare listed columns, so","emphasis":null},{"kind":"context","text":"      non-deterministic outputs (`_generated_at` from","emphasis":null},{"kind":"context","text":"      `current_timestamp`) are excluded from comparison.","emphasis":null},{"kind":"context","text":"    model: mart_dq_summary","emphasis":null},{"kind":"context","text":"    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /","emphasis":null},{"kind":"context","text":"    # #73). Demonstrates that the slicer ride-along applies to every","emphasis":null},{"kind":"context","text":"    # unit_test, not just the first in the file.","emphasis":null},{"kind":"context","text":"    given:","emphasis":null},{"kind":"context","text":"      - input: ref('stg_synthea__encounters')","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_encounter_timestamps","emphasis":null},{"kind":"context","text":"            , true as no_future_encounter_dates","emphasis":null},{"kind":"context","text":"            , true as end_after_1900","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"      - input: ref(\"stg_synthea__medications\")","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_medication_dates","emphasis":null},{"kind":"context","text":"            , true as no_future_medication_dates","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"            , true as end_after_1900_if_present","emphasis":null},{"kind":"context","text":"    expect:","emphasis":null},{"kind":"context","text":"      format: dict","emphasis":null},{"kind":"context","text":"      rows:","emphasis":null},{"kind":"context","text":"        - entity_type: 'encounters'","emphasis":null},{"kind":"removed","text":"          quarantined_count: null","emphasis":[29,33]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"        - entity_type: 'medications'","emphasis":null},{"kind":"removed","text":"          quarantined_count: 1","emphasis":[29,30]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"    overrides:","emphasis":null},{"kind":"context","text":"      macros:","emphasis":null},{"kind":"context","text":"        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"","emphasis":null},{"kind":"context","text":"      vars:","emphasis":null},{"kind":"removed","text":"        dq_quarantine_threshold: 10","emphasis":[33,35]},{"kind":"added","text":"        dq_quarantine_threshold: 5","emphasis":[33,34]},{"kind":"context","text":"      env_vars:","emphasis":null},{"kind":"context","text":"        DBT_DQ_REGION: 'us-east-1'","emphasis":null},{"kind":"context","text":"  # TRAILING bracket for test 2 — terminated by the blank line","emphasis":null},{"kind":"context","text":"  # separating it from test 3 (cute-dbt#69 / #73).","emphasis":null}]},"data_diff":{"given":[],"expect":{"columns":[{"name":"entity_type","status":"present"},{"name":"quarantine_rate_pct","status":"present"},{"name":"quarantined_count","status":"present"},{"name":"total_count","status":"present"}],"rows":[{"kind":"modified","cells":[{"old":{"display":"encounters","key":{"t":"str","v":"encounters"}},"new":{"display":"encounters","key":{"t":"str","v":"encounters"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"","key":{"t":"null"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"medications","key":{"t":"str","v":"medications"}},"new":{"display":"medications","key":{"t":"str","v":"medications"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"1","key":{"t":"number","v":"1"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]}]}},"overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"dq_quarantine_threshold","tier":"direct"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_encounter_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , 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persisted) - used as a building block\n    Use cases: Ad-hoc queries, building blocks for other models, BI tool queries\n\n    Joins fct_encounters with all dimension tables to provide a denormalized\n    view with human-readable names instead of just keys.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from {{ ref('fct_encounters') }}\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_encounter_summary\n    description: |\n      Enriched encounter view with dimension attributes for ad-hoc analysis.\n      Ephemeral (not persisted) - used as a building block for other queries.\n      Joins encounters with patient, provider, organization, and payer details.\n\n      Use cases:\n      - Ad-hoc encounter analysis\n      - Building block for custom reports\n      - Quick encounter lookups with full context\n\n    config:\n      materialized: ephemeral\n      tags: ['analytics', 'marts', 'view']\n    columns:\n      - name: encounter_id\n        description: Unique encounter identifier\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: patient_id\n        description: Patient identifier\n\n      - name: patient_name\n        description: Full patient name\n\n      - name: provider_id\n        description: Provider identifier\n\n      - name: provider_name\n        description: Full provider name\n\n      - name: organization_id\n        description: Organization identifier\n\n      - name: organization_name\n        description: Organization name\n\n      - name: payer_id\n        description: Payer identifier\n\n      - name: payer_name\n        description: Payer name\n\n      - name: encounter_class\n        description: Type of encounter\n\n      - name: total_cost\n        description: Total claim cost\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n\n      - name: patient_responsibility\n        description: Amount owed by patient"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","dag":{"nodes":[{"id":"condition_cohorts","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_patient_current_conditions.sql","role":"final"}],"edges":[{"from":"condition_cohorts","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_cohorts":"condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n)","final":"final as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)"},"code_map":{"compiled":"\n\n\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":33,"col":1,"byte":623},"end":{"line":33,"col":20,"byte":642}},"condition_cohorts":{"start":{"line":5,"col":6,"byte":9},"end":{"line":18,"col":2,"byte":357}},"final":{"start":{"line":20,"col":1,"byte":360},"end":{"line":31,"col":2,"byte":621}}},"column_spans":{"(final select)\u001f*":{"start":{"line":33,"col":8,"byte":630},"end":{"line":33,"col":9,"byte":631}},"condition_cohorts\u001fcondition_code":{"start":{"line":10,"col":11,"byte":118},"end":{"line":10,"col":25,"byte":132}},"condition_cohorts\u001fcondition_description":{"start":{"line":11,"col":11,"byte":143},"end":{"line":11,"col":32,"byte":164}},"condition_cohorts\u001fcondition_key":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":24,"byte":107}},"condition_cohorts\u001fencounter_count":{"start":{"line":14,"col":11,"byte":238},"end":{"line":14,"col":26,"byte":253}},"condition_cohorts\u001ffirst_diagnosis_date":{"start":{"line":12,"col":11,"byte":175},"end":{"line":12,"col":31,"byte":195}},"condition_cohorts\u001fis_active":{"start":{"line":15,"col":11,"byte":264},"end":{"line":15,"col":20,"byte":273}},"condition_cohorts\u001fmonths_with_condition":{"start":{"line":13,"col":11,"byte":206},"end":{"line":13,"col":32,"byte":227}},"condition_cohorts\u001fpatient_id":{"start":{"line":8,"col":11,"byte":73},"end":{"line":8,"col":21,"byte":83}},"condition_cohorts\u001fpatient_key":{"start":{"line":7,"col":9,"byte":51},"end":{"line":7,"col":20,"byte":62}},"final\u001fcondition_code":{"start":{"line":25,"col":11,"byte":457},"end":{"line":25,"col":25,"byte":471}},"final\u001fcondition_description":{"start":{"line":26,"col":11,"byte":482},"end":{"line":26,"col":32,"byte":503}},"final\u001fcondition_key":{"start":{"line":24,"col":11,"byte":433},"end":{"line":24,"col":24,"byte":446}},"final\u001fencounter_count":{"start":{"line":29,"col":11,"byte":577},"end":{"line":29,"col":26,"byte":592}},"final\u001ffirst_diagnosis_date":{"start":{"line":27,"col":11,"byte":514},"end":{"line":27,"col":31,"byte":534}},"final\u001fmonths_with_condition":{"start":{"line":28,"col":11,"byte":545},"end":{"line":28,"col":32,"byte":566}},"final\u001fpatient_id":{"start":{"line":23,"col":11,"byte":412},"end":{"line":23,"col":21,"byte":422}},"final\u001fpatient_key":{"start":{"line":22,"col":9,"byte":390},"end":{"line":22,"col":20,"byte":401}}},"raw_node_spans":{"condition_cohorts":{"start":{"line":5,"col":6,"byte":441},"end":{"line":18,"col":2,"byte":778}},"final":{"start":{"line":20,"col":1,"byte":781},"end":{"line":31,"col":2,"byte":1042}}},"raw_column_spans":{"(final select)\u001f*":{"start":{"line":33,"col":8,"byte":1051},"end":{"line":33,"col":9,"byte":1052}}}},"column_lineage":{"context":{"condition_code":{"description":"ICD/SNOMED condition code","documented":true},"condition_description":{"description":"Full condition name","documented":true},"condition_key":{"description":"Foreign key to dim_conditions","tests":[{"kind":"relationships","kwargs":{"column_name":"condition_key","field":"condition_key","model":"{{ get_where_subquery(ref('v_patient_current_conditions')) }}","to":"ref('dim_conditions')"},"node_id":"test.healthcare_analytics.relationships_v_patient_current_conditions_condition_key__condition_key__ref_dim_conditions_.44a146ce81"}],"documented":true},"encounter_count":{"description":"Number of encounters with this condition","documented":true},"first_diagnosis_date":{"description":"Date of first diagnosis","documented":true},"months_with_condition":{"description":"Duration of condition in months","documented":true},"patient_id":{"description":"Natural patient identifier","documented":true},"patient_key":{"description":"Foreign key to dim_patients","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"is_active"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"is_active"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Patient Current Conditions View\n\n    Purpose: Current (active) conditions for each patient\n    Grain: One row per patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_patient_current_conditions\n    description: |\n      Current (active) conditions for each patient, filtered from fct_condition_cohorts.\n      Use for patient panels, disease management dashboards, and care gap identification.\n      Grain: (patient_id, condition_code) - only active conditions\n\n      Use cases:\n      - Patient panel management\n      - Active disease registries\n      - Care gap identification\n      - Real-time patient condition views\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Patient panel displays\"\n        - \"Active condition registries\"\n        - \"Care gap dashboards\"\n      example_queries:\n        - name: \"Patient active conditions lookup\"\n          description: \"Get all active conditions for a specific patient\"\n          sql: |\n            select\n                vpcc.patient_id\n                , vpcc.condition_code\n                , vpcc.condition_description\n                , vpcc.first_diagnosis_date\n                , vpcc.months_with_condition\n                , vpcc.encounter_count\n            from main_marts.v_patient_current_conditions vpcc\n            where vpcc.patient_id = 'patient-uuid-here'\n            order by vpcc.first_diagnosis_date\n\n        - name: \"Active condition summary\"\n          description: \"Count of patients by active condition for disease registries\"\n          sql: |\n            select\n                vpcc.condition_code\n                , vpcc.condition_description\n                , count(distinct vpcc.patient_id) as active_patient_count\n                , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\n                , round(avg(vpcc.encounter_count), 1) as avg_encounters\n            from main_marts.v_patient_current_conditions vpcc\n            group by 1, 2\n            order by active_patient_count desc\n            limit 20\n\n    columns:\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n\n      - name: months_with_condition\n        description: Duration of condition in months\n\n      - name: encounter_count\n        description: Number of encounters with this condition"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_provider_active_patients.sql","role":"final"}],"edges":[{"from":"patient_recent_encounters","to":"patient_provider_assignment","edge_type":"from"},{"from":"current_conditions","to":"active_conditions_by_provider","edge_type":"from"},{"from":"patient_provider_assignment","to":"active_conditions_by_provider","edge_type":"inner"},{"from":"active_conditions_by_provider","to":"provider_summary","edge_type":"from"},{"from":"providers","to":"final","edge_type":"from"},{"from":"provider_summary","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","active_conditions_by_provider":"active_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":96,"col":1,"byte":2429},"end":{"line":96,"col":20,"byte":2448}},"active_conditions_by_provider":{"start":{"line":52,"col":1,"byte":1176},"end":{"line":61,"col":2,"byte":1451}},"current_conditions":{"start":{"line":17,"col":1,"byte":294},"end":{"line":25,"col":2,"byte":523}},"final":{"start":{"line":74,"col":1,"byte":1744},"end":{"line":94,"col":2,"byte":2427}},"patient_provider_assignment":{"start":{"line":42,"col":1,"byte":962},"end":{"line":49,"col":2,"byte":1123}},"patient_recent_encounters":{"start":{"line":28,"col":1,"byte":599},"end":{"line":39,"col":2,"byte":910}},"provider_summary":{"start":{"line":64,"col":1,"byte":1479},"end":{"line":72,"col":2,"byte":1741}},"providers":{"start":{"line":5,"col":6,"byte":9},"end":{"line":14,"col":2,"byte":232}}},"column_spans":{"(final 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_provider_active_patients\n    description: |\n      Active patient count per provider for panel management and workload analysis.\n      Aggregates patients with active conditions by their treating provider.\n      Grain: provider_id\n\n      Use cases:\n      - Provider panel size management\n      - Workload distribution analysis\n      - Capacity planning\n      - Provider assignment optimization\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: provider_id\n      owner: healthcare_analytics\n      uses:\n        - \"Provider panel management\"\n        - \"Workload balancing\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Provider panel sizes\"\n          description: \"View provider workload by active patient and condition counts\"\n          sql: |\n            select\n                vpap.provider_id\n                , vpap.provider_name\n                , vpap.active_patient_count\n                , vpap.total_active_conditions\n                , vpap.avg_conditions_per_patient\n            from main_marts.v_provider_active_patients vpap\n            where vpap.active_patient_count > 0\n            order by vpap.active_patient_count desc\n            limit 25\n\n        - name: \"Provider workload distribution\"\n          description: \"Analyze distribution of patient panels across providers\"\n          sql: |\n            select\n                case\n                    when vpap.active_patient_count = 0 then '0'\n                    when vpap.active_patient_count \u003c= 10 then '1-10'\n                    when vpap.active_patient_count \u003c= 50 then '11-50'\n                    when vpap.active_patient_count \u003c= 100 then '51-100'\n                    else '100+'\n                end as panel_size_bucket\n                , count(*) as provider_count\n                , sum(vpap.active_patient_count) as total_patients\n            from main_marts.v_provider_active_patients vpap\n            group by 1\n            order by 1\n\n    columns:\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: active_patient_count\n        description: Number of patients with active conditions\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_active_conditions\n        description: Total number of active conditions managed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_conditions_per_patient\n        description: Average number of active conditions per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"enforcement.constraint-unbacked":{"name":"Declared constraint without a backing test","group":"enforcement","tier":"total","instrument":"data-test","conditions":["the model declares a primary_key or unique constraint (model-level constraints[] or a column-level constraint) whose enforcement on the manifest's adapter is metadata-only (NotEnforced) — the warehouse accepts the declaration but does not enforce uniqueness at write time","no enabled generic uniqueness data test (unique, attached to the model) backs the constrained column — the constraint→test edge is INFERRED by column + test-name match, since the manifest never links a constraint to its test","the verdict is UNCOVERED only when the constraint is declared, metadata-only, AND has no inferred backing test; a backing test (even one cute-dbt could miss) keeps it silent"],"exclusions":["a constraint the adapter ENFORCES at write time (e.g. not_null / foreign_key on Postgres/DuckDB) is never a gap — the warehouse guarantees it","a constraint kind with no column-level generic-test backing (check / custom / foreign_key) is out of this inference and never reported here","the inferred edge can MISS a renamed test or a singular/custom test asserting the same uniqueness — the copy says \"backing test\" (an authoring-discipline cue), never that the warehouse lacks an index; columns are authored-YAML-only, so this is never a warehouse-truth claim","the whole `enforcement` group is gated behind the governance experiment — off by default, it never fires on a non-governance report"],"rationale":"A primary-key / unique constraint that the warehouse treats as metadata-only is a DECLARED guarantee with nothing checking it: duplicate rows load silently, and any downstream join or incremental merge that trusts the declared grain corrupts. A backing data test is the only thing that actually verifies the declared uniqueness on this adapter.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/enforcement.constraint-unbacked.html"},"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}},"project_definition":{"name":"healthcare_analytics","version":"1.0.0","require_dbt_version":">=1.8.0","vars":{"default_state_grid_density":"monthly","dq_quarantine_threshold":5},"vars_spans":{"default_state_grid_density":{"line":12,"column":3},"dq_quarantine_threshold":{"line":11,"column":3}},"config_trees":{"models":{"children":{"healthcare_analytics":{"configs":{"materialized":"view"},"children":{"marts":{"configs":{"materialized":"table"}},"staging":{"configs":{"materialized":"view"}}}}}}},"dispatch":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}],"on_run_start":["grant select on all tables in schema analytics to role reporting"],"paths":{"clean-targets":["target","dbt_packages"],"model-paths":["models"],"test-paths":["tests"]},"other":{"profile":"healthcare_analytics"}},"project_change_panel":{"kind":"categorized","changes":[{"category":"vars","label":"default_state_grid_density","old":"weekly","new":"monthly","vars":{"entries":[{"name":"default_state_grid_density","old":"weekly","new":"monthly","config":["model.healthcare_analytics.mart_date_state_grid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"vars","label":"dq_quarantine_threshold","old":10,"new":5,"vars":{"entries":[{"name":"dq_quarantine_threshold","old":10,"new":5,"direct":["model.healthcare_analytics.mart_dq_summary"],"via_macros":[{"model":"model.healthcare_analytics.stg_synthea__encounters","via":"macro.healthcare_analytics.add_dq_flags"},{"model":"model.healthcare_analytics.stg_synthea__medications","via":"macro.healthcare_analytics.add_dq_flags"}],"insulated_tests":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"config_tree","label":"models.healthcare_analytics.marts: +materialized","old":"view","new":"table","tree":{"section":"models","segments":["healthcare_analytics","marts"],"key":"materialized"}},{"category":"dispatch","label":"dispatch","old":[{"macro_namespace":"dbt_utils","search_order":["dbt_utils","healthcare_analytics"]}],"new":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}]},{"category":"hooks","label":"on-run-start","old":["grant usage on schema analytics to role reporting"],"new":["grant select on all tables in schema analytics to role reporting"],"hook":{"sql_diff":{"lines":[{"kind":"removed","text":"grant usage on schema analytics to role reporting","emphasis":[6,13]},{"kind":"added","text":"grant select on all tables in schema analytics to role reporting","emphasis":[6,28]}]},"operation_ids":["operation.healthcare_analytics.healthcare_analytics-on-run-start-0"],"manifest":"matched"}}]},"governance":{"group_chips":[{"group":"clinical_quality","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com"}],"blast_radius":[{"exposure_label":"provider_quality_dashboard","exposure_type":"dashboard","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com","in_scope_model_count":7}],"lifecycle_chips":[{"kind":"group-owner-touch","label":"Touches group clinical_quality (owner: clinical-quality@example.com)"}],"meta_tags":[{"model":"dim_conditions","tags":["analytics","dimension","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify the most prevalent conditions in the patient population\",\"name\":\"Top 10 most common conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , patient_count\\n    , encounter_count\\nfrom main_marts.dim_conditions\\norder by patient_count desc\\nlimit 10\"},{\"description\":\"Find conditions that drive the most healthcare utilization\",\"name\":\"Conditions by encounter frequency\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , encounter_count\\n    , patient_count\\n    , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\\nfrom main_marts.dim_conditions\\nwhere patient_count > 0\\norder by encounter_count desc\\nlimit 20\"},{\"description\":\"Find conditions first diagnosed in the last year\",\"name\":\"Recently diagnosed conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , first_occurrence_date\\n    , last_occurrence_date\\n    , patient_count\\nfrom main_marts.dim_conditions\\nwhere first_occurrence_date >= current_date - interval '1 year'\\norder by first_occurrence_date desc\"}]"},{"key":"grain","value":"condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Condition lookup and filtering\",\"Cohort analysis by diagnosis\",\"Disease prevalence reporting\"]"}],"columns":[]},{"model":"dim_payers","tags":[],"meta":[],"columns":[{"column":"payer_key","tags":["dimension_key"],"meta":[{"key":"owner","value":"clinical-quality"},{"key":"pii","value":"false"}]}]},{"model":"fct_condition_cohorts","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Find conditions with the most patients for population health programs\",\"name\":\"Largest condition cohorts\",\"sql\":\"select\\n    fcc.condition_code\\n    , fcc.condition_description\\n    , count(distinct fcc.patient_id) as patient_count\\n    , sum(fcc.encounter_count) as total_encounters\\n    , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\\n    , sum(case when fcc.is_active then 1 else 0 end) as active_patients\\nfrom main_marts.fct_condition_cohorts fcc\\ngroup by 1, 2\\norder by patient_count desc\\nlimit 15\"},{\"description\":\"Identify patients with long-standing conditions for care management\",\"name\":\"Chronic condition patients (long duration)\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fcc.condition_description\\n    , fcc.first_diagnosis_date\\n    , fcc.months_with_condition\\n    , fcc.encounter_count\\n    , fcc.is_active\\nfrom main_marts.fct_condition_cohorts fcc\\njoin main_marts.dim_patients p\\n    on fcc.patient_key = p.patient_key\\nwhere fcc.months_with_condition >= 24\\n    and fcc.is_active = true\\norder by fcc.months_with_condition desc\\nlimit 50\"},{\"description\":\"Find patients with comorbidities for complex care programs\",\"name\":\"Patients with multiple conditions\",\"sql\":\"with patient_condition_counts as (\\n    select\\n        patient_id\\n        , patient_key\\n        , count(distinct condition_code) as condition_count\\n        , sum(case when is_active then 1 else 0 end) as active_condition_count\\n    from main_marts.fct_condition_cohorts\\n    group by 1, 2\\n)\\nselect\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , pcc.condition_count\\n    , pcc.active_condition_count\\nfrom patient_condition_counts pcc\\njoin main_marts.dim_patients p\\n    on pcc.patient_key = p.patient_key\\nwhere pcc.condition_count >= 3\\norder by pcc.condition_count desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Disease management programs\",\"Cohort identification\",\"Chronic condition tracking\",\"Care gap analysis\"]"}],"columns":[]},{"model":"fct_cost_analysis","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Analyze cost distribution across different insurance payers\",\"name\":\"Cost breakdown by payer\",\"sql\":\"select\\n    py.payer_name\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\ngroup by py.payer_name\\norder by total_cost desc\"},{\"description\":\"Track cost trends over time for financial forecasting\",\"name\":\"Monthly cost trends\",\"sql\":\"select\\n    strftime(fca.encounter_date, '%Y-%m') as year_month\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\nwhere fca.year_actual >= 2023\\ngroup by 1\\norder by 1 desc\"},{\"description\":\"Compare costs across different encounter classes\",\"name\":\"Cost by encounter type\",\"sql\":\"select\\n    fca.encounter_class\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost\\n    , round(avg(fca.procedure_count), 2) as avg_procedures\\n    , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\ngroup by fca.encounter_class\\norder by total_cost desc\"},{\"description\":\"Identify individual high-cost encounters for review\",\"name\":\"High-cost encounters\",\"sql\":\"select\\n    fca.encounter_id\\n    , fca.encounter_date\\n    , fca.encounter_class\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , py.payer_name\\n    , fca.total_cost\\n    , fca.payer_coverage\\n    , fca.patient_responsibility\\n    , fca.procedure_count\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_patients p\\n    on fca.patient_key = p.patient_key\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\norder by fca.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"encounter_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Financial reporting\",\"Payer mix analysis\",\"Cost allocation tracking\",\"Revenue cycle management\"]"}],"columns":[]},{"model":"fct_patient_summary","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify high-cost patients for care management programs\",\"name\":\"Top 10 costliest patients by year\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fps.year_actual\\n    , fps.total_cost\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.payer_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\njoin main_marts.dim_patients p\\n    on fps.patient_key = p.patient_key\\nwhere fps.year_actual = 2024\\norder by fps.total_cost desc\\nlimit 10\"},{\"description\":\"Analyze patient population distribution and costs by age cohort\",\"name\":\"Patient count and cost by age group\",\"sql\":\"select\\n    case\\n        when fps.patient_age_at_year_end < 18 then '0-17'\\n        when fps.patient_age_at_year_end < 35 then '18-34'\\n        when fps.patient_age_at_year_end < 50 then '35-49'\\n        when fps.patient_age_at_year_end < 65 then '50-64'\\n        else '65+'\\n    end as age_group\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_cost\\n    , round(avg(fps.encounter_count), 2) as avg_encounters\\nfrom main_marts.fct_patient_summary fps\\nwhere fps.year_actual = 2024\\ngroup by 1\\norder by 1\"},{\"description\":\"Year-over-year comparison of costs and utilization metrics\",\"name\":\"Cost and utilization trends over time\",\"sql\":\"select\\n    fps.year_actual\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\\n    , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_population_cost\\n    , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\ngroup by fps.year_actual\\norder by fps.year_actual desc\"},{\"description\":\"Find patients with above-average encounters and conditions\",\"name\":\"High utilization patients\",\"sql\":\"with averages as (\\n    select\\n        avg(encounter_count) as avg_encounters\\n        , avg(condition_count) as avg_conditions\\n    from main_marts.fct_patient_summary\\n    where year_actual = 2024\\n)\\nselect\\n    fps.patient_id\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.total_cost\\n    , fps.unique_providers\\nfrom main_marts.fct_patient_summary fps\\ncross join averages a\\nwhere fps.year_actual = 2024\\n    and fps.encounter_count > a.avg_encounters * 2\\n    and fps.condition_count > a.avg_conditions\\norder by fps.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + year_actual"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Population health reporting\",\"Cost and utilization analysis\",\"Patient stratification\",\"Year-over-year trending\"]"}],"columns":[]},{"model":"fct_provider_metrics","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"fct_provider_scorecard","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"mart_date_state_grid","tags":["analytics","marts","reporting"],"meta":[{"key":"grain","value":"month_key x state"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Dense reporting grids\",\"Zero-activity gap detection\"]"}],"columns":[]},{"model":"mart_dq_summary","tags":["analytics","data_quality","marts"],"meta":[{"key":"grain","value":"entity_type"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Data quality dashboards\",\"Quarantine monitoring\",\"Data quality alerting\"]"}],"columns":[]},{"model":"v_encounter_summary","tags":["analytics","marts","view"],"meta":[],"columns":[]},{"model":"v_patient_current_conditions","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Get all active conditions for a specific patient\",\"name\":\"Patient active conditions lookup\",\"sql\":\"select\\n    vpcc.patient_id\\n    , vpcc.condition_code\\n    , vpcc.condition_description\\n    , vpcc.first_diagnosis_date\\n    , vpcc.months_with_condition\\n    , vpcc.encounter_count\\nfrom main_marts.v_patient_current_conditions vpcc\\nwhere vpcc.patient_id = 'patient-uuid-here'\\norder by vpcc.first_diagnosis_date\"},{\"description\":\"Count of patients by active condition for disease registries\",\"name\":\"Active condition summary\",\"sql\":\"select\\n    vpcc.condition_code\\n    , vpcc.condition_description\\n    , count(distinct vpcc.patient_id) as active_patient_count\\n    , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\\n    , round(avg(vpcc.encounter_count), 1) as avg_encounters\\nfrom main_marts.v_patient_current_conditions vpcc\\ngroup by 1, 2\\norder by active_patient_count desc\\nlimit 20\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Patient panel displays\",\"Active condition registries\",\"Care gap dashboards\"]"}],"columns":[]},{"model":"v_provider_active_patients","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"View provider workload by active patient and condition counts\",\"name\":\"Provider panel sizes\",\"sql\":\"select\\n    vpap.provider_id\\n    , vpap.provider_name\\n    , vpap.active_patient_count\\n    , vpap.total_active_conditions\\n    , vpap.avg_conditions_per_patient\\nfrom main_marts.v_provider_active_patients vpap\\nwhere vpap.active_patient_count > 0\\norder by vpap.active_patient_count desc\\nlimit 25\"},{\"description\":\"Analyze distribution of patient panels across providers\",\"name\":\"Provider workload distribution\",\"sql\":\"select\\n    case\\n        when vpap.active_patient_count = 0 then '0'\\n        when vpap.active_patient_count \u003c= 10 then '1-10'\\n        when vpap.active_patient_count \u003c= 50 then '11-50'\\n        when vpap.active_patient_count \u003c= 100 then '51-100'\\n        else '100+'\\n    end as panel_size_bucket\\n    , count(*) as provider_count\\n    , sum(vpap.active_patient_count) as total_patients\\nfrom main_marts.v_provider_active_patients vpap\\ngroup by 1\\norder by 1\"}]"},{"key":"grain","value":"provider_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider panel management\",\"Workload balancing\",\"Capacity planning\"]"}],"columns":[]}]},"macro_lens":{"macros":[{"name":"quarantine_filter","package":"healthcare_analytics","path":"macros/data_quality/quarantine_filter.sql","diff":{"lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"removed","text":"    where {{ field_name }} is true","emphasis":[27,29]},{"kind":"added","text":"    where {{ field_name }} = true","emphasis":[27,28]},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}]},"body_lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"context","text":"    where {{ field_name }} = true","emphasis":null},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}],"impacted_count":3,"tree":[{"kind":"dir","label":"models","depth":0,"model_id":""},{"kind":"dir","label":"intermediate","depth":1,"model_id":""},{"kind":"dir","label":"healthcare","depth":2,"model_id":""},{"kind":"model","label":"int_encounters__enriched","depth":3,"model_id":"model.healthcare_analytics.int_encounters__enriched"},{"kind":"dir","label":"marts","depth":1,"model_id":""},{"kind":"dir","label":"core","depth":2,"model_id":""},{"kind":"model","label":"fct_clinical_events","depth":3,"model_id":"model.healthcare_analytics.fct_clinical_events"},{"kind":"model","label":"fct_encounters","depth":3,"model_id":"model.healthcare_analytics.fct_encounters"}],"impacted_models":[{"model_id":"model.healthcare_analytics.fct_clinical_events","name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='clinical_event_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with valid_encounters as (","emphasis":null},{"kind":"context","text":"    select encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"conditions as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        condition_id as event_id","emphasis":null},{"kind":"context","text":"        , 'CONDITION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , cond.encounter_id","emphasis":null},{"kind":"context","text":"        , condition_start_date as event_start_date","emphasis":null},{"kind":"context","text":"        , condition_end_date as event_end_date","emphasis":null},{"kind":"context","text":"        , condition_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , condition_description as description","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_code","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_description","emphasis":null},{"kind":"context","text":"        , cast(null as decimal(18, 2)) as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__conditions') }} as cond","emphasis":null},{"kind":"context","text":"    where cond.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"medications as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        medication_id as event_id","emphasis":null},{"kind":"context","text":"        , 'MEDICATION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , med.encounter_id","emphasis":null},{"kind":"context","text":"        , cast(medication_start_at as date) as event_start_date","emphasis":null},{"kind":"context","text":"        , cast(medication_end_at as date) as event_end_date","emphasis":null},{"kind":"context","text":"        , medication_code as code","emphasis":null},{"kind":"context","text":"        , 'RXNORM' as code_system","emphasis":null},{"kind":"context","text":"        , medication_description as description","emphasis":null},{"kind":"context","text":"        , reason_code","emphasis":null},{"kind":"context","text":"        , reason_description","emphasis":null},{"kind":"context","text":"        , total_cost as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__medications') }} as med","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"    and med.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"procedures as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        procedure_id as event_id","emphasis":null},{"kind":"context","text":"        , 'PROCEDURE' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , proc.encounter_id","emphasis":null},{"kind":"context","text":"        , cast(procedure_at as date) as event_start_date","emphasis":null},{"kind":"context","text":"        , cast(null as date) as event_end_date","emphasis":null},{"kind":"context","text":"        , procedure_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , procedure_description as description","emphasis":null},{"kind":"context","text":"        , reason_code","emphasis":null},{"kind":"context","text":"        , reason_description","emphasis":null},{"kind":"context","text":"        , base_cost as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__procedures') }} as proc","emphasis":null},{"kind":"context","text":"    where proc.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"all_events as (","emphasis":null},{"kind":"context","text":"    select * from conditions","emphasis":null},{"kind":"context","text":"    union all","emphasis":null},{"kind":"context","text":"    select * from medications","emphasis":null},{"kind":"context","text":"    union all","emphasis":null},{"kind":"context","text":"    select * from procedures","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"patients as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_patients') }}","emphasis":null},{"kind":"context","text":"    where is_current = true","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , date_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"encounters as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_key","emphasis":null},{"kind":"context","text":"        , encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('fct_encounters') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        row_number() over (order by e.event_id) as clinical_event_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- event identifiers","emphasis":null},{"kind":"context","text":"        , e.event_type","emphasis":null},{"kind":"context","text":"        , e.event_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , coalesce(p.patient_key, -1) as patient_key","emphasis":null},{"kind":"context","text":"        , e.patient_id","emphasis":null},{"kind":"context","text":"        , coalesce(enc.encounter_key, -1) as encounter_key","emphasis":null},{"kind":"context","text":"        , e.encounter_id","emphasis":null},{"kind":"context","text":"        , coalesce(d.date_key, -1) as event_date_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dates","emphasis":null},{"kind":"context","text":"        , e.event_start_date","emphasis":null},{"kind":"context","text":"        , e.event_end_date","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- code information","emphasis":null},{"kind":"context","text":"        , e.code","emphasis":null},{"kind":"context","text":"        , e.code_system","emphasis":null},{"kind":"context","text":"        , e.description","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- reason","emphasis":null},{"kind":"context","text":"        , e.reason_code","emphasis":null},{"kind":"context","text":"        , e.reason_description","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost","emphasis":null},{"kind":"context","text":"        , e.event_cost","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from all_events as e","emphasis":null},{"kind":"context","text":"    left join patients as p on e.patient_id = p.patient_id","emphasis":null},{"kind":"context","text":"    left join encounters as enc on e.encounter_id = enc.encounter_id","emphasis":null},{"kind":"context","text":"    left join dim_date as d on e.event_start_date = d.date_actual","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- cute-dbt fixture: synthetic body mod","emphasis":null}],"call_sites":[{"line":11,"text":"{{ quarantine_filter() }}"},{"line":47,"text":"{{ quarantine_filter() }}"}],"call_site_total":2,"call_site_cap":3,"inline_body":true},{"model_id":"model.healthcare_analytics.fct_encounters","name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='encounter_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with encounters as (","emphasis":null},{"kind":"context","text":"    select * from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"patients as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , birth_date","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_patients') }}","emphasis":null},{"kind":"context","text":"    where is_current = true","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"providers as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_providers') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"organizations as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_organizations') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"payers as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        payer_key","emphasis":null},{"kind":"context","text":"        , payer_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_payers') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , date_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        row_number() over (order by e.encounter_id) as encounter_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- natural key","emphasis":null},{"kind":"context","text":"        , e.encounter_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , coalesce(p.patient_key, -1) as patient_key","emphasis":null},{"kind":"context","text":"        , e.patient_id","emphasis":null},{"kind":"context","text":"        , coalesce(pr.provider_key, -1) as provider_key","emphasis":null},{"kind":"context","text":"        , e.provider_id","emphasis":null},{"kind":"context","text":"        , coalesce(o.organization_key, -1) as organization_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , coalesce(py.payer_key, -1) as payer_key","emphasis":null},{"kind":"context","text":"        , e.payer_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- date keys","emphasis":null},{"kind":"context","text":"        , coalesce(ds.date_key, -1) as encounter_start_date_key","emphasis":null},{"kind":"context","text":"        , coalesce(de.date_key, -1) as 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where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: dim_conditions\n    description: |\n      Master condition dimension for healthcare analytics.\n      One row per unique ICD/SNOMED condition code, deduplicated from staging.\n      Use for filtering and grouping by condition across fact tables.\n\n      Use cases:\n      - Condition-based cohort analysis\n      - Disease prevalence tracking\n      - Condition frequency and distribution reporting\n      - Join target for condition-related facts\n\n    config:\n      tags: ['analytics', 'marts', 'dimension']\n    meta:\n      grain: condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Condition lookup and filtering\"\n        - \"Cohort analysis by diagnosis\"\n        - \"Disease prevalence reporting\"\n      example_queries:\n        - name: \"Top 10 most common conditions\"\n          description: \"Identify the most prevalent conditions in the patient population\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , patient_count\n                , encounter_count\n            from main_marts.dim_conditions\n            order by patient_count desc\n            limit 10\n\n        - name: \"Conditions by encounter frequency\"\n          description: \"Find conditions that drive the most healthcare utilization\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , encounter_count\n                , patient_count\n                , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\n            from main_marts.dim_conditions\n            where patient_count > 0\n            order by encounter_count desc\n            limit 20\n\n        - name: \"Recently diagnosed conditions\"\n          description: \"Find conditions first diagnosed in the last year\"\n          sql: |\n            select\n                condition_code\n                , condition_description\n                , first_occurrence_date\n                , last_occurrence_date\n                , patient_count\n            from main_marts.dim_conditions\n            where first_occurrence_date >= current_date - interval '1 year'\n            order by first_occurrence_date desc\n\n    columns:\n      - name: condition_key\n        description: Surrogate key (row_number)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_code\n        description: ICD/SNOMED code (natural key)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: condition_description\n        description: Full condition name/description\n        data_tests:\n          - not_null\n\n      - name: first_occurrence_date\n        description: Earliest date this condition was diagnosed in the dataset\n\n      - name: last_occurrence_date\n        description: Most recent date this condition was diagnosed\n\n      - name: patient_count\n        description: Number of unique patients with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Total number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number \u003c= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day \u003c= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. Valid range is years 0001-2147483\n    (integer max 2,147,483,647). Safe for all practical date ranges.\n\n    Note on day_of_week: dbt_date uses US convention (Sunday=1, Saturday=7),\n    NOT ISO 8601 (Monday=1). Weekend and holiday logic accounts for this.\n#}\n\nwith date_spine as (\n    {{ dbt_date.get_date_dimension('1909-01-01', '2025-12-31') }}\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_date\n    description: >\n      Calendar dimension covering 1909-01-01 to 2025-12-31.\n      Provides date attributes, day/week/month/quarter/year hierarchies,\n      weekend flags, and US holiday identification.\n    data_tests:\n      # Row count validation - covers 117 years of dates (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 42000\n          config:\n            severity: warn\n    columns:\n      - name: date_key\n        description: Surrogate key in YYYYMMDD integer format (e.g., 20240115)\n        data_tests:\n          - unique\n          - not_null\n          # Date key must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 19090101\n              max_value: 20251231\n              config:\n                severity: warn\n\n      - name: date_actual\n        description: The actual date value\n        data_tests:\n          - unique\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1909-01-01'::date\"\n              max_value: \"'2025-12-31'::date\"\n              config:\n                severity: warn\n\n      - name: day_of_week\n        description: Day of week number (US convention - Sunday=1, Saturday=7)\n        data_tests:\n          - not_null\n          # Day of week must be 1-7 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 7\n              config:\n                severity: warn\n\n      - name: day_name\n        description: Full day name (e.g., Monday, Tuesday)\n        data_tests:\n          - not_null\n\n      - name: day_name_short\n        description: Abbreviated day name (e.g., Mon, Tue)\n        data_tests:\n          - not_null\n\n      - name: day_of_month\n        description: Day of the month (1-31)\n        data_tests:\n          - not_null\n          # Day of month must be 1-31 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 31\n              config:\n                severity: warn\n\n      - name: day_of_year\n        description: Day of the year (1-366)\n        data_tests:\n          - not_null\n          # Day of year must be 1-366 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 366\n              config:\n                severity: warn\n\n      - name: week_of_year\n        description: Week of the year (1-53)\n        data_tests:\n          - not_null\n          # Week of year must be 1-53 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 53\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: month_name\n        description: Full month name (e.g., January, February)\n        data_tests:\n          - not_null\n\n      - name: month_name_short\n        description: Abbreviated month name (e.g., Jan, Feb)\n        data_tests:\n          - not_null\n\n      - name: quarter_actual\n        description: Quarter number (1-4)\n        data_tests:\n          - not_null\n          # Quarter must be 1-4 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 4\n              config:\n                severity: warn\n\n      - name: quarter_name\n        description: Quarter name (Q1, Q2, Q3, Q4)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['Q1', 'Q2', 'Q3', 'Q4']\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: is_weekend\n        description: Boolean flag indicating if date falls on Saturday or Sunday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: is_us_holiday\n        description: Boolean flag indicating if date is a recognized US holiday\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: holiday_name\n        description: Name of the US holiday (null if not a holiday)"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_date","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_date_date_key.0869fd48f9"]},"evidence":[{"label":"unique_key","value":"date_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_organizations","path":"models/marts/core/dim_organizations.sql","description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_organizations.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_organizations\n    description: >\n      Organization dimension containing healthcare facilities.\n      Includes an unknown member row (organization_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 800\n          config:\n            severity: warn\n    columns:\n      - name: organization_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: organization_id\n        description: Natural organization identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_name\n        description: Name of the healthcare organization\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: revenue\n        description: Total revenue\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from \u003c= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_patients\n    description: >\n      Patient dimension with SCD Type 2 history tracking.\n      Each row represents a specific version of a patient's record.\n      Includes an unknown member row (patient_key = -1) for handling missing lookups.\n\n      SCD Type 2 columns: patient_key (surrogate), patient_id (natural),\n      valid_from, valid_to, is_current.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_key\n        description: Surrogate key (unique per version, -1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_id\n        description: Natural patient identifier (UUID), same across SCD2 versions\n        data_tests:\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: full_name\n        description: Patient's full name (first + last)\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          # Date range validation - exclude unknown member (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"birth_date is not null and patient_key != -1\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: age_years\n        description: Current age or age at death (in years)\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"age_years is not null\"\n              config:\n                severity: warn\n\n      - name: is_deceased\n        description: Boolean flag indicating if patient has died\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"patient_key != -1\"\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: valid_from\n        description: Date when this SCD2 version became active\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: valid_to\n        description: Date when this SCD2 version was superseded (null = current)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"valid_to is not null\"\n              config:\n                severity: warn\n\n      - name: is_current\n        description: Boolean flag indicating if this is the current version\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: [true, false]\n              quote: false\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"dim_providers","path":"models/marts/core/dim_providers.sql","description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_providers.sql","role":"final"}],"edges":[{"from":"providers","to":"sequenced","edge_type":"from"},{"from":"organizations","to":"sequenced","edge_type":"left"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","providers":"providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n)","unknown_member":"unknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":76,"col":1,"byte":1766},"end":{"line":76,"col":20,"byte":1785}},"final":{"start":{"line":70,"col":1,"byte":1677},"end":{"line":74,"col":2,"byte":1764}},"organizations":{"start":{"line":7,"col":1,"byte":93},"end":{"line":12,"col":2,"byte":226}},"providers":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":90}},"sequenced":{"start":{"line":14,"col":1,"byte":229},"end":{"line":48,"col":2,"byte":972}},"unknown_member":{"start":{"line":51,"col":1,"byte":1032},"end":{"line":68,"col":2,"byte":1674}}},"column_spans":{"(final 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when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"city":{"description":"City","documented":true},"gender":{"description":"Provider gender (M or F)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"gender","model":"{{ get_where_subquery(ref('dim_providers')) }}","values":["M","F"]},"node_id":"test.healthcare_analytics.accepted_values_dim_providers_gender__M__F.248d04b44f"}],"documented":true},"latitude":{"description":"Latitude coordinate","documented":true},"longitude":{"description":"Longitude coordinate","documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('dim_providers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_providers_organization_id.c650c07852"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for 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 -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_providers\n    description: >\n      Provider dimension containing healthcare providers (doctors, nurses, etc.).\n      Includes an unknown member row (provider_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 4000\n          config:\n            severity: warn\n    columns:\n      - name: provider_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n        data_tests:\n          - not_null\n\n      - name: gender\n        description: Provider gender (M or F)\n        data_tests:\n          - accepted_values:\n              values: ['M', 'F']\n              config:\n                where: \"provider_key != -1\"\n\n      - name: specialty\n        description: Medical specialty\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State (full state name)\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: latitude\n        description: Latitude coordinate\n\n      - name: longitude\n        description: Longitude coordinate\n\n      - name: utilization\n        description: Total utilization count\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"utilization is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_providers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_providers_provider_key.02ac65c919"]},"evidence":[{"label":"unique_key","value":"provider_key"}],"pin_node":"(final 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This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        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select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. 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...}"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_condition_cohorts","path":"models/marts/analytics/fct_condition_cohorts.sql","description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"patients","role":"import"},{"id":"dim_conditions","role":"import"},{"id":"condition_windows","role":"transform"},{"id":"with_dimensions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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select)"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"last_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_year"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_diagnosis_year"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"is_active"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"is_active"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"_loaded_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"_loaded_at"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_condition_cohorts\n    description: |\n      Patient-condition cohort analysis for disease management and population health.\n      One row per unique patient-condition combination with timeline and encounter details.\n      Grain: (patient_id, condition_code)\n\n      Use cases:\n      - Disease management program enrollment\n      - Cohort identification for clinical trials\n      - Chronic condition tracking\n      - Care gap analysis\n      - Risk stratification by condition\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Disease management programs\"\n        - \"Cohort identification\"\n        - \"Chronic condition tracking\"\n        - \"Care gap analysis\"\n      example_queries:\n        - name: \"Largest condition cohorts\"\n          description: \"Find conditions with the most patients for population health programs\"\n          sql: |\n            select\n                fcc.condition_code\n                , fcc.condition_description\n                , count(distinct fcc.patient_id) as patient_count\n                , sum(fcc.encounter_count) as total_encounters\n                , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\n                , sum(case when fcc.is_active then 1 else 0 end) as active_patients\n            from main_marts.fct_condition_cohorts fcc\n            group by 1, 2\n            order by patient_count desc\n            limit 15\n\n        - name: \"Chronic condition patients (long duration)\"\n          description: \"Identify patients with long-standing conditions for care management\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fcc.condition_description\n                , fcc.first_diagnosis_date\n                , fcc.months_with_condition\n                , fcc.encounter_count\n                , fcc.is_active\n            from main_marts.fct_condition_cohorts fcc\n            join main_marts.dim_patients p\n                on fcc.patient_key = p.patient_key\n            where fcc.months_with_condition >= 24\n                and fcc.is_active = true\n            order by fcc.months_with_condition desc\n            limit 50\n\n        - name: \"Patients with multiple conditions\"\n          description: \"Find patients with comorbidities for complex care programs\"\n          sql: |\n            with patient_condition_counts as (\n                select\n                    patient_id\n                    , patient_key\n                    , count(distinct condition_code) as condition_count\n                    , sum(case when is_active then 1 else 0 end) as active_condition_count\n                from main_marts.fct_condition_cohorts\n                group by 1, 2\n            )\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , pcc.condition_count\n                , pcc.active_condition_count\n            from patient_condition_counts pcc\n            join main_marts.dim_patients p\n                on pcc.patient_key = p.patient_key\n            where pcc.condition_count >= 3\n            order by pcc.condition_count desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - condition_code\n          config:\n            severity: error\n    columns:\n      - name: condition_cohort_key\n        description: Surrogate key (MD5 hash of patient_id + condition_code)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n        data_tests:\n          - not_null\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n        data_tests:\n          - not_null\n\n      - name: last_diagnosis_date\n        description: Date of most recent diagnosis (null if first occurrence only)\n\n      - name: first_diagnosis_year\n        description: Year of first diagnosis\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: months_with_condition\n        description: Months between first and last diagnosis (0 if single occurrence)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounter_count\n        description: Number of encounters with this condition\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: is_active\n        description: True if condition has no end date (currently active)\n        data_tests:\n          - not_null\n          - accepted_values:\n              arguments:\n                values: [true, false]\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":122,"col":1,"byte":2903},"end":{"line":122,"col":20,"byte":2922}},"dim_date":{"start":{"line":24,"col":1,"byte":435},"end":{"line":30,"col":2,"byte":564}},"encounters":{"start":{"line":5,"col":6,"byte":9},"end":{"line":22,"col":2,"byte":432}},"encounters_enriched":{"start":{"line":42,"col":1,"byte":827},"end":{"line":64,"col":2,"byte":1619}},"final":{"start":{"line":66,"col":1,"byte":1622},"end":{"line":120,"col":2,"byte":2901}},"procedures":{"start":{"line":32,"col":1,"byte":567},"end":{"line":39,"col":2,"byte":782}}},"column_spans":{"(final 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_cost_analysis\n    description: |\n      Detailed cost breakdown for financial analysis and revenue cycle management.\n      One row per encounter with cost allocation details across payer and patient.\n      Grain: encounter_id\n\n      Use cases:\n      - Financial performance reporting\n      - Payer mix analysis\n      - Patient out-of-pocket tracking\n      - Cost driver identification\n      - Revenue cycle optimization\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: encounter_id\n      owner: healthcare_analytics\n      uses:\n        - \"Financial reporting\"\n        - \"Payer mix analysis\"\n        - \"Cost allocation tracking\"\n        - \"Revenue cycle management\"\n      example_queries:\n        - name: \"Cost breakdown by payer\"\n          description: \"Analyze cost distribution across different insurance payers\"\n          sql: |\n            select\n                py.payer_name\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            group by py.payer_name\n            order by total_cost desc\n\n        - name: \"Monthly cost trends\"\n          description: \"Track cost trends over time for financial forecasting\"\n          sql: |\n            select\n                strftime(fca.encounter_date, '%Y-%m') as year_month\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\n                , round(sum(fca.payer_coverage), 2) as total_payer_coverage\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            where fca.year_actual >= 2023\n            group by 1\n            order by 1 desc\n\n        - name: \"Cost by encounter type\"\n          description: \"Compare costs across different encounter classes\"\n          sql: |\n            select\n                fca.encounter_class\n                , count(distinct fca.encounter_id) as encounter_count\n                , round(sum(fca.total_cost), 2) as total_cost\n                , round(avg(fca.total_cost), 2) as avg_cost\n                , round(avg(fca.procedure_count), 2) as avg_procedures\n                , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\n                , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_cost_analysis fca\n            group by fca.encounter_class\n            order by total_cost desc\n\n        - name: \"High-cost encounters\"\n          description: \"Identify individual high-cost encounters for review\"\n          sql: |\n            select\n                fca.encounter_id\n                , fca.encounter_date\n                , fca.encounter_class\n                , p.first_name || ' ' || p.last_name as patient_name\n                , py.payer_name\n                , fca.total_cost\n                , fca.payer_coverage\n                , fca.patient_responsibility\n                , fca.procedure_count\n            from main_marts.fct_cost_analysis fca\n            join main_marts.dim_patients p\n                on fca.patient_key = p.patient_key\n            join main_marts.dim_payers py\n                on fca.payer_key = py.payer_key\n            order by fca.total_cost desc\n            limit 25\n\n    columns:\n      - name: cost_analysis_key\n        description: Surrogate key (MD5 hash of encounter_id)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('fct_encounters')\n                field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: payer_key\n        description: Foreign key to dim_payers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_payers')\n                field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n\n      - name: encounter_date_key\n        description: Foreign key to dim_date\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: year_actual\n        description: Calendar year of encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, etc.)\n\n      - name: total_cost\n        description: Total claim cost\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: coverage_pct\n        description: Percentage covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: patient_cost_pct\n        description: Percentage owed by patient (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: cost_per_procedure\n        description: Average cost per procedure (null if no procedures)\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}},"raw_node_spans":{"dim_date":{"start":{"line":38,"col":1,"byte":685},"end":{"line":43,"col":2,"byte":781}},"encounters":{"start":{"line":3,"col":6,"byte":95},"end":{"line":6,"col":2,"byte":197}},"final":{"start":{"line":45,"col":1,"byte":784},"end":{"line":103,"col":2,"byte":2732}},"organizations":{"start":{"line":24,"col":1,"byte":463},"end":{"line":29,"col":2,"byte":585}},"patients":{"start":{"line":8,"col":1,"byte":200},"end":{"line":15,"col":2,"byte":351}},"payers":{"start":{"line":31,"col":1,"byte":588},"end":{"line":36,"col":2,"byte":682}},"providers":{"start":{"line":17,"col":1,"byte":354},"end":{"line":22,"col":2,"byte":460}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":94,"col":32,"byte":2276},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}},"raw_dag":{"nodes":[{"id":"dim_date","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"encounters","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"final","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"organizations","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"patients","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"payers","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"providers","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"dim_date":"dim_date","encounters":"encounters","final":"final","organizations":"organizations","patients":"patients","payers":"payers","providers":"providers"}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp 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from {{ ref('dim_payers') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters\n    description: >\n      Encounter fact table containing healthcare visits/encounters.\n      Links to patient, provider, organization, and payer dimensions.\n      Includes date keys, costs, and derived duration.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 40000\n          config:\n            severity: warn\n    columns:\n      - name: encounter_key\n        description: Surrogate key for the encounter\n        data_tests:\n          - unique\n          - not_null\n\n      - name: encounter_id\n        description: Natural encounter identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_providers')\n              field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_organizations')\n              field: organization_key\n\n      - name: organization_id\n        description: Natural organization identifier\n        data_tests:\n          - not_null\n\n      - name: payer_key\n        description: Foreign key to dim_payers (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_payers')\n              field: payer_key\n\n      - name: payer_id\n        description: Natural payer identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_start_date_key\n        description: Foreign key to dim_date for encounter start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"encounter_start_date_key != -1\"\n\n      - name: encounter_end_date_key\n        description: Foreign key to dim_date for encounter end\n\n      - name: encounter_class\n        description: Type of encounter (ambulatory, wellness, emergency, etc.)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_code\n        description: SNOMED CT code for the encounter type\n\n      - name: encounter_description\n        description: Description of the encounter type\n\n      - name: reason_code\n        description: SNOMED CT code for the reason of visit\n\n      - name: reason_description\n        description: Description of the reason for visit\n\n      - name: encounter_start_at\n        description: Start timestamp of the encounter\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              config:\n                severity: warn\n\n      - name: encounter_end_at\n        description: End timestamp of the encounter\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::timestamp\"\n              max_value: \"current_timestamp\"\n              row_condition: \"encounter_end_at is not null\"\n              config:\n                severity: warn\n\n      - name: duration_minutes\n        description: Duration of encounter in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: base_encounter_cost\n        description: Base cost of the encounter\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"base_encounter_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Total claim cost\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: payer_coverage\n        description: Amount covered by payer\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: patient_responsibility\n        description: Amount patient is responsible for (total_claim_cost - payer_coverage)\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: patient_age_at_encounter\n        description: Patient's age at the time of the encounter\n        data_tests:\n          # Age must be reasonable 0-150 years (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 150\n              row_condition: \"patient_age_at_encounter is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"unique_key","value":"encounter_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters","construct":"constraint.primary_key[encounter_key]","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_encounter_key.a3b77e9141"]},"evidence":[{"label":"constraint","value":"primary_key on encounter_key"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":13,"col":1,"byte":233},"end":{"line":22,"col":1,"byte":450}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":11,"col":2,"byte":231}}},"raw_zones":[{"kind":"for_loop","start":{"line":16,"col":9,"byte":304},"end":{"line":18,"col":21,"byte":428},"presence":"structural","node_id":"source"},{"kind":"incremental_guard","start":{"line":29,"col":1,"byte":594},"end":{"line":31,"col":12,"byte":691},"presence":"structural","node_id":"(final select)"}],"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":14,"col":5,"byte":244},"end":{"line":14,"col":17,"byte":256}},"(final select)\u001fencounter_type":{"start":{"line":16,"col":7,"byte":280},"end":{"line":16,"col":21,"byte":294}},"(final select)\u001fpatient_id":{"start":{"line":15,"col":7,"byte":263},"end":{"line":15,"col":17,"byte":273}},"(final select)\u001fstart_time":{"start":{"line":17,"col":7,"byte":301},"end":{"line":17,"col":17,"byte":311}},"(final select)\u001ftotal_cost_rounded":{"start":{"line":18,"col":7,"byte":318},"end":{"line":18,"col":25,"byte":336}},"source\u001fencounter_id":{"start":{"line":5,"col":9,"byte":38},"end":{"line":5,"col":21,"byte":50}},"source\u001fencounter_type":{"start":{"line":7,"col":11,"byte":82},"end":{"line":7,"col":25,"byte":96}},"source\u001fpatient_id":{"start":{"line":6,"col":11,"byte":61},"end":{"line":6,"col":21,"byte":71}},"source\u001fstart_time":{"start":{"line":8,"col":11,"byte":107},"end":{"line":8,"col":21,"byte":117}},"source\u001ftotal_cost_rounded":{"start":{"line":9,"col":11,"byte":128},"end":{"line":9,"col":53,"byte":170}}},"raw_node_spans":{"source":{"start":{"line":3,"col":6,"byte":185},"end":{"line":13,"col":2,"byte":476}}},"raw_column_spans":{"(final select)\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}},"source\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}}},"raw_dag":{"nodes":[{"id":"source","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"zone:0","role":"zone","is_zone":true,"presence":"structural"},{"id":"zone:1","role":"zone","is_zone":true,"presence":"structural"}]},"node_map":{"compiled":{"source":"source"}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost_rounded"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost_rounded"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        {% for cost_col in rounded_cost_columns %}\n        , round({{ cost_col }}, 2) as {{ cost_col }}_rounded\n        {% endfor %}\n    from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}\n","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":false,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters_monthly.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"encounters_with_date","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1374},"end":{"line":53,"col":20,"byte":1393}},"dim_date":{"start":{"line":16,"col":1,"byte":301},"end":{"line":22,"col":2,"byte":431}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":298}},"encounters_with_date":{"start":{"line":24,"col":1,"byte":434},"end":{"line":34,"col":2,"byte":790}},"final":{"start":{"line":36,"col":1,"byte":793},"end":{"line":51,"col":2,"byte":1372}}},"column_spans":{"(final 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  , year_actual\n        , month_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_month, year_actual, month_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_monthly\n    description: >\n      Monthly aggregated encounter statistics by encounter class.\n      Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 100\n          config:\n            severity: warn\n    columns:\n      - name: year_month\n        description: Year-month string (YYYY-MM format)\n        data_tests:\n          - not_null\n          # Pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{4}-\\\\d{2}$\"\n              config:\n                severity: warn\n\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Month number (1-12)\n        data_tests:\n          - not_null\n          # Month must be 1-12 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              max_value: 12\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from final","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_encounters_yearly\n    description: >\n      Yearly aggregated encounter statistics by encounter class.\n      Provides counts, costs, patient metrics, and encounters per patient ratio.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 10\n          config:\n            severity: warn\n    columns:\n      - name: year_actual\n        description: Year number\n        data_tests:\n          - not_null\n          # Year range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1909\n              max_value: 2025\n              config:\n                severity: warn\n\n      - name: encounter_class\n        description: Type of encounter\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['ambulatory', 'wellness', 'emergency', 'urgentcare', 'outpatient', 'inpatient']\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: unique_patients\n        description: Number of unique patients with encounters\n        data_tests:\n          - not_null\n          # Count must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 1\n              config:\n                severity: warn\n\n      - name: total_claim_cost\n        description: Sum of total claim costs\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_claim_cost is not null\"\n              config:\n                severity: warn\n\n      - name: total_payer_coverage\n        description: Sum of payer coverage amounts\n        data_tests:\n          # Coverage must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_payer_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: total_patient_responsibility\n        description: Sum of patient responsibility amounts\n        data_tests:\n          # Patient responsibility must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"total_patient_responsibility is not null\"\n              config:\n                severity: warn\n\n      - name: avg_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          # Duration must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"avg_duration_minutes is not null\"\n              config:\n                severity: warn\n\n      - name: encounters_per_patient\n        description: Average number of encounters per patient\n        data_tests:\n          # Ratio must be positive (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"encounters_per_patient is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_yearly","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"year_actual, encounter_class"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"state":"modified"},{"name":"fct_patient_summary","path":"models/marts/analytics/fct_patient_summary.sql","description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"conditions","role":"import"},{"id":"encounters_with_year","role":"transform"},{"id":"conditions_per_patient_year","role":"transform"},{"id":"patient_year_agg","role":"transform"},{"id":"with_conditions","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_patient_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_year","edge_type":"from"},{"from":"dim_date","to":"encounters_with_year","edge_type":"inner"},{"from":"procedures","to":"encounters_with_year","edge_type":"left"},{"from":"conditions","to":"conditions_per_patient_year","edge_type":"from"},{"from":"encounters_with_year","to":"patient_year_agg","edge_type":"from"},{"from":"patient_year_agg","to":"with_conditions","edge_type":"from"},{"from":"conditions_per_patient_year","to":"with_conditions","edge_type":"left"},{"from":"with_conditions","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","conditions":"conditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","conditions_per_patient_year":"conditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n)","encounters_with_year":"encounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final 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dimensions for foreign key lookups.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from {{ ref('stg_synthea__conditions') }}\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_patient_summary\n    description: |\n      Annual patient-level aggregations for outcomes tracking and population health analysis.\n      One row per patient per calendar year with comprehensive utilization and cost metrics.\n      Grain: (patient_id, year_actual)\n\n      Use cases:\n      - Patient outcomes tracking over time\n      - Annual cost summaries and trends\n      - Cohort analysis and patient stratification\n      - Health trajectory visualization\n      - High-cost patient identification\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: patient_id + year_actual\n      owner: healthcare_analytics\n      uses:\n        - \"Population health reporting\"\n        - \"Cost and utilization analysis\"\n        - \"Patient stratification\"\n        - \"Year-over-year trending\"\n      example_queries:\n        - name: \"Top 10 costliest patients by year\"\n          description: \"Identify high-cost patients for care management programs\"\n          sql: |\n            select\n                p.patient_id\n                , p.first_name || ' ' || p.last_name as patient_name\n                , fps.year_actual\n                , fps.total_cost\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.payer_coverage_pct\n            from main_marts.fct_patient_summary fps\n            join main_marts.dim_patients p\n                on fps.patient_key = p.patient_key\n            where fps.year_actual = 2024\n            order by fps.total_cost desc\n            limit 10\n\n        - name: \"Patient count and cost by age group\"\n          description: \"Analyze patient population distribution and costs by age cohort\"\n          sql: |\n            select\n                case\n                    when fps.patient_age_at_year_end < 18 then '0-17'\n                    when fps.patient_age_at_year_end < 35 then '18-34'\n                    when fps.patient_age_at_year_end < 50 then '35-49'\n                    when fps.patient_age_at_year_end < 65 then '50-64'\n                    else '65+'\n                end as age_group\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_cost\n                , round(avg(fps.encounter_count), 2) as avg_encounters\n            from main_marts.fct_patient_summary fps\n            where fps.year_actual = 2024\n            group by 1\n            order by 1\n\n        - name: \"Cost and utilization trends over time\"\n          description: \"Year-over-year comparison of costs and utilization metrics\"\n          sql: |\n            select\n                fps.year_actual\n                , count(distinct fps.patient_id) as patient_count\n                , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\n                , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\n                , round(avg(fps.total_cost), 2) as avg_annual_cost\n                , round(sum(fps.total_cost), 2) as total_population_cost\n                , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\n            from main_marts.fct_patient_summary fps\n            group by fps.year_actual\n            order by fps.year_actual desc\n\n        - name: \"High utilization patients\"\n          description: \"Find patients with above-average encounters and conditions\"\n          sql: |\n            with averages as (\n                select\n                    avg(encounter_count) as avg_encounters\n                    , avg(condition_count) as avg_conditions\n                from main_marts.fct_patient_summary\n                where year_actual = 2024\n            )\n            select\n                fps.patient_id\n                , fps.encounter_count\n                , fps.condition_count\n                , fps.total_cost\n                , fps.unique_providers\n            from main_marts.fct_patient_summary fps\n            cross join averages a\n            where fps.year_actual = 2024\n                and fps.encounter_count > a.avg_encounters * 2\n                and fps.condition_count > a.avg_conditions\n            order by fps.total_cost desc\n            limit 25\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - patient_id\n              - year_actual\n          config:\n            severity: error\n    columns:\n      - name: patient_summary_key\n        description: Surrogate key (MD5 hash of patient_id + year_actual)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_patients')\n                field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: Calendar year of the summary\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: patient_age_at_year_end\n        description: Patient age as of December 31 of the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: encounter_count\n        description: Number of encounters in the year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000\n              config:\n                severity: error\n\n      - name: procedure_count\n        description: Number of procedures in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: condition_count\n        description: Number of distinct conditions diagnosed in the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the year\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 10000000\n              config:\n                severity: error\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: patient_responsibility\n        description: Amount owed by patient (copay + coinsurance)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: payer_coverage_pct\n        description: Percentage of costs covered by insurance (0-100)\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 100\n              config:\n                severity: error\n\n      - name: unique_providers\n        description: Number of distinct providers seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 1000\n              config:\n                severity: error\n\n      - name: unique_organizations\n        description: Number of distinct organizations visited\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_patient_summary_patient_summary_key.e532e1ff45"]},"evidence":[{"label":"unique_key","value":"patient_summary_key"}],"pin_node":"(final select)"},{"check":"enforcement.constraint-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_patient_summary","construct":"constraint.primary_key[(patient_id, year_actual)]","verdict":{"status":"covered","by":["test.healthcare_analytics.dbt_utils_unique_combination_of_columns_fct_patient_summary_patient_id__year_actual.cdb3418166"]},"evidence":[{"label":"constraint","value":"primary_key on (patient_id, year_actual)"},{"label":"adapter","value":"duckdb (metadata-only)"},{"label":"backing-test","value":"present"}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_metrics","path":"models/marts/analytics/fct_provider_metrics.sql","description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_with_date","role":"transform"},{"id":"provider_month_agg","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_provider_metrics.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_with_date","edge_type":"from"},{"from":"dim_date","to":"encounters_with_date","edge_type":"inner"},{"from":"procedures","to":"encounters_with_date","edge_type":"left"},{"from":"encounters_with_date","to":"provider_month_agg","edge_type":"from"},{"from":"provider_month_agg","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , 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year-month","emphasis":null},{"kind":"context","text":"provider_month_agg as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- counts","emphasis":null},{"kind":"context","text":"        , count(*) as encounter_count","emphasis":null},{"kind":"context","text":"        , count(distinct e.patient_id) as unique_patient_count","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- patient metrics","emphasis":null},{"kind":"context","text":"        , avg(e.patient_age_at_encounter) as avg_patient_age","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- encounter metrics","emphasis":null},{"kind":"context","text":"        , avg(e.duration_minutes) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- procedure metrics","emphasis":null},{"kind":"removed","text":"        , sum(e.procedure_count) as procedure_total","emphasis":[36,51]},{"kind":"added","text":"        , sum(e.procedure_count) as total_procedures","emphasis":[36,52]},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost metrics","emphasis":null},{"kind":"context","text":"        , sum(e.total_claim_cost) as total_cost","emphasis":null},{"kind":"context","text":"        , sum(e.payer_coverage) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from encounters_with_date as e","emphasis":null},{"kind":"context","text":"    group by","emphasis":null},{"kind":"context","text":"        e.provider_id","emphasis":null},{"kind":"context","text":"        , e.provider_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , e.organization_key","emphasis":null},{"kind":"context","text":"        , e.year_actual","emphasis":null},{"kind":"context","text":"        , e.month_actual","emphasis":null},{"kind":"context","text":"        , e.year_month_str","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"        , organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- time grain","emphasis":null},{"kind":"context","text":"        , year_actual","emphasis":null},{"kind":"context","text":"        , month_actual","emphasis":null},{"kind":"context","text":"        , year_month_str","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: counts","emphasis":null},{"kind":"context","text":"        , encounter_count","emphasis":null},{"kind":"context","text":"        , unique_patient_count","emphasis":null},{"kind":"context","text":"        , total_procedures","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: averages","emphasis":null},{"kind":"context","text":"        , round(avg_patient_age, 2) as avg_patient_age","emphasis":null},{"kind":"context","text":"        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: costs","emphasis":null},{"kind":"context","text":"        , coalesce(total_cost, 0) as total_cost","emphasis":null},{"kind":"context","text":"        , coalesce(total_payer_coverage, 0) as total_payer_coverage","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- measures: derived ratios","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when encounter_count > 0","emphasis":null},{"kind":"context","text":"            then round(total_cost / encounter_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        , case","emphasis":null},{"kind":"context","text":"            when unique_patient_count > 0","emphasis":null},{"kind":"context","text":"            then round(cast(encounter_count as decimal) / unique_patient_count, 2)","emphasis":null},{"kind":"context","text":"            else 0","emphasis":null},{"kind":"context","text":"        end as encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from provider_month_agg","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null}]},"model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: fct_provider_metrics\n    description: |\n      Monthly provider-level utilization and performance metrics.\n      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.\n      Grain: (provider_id, year_actual, month_actual)\n\n      Use cases:\n      - Provider performance tracking\n      - Workload distribution analysis\n      - Cost efficiency comparisons\n      - Capacity planning and staffing\n      - Quality improvement initiatives\n\n    config:\n      tags: ['analytics', 'marts', 'fact']\n    meta:\n      grain: provider_id + year_month_str\n      owner: healthcare_analytics\n      uses:\n        - \"Provider productivity reporting\"\n        - \"Workload balancing\"\n        - \"Cost efficiency analysis\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Top providers by patient volume\"\n          description: \"Identify highest-volume providers for capacity planning\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            order by total_encounters desc\n            limit 15\n\n        - name: \"Provider metrics trend by month\"\n          description: \"Track provider productivity over time\"\n          sql: |\n            select\n                fpm.year_month_str\n                , count(distinct fpm.provider_id) as active_providers\n                , sum(fpm.encounter_count) as total_encounters\n                , sum(fpm.unique_patient_count) as total_patients\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\n            from main_marts.fct_provider_metrics fpm\n            where fpm.year_actual >= 2023\n            group by fpm.year_month_str\n            order by fpm.year_month_str desc\n\n        - name: \"Provider efficiency comparison\"\n          description: \"Compare providers by cost and volume efficiency\"\n          sql: |\n            select\n                p.provider_id\n                , p.name as provider_name\n                , p.specialty\n                , sum(fpm.encounter_count) as total_encounters\n                , round(sum(fpm.total_cost), 2) as total_cost\n                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\n                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\n                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\n            from main_marts.fct_provider_metrics fpm\n            join main_marts.dim_providers p\n                on fpm.provider_key = p.provider_key\n            where fpm.year_actual = 2024\n            group by 1, 2, 3\n            having sum(fpm.encounter_count) >= 10\n            order by avg_cost_per_encounter\n\n    data_tests:\n      # Grain test: enforce unique combination\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - provider_id\n              - year_actual\n              - month_actual\n          config:\n            severity: error\n    columns:\n      - name: provider_metrics_key\n        description: Surrogate key (MD5 hash of provider_id + year_month_str)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - not_null\n          - relationships:\n              arguments:\n                to: ref('dim_providers')\n                field: provider_key\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - not_null\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: year_actual\n        description: Calendar year\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1900\n                max_value: 2100\n              config:\n                severity: warn\n\n      - name: month_actual\n        description: Calendar month (1-12)\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 1\n                max_value: 12\n              config:\n                severity: error\n\n      - name: year_month_str\n        description: Year-month string (YYYY-MM)\n\n      - name: encounter_count\n        description: Number of encounters in the month\n        data_tests:\n          - not_null\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: unique_patient_count\n        description: Number of distinct patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_patient_age\n        description: Average age of patients seen\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n                max_value: 150\n              config:\n                severity: warn\n\n      - name: avg_encounter_duration_minutes\n        description: Average encounter duration in minutes\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_procedures\n        description: Total procedures performed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_cost\n        description: Total claim cost for the month\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_payer_coverage\n        description: Amount covered by insurance\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_cost_per_encounter\n        description: Average cost per encounter\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: encounters_per_patient_avg\n        description: Average encounters per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded","diff":{"lines":[{"kind":"context","text":"  - name: fct_provider_metrics","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      Monthly provider-level utilization and performance metrics.","emphasis":null},{"kind":"removed","text":"      One row per provider per month.","emphasis":[31,36]},{"kind":"added","text":"      One row per provider per calendar month with patient volumes, costs, and efficiency metrics.","emphasis":[31,97]},{"kind":"context","text":"      Grain: (provider_id, year_actual, month_actual)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      Use cases:","emphasis":null},{"kind":"context","text":"      - Provider performance tracking","emphasis":null},{"kind":"context","text":"      - Workload distribution analysis","emphasis":null},{"kind":"context","text":"      - Cost efficiency comparisons","emphasis":null},{"kind":"context","text":"      - Capacity planning and staffing","emphasis":null},{"kind":"context","text":"      - Quality improvement initiatives","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    config:","emphasis":null},{"kind":"context","text":"      tags: ['analytics', 'marts', 'fact']","emphasis":null},{"kind":"context","text":"    meta:","emphasis":null},{"kind":"context","text":"      grain: provider_id + year_month_str","emphasis":null},{"kind":"context","text":"      owner: healthcare_analytics","emphasis":null},{"kind":"context","text":"      uses:","emphasis":null},{"kind":"context","text":"        - \"Provider productivity reporting\"","emphasis":null},{"kind":"context","text":"        - \"Workload balancing\"","emphasis":null},{"kind":"context","text":"        - \"Cost efficiency analysis\"","emphasis":null},{"kind":"context","text":"        - \"Capacity planning\"","emphasis":null},{"kind":"context","text":"      example_queries:","emphasis":null},{"kind":"context","text":"        - name: \"Top providers by patient volume\"","emphasis":null},{"kind":"context","text":"          description: \"Identify highest-volume providers for capacity planning\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            order by total_encounters desc","emphasis":null},{"kind":"context","text":"            limit 15","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider metrics trend by month\"","emphasis":null},{"kind":"context","text":"          description: \"Track provider productivity over time\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                fpm.year_month_str","emphasis":null},{"kind":"context","text":"                , count(distinct fpm.provider_id) as active_providers","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , sum(fpm.unique_patient_count) as total_patients","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            where fpm.year_actual >= 2023","emphasis":null},{"kind":"context","text":"            group by fpm.year_month_str","emphasis":null},{"kind":"context","text":"            order by fpm.year_month_str desc","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        - name: \"Provider efficiency comparison\"","emphasis":null},{"kind":"context","text":"          description: \"Compare providers by cost and volume efficiency\"","emphasis":null},{"kind":"context","text":"          sql: |","emphasis":null},{"kind":"context","text":"            select","emphasis":null},{"kind":"context","text":"                p.provider_id","emphasis":null},{"kind":"context","text":"                , p.name as provider_name","emphasis":null},{"kind":"context","text":"                , p.specialty","emphasis":null},{"kind":"context","text":"                , sum(fpm.encounter_count) as total_encounters","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_cost), 2) as total_cost","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"                , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient","emphasis":null},{"kind":"context","text":"                , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter","emphasis":null},{"kind":"context","text":"            from main_marts.fct_provider_metrics fpm","emphasis":null},{"kind":"context","text":"            join main_marts.dim_providers p","emphasis":null},{"kind":"context","text":"                on fpm.provider_key = p.provider_key","emphasis":null},{"kind":"context","text":"            where fpm.year_actual = 2024","emphasis":null},{"kind":"context","text":"            group by 1, 2, 3","emphasis":null},{"kind":"context","text":"            having sum(fpm.encounter_count) >= 10","emphasis":null},{"kind":"context","text":"            order by avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    data_tests:","emphasis":null},{"kind":"context","text":"      # Grain test: enforce unique combination","emphasis":null},{"kind":"context","text":"      - dbt_utils.unique_combination_of_columns:","emphasis":null},{"kind":"context","text":"          arguments:","emphasis":null},{"kind":"context","text":"            combination_of_columns:","emphasis":null},{"kind":"context","text":"              - provider_id","emphasis":null},{"kind":"context","text":"              - year_actual","emphasis":null},{"kind":"context","text":"              - month_actual","emphasis":null},{"kind":"context","text":"          config:","emphasis":null},{"kind":"context","text":"            severity: error","emphasis":null},{"kind":"context","text":"    columns:","emphasis":null},{"kind":"context","text":"      - name: provider_metrics_key","emphasis":null},{"kind":"context","text":"        description: Surrogate key (MD5 hash of provider_id + year_month_str)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - unique","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_providers","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - relationships:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                to: ref('dim_providers')","emphasis":null},{"kind":"context","text":"                field: provider_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: provider_id","emphasis":null},{"kind":"context","text":"        description: Natural provider identifier","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_key","emphasis":null},{"kind":"context","text":"        description: Foreign key to dim_organizations","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: organization_id","emphasis":null},{"kind":"context","text":"        description: Natural organization identifier","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_actual","emphasis":null},{"kind":"context","text":"        description: Calendar year","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1900","emphasis":null},{"kind":"context","text":"                max_value: 2100","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: month_actual","emphasis":null},{"kind":"context","text":"        description: Calendar month (1-12)","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 1","emphasis":null},{"kind":"context","text":"                max_value: 12","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: year_month_str","emphasis":null},{"kind":"context","text":"        description: Year-month string (YYYY-MM)","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounter_count","emphasis":null},{"kind":"context","text":"        description: Number of encounters in the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - not_null","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: unique_patient_count","emphasis":null},{"kind":"context","text":"        description: Number of distinct patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_patient_age","emphasis":null},{"kind":"context","text":"        description: Average age of patients seen","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"                max_value: 150","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: warn","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_encounter_duration_minutes","emphasis":null},{"kind":"context","text":"        description: Average encounter duration in minutes","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_procedures","emphasis":null},{"kind":"context","text":"        description: Total procedures performed","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_cost","emphasis":null},{"kind":"context","text":"        description: Total claim cost for the month","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: total_payer_coverage","emphasis":null},{"kind":"context","text":"        description: Amount covered by insurance","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: avg_cost_per_encounter","emphasis":null},{"kind":"context","text":"        description: Average cost per encounter","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: encounters_per_patient_avg","emphasis":null},{"kind":"context","text":"        description: Average encounters per patient","emphasis":null},{"kind":"context","text":"        data_tests:","emphasis":null},{"kind":"context","text":"          - dbt_expectations.expect_column_values_to_be_between:","emphasis":null},{"kind":"context","text":"              arguments:","emphasis":null},{"kind":"context","text":"                min_value: 0","emphasis":null},{"kind":"context","text":"              config:","emphasis":null},{"kind":"context","text":"                severity: error","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"      - name: _loaded_at","emphasis":null},{"kind":"context","text":"        description: Timestamp when record was loaded","emphasis":null}]}},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_provider_metrics","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602"]},"evidence":[{"label":"unique_key","value":"provider_metrics_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_fct_provider_metrics_provider_metrics_key.d58939d602","causes":["where-filtered — only rows matching `year_actual >= 2024` are checked; the grain is unverified outside the filter","limit: 100 — failing-row reporting caps at 100 rows; the test still trips on the first failure, but the failure surface is truncated"]}],"pin_node":"(final select)"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":true,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"fct_provider_scorecard","path":"models/marts/analytics/fct_provider_scorecard.sql","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id"},"code_map":{"compiled":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).\nselect\n    provider_id\n    , avg(quality_score) as avg_quality_score\n    , count(*) as encounter_count\nfrom \"memory\".\"main_marts\".\"fct_provider_metrics\"\ngroup by provider_id","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":7,"col":21,"byte":250}}},"column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":4,"col":7,"byte":106},"end":{"line":4,"col":46,"byte":145}},"(final select)\u001fencounter_count":{"start":{"line":5,"col":7,"byte":152},"end":{"line":5,"col":34,"byte":179}},"(final select)\u001fprovider_id":{"start":{"line":3,"col":5,"byte":88},"end":{"line":3,"col":16,"byte":99}}},"raw_column_spans":{"(final select)\u001favg_quality_score":{"start":{"line":6,"col":29,"byte":223},"end":{"line":6,"col":46,"byte":240}},"(final select)\u001fencounter_count":{"start":{"line":7,"col":19,"byte":259},"end":{"line":7,"col":34,"byte":274}}},"raw_dag":{"nodes":[{"id":"(final select)","role":"terminal","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"(final select)":"(final 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)","emphasis":null},{"kind":"added","text":"}}","emphasis":null},{"kind":"added","text":"","emphasis":null},{"kind":"added","text":"-- A new provider scorecard model added in this PR (cute-dbt#416 NEW state).","emphasis":null},{"kind":"added","text":"select","emphasis":null},{"kind":"added","text":"    provider_id","emphasis":null},{"kind":"added","text":"    , avg(quality_score) as avg_quality_score","emphasis":null},{"kind":"added","text":"    , count(*) as encounter_count","emphasis":null},{"kind":"added","text":"from {{ ref('fct_provider_metrics') }}","emphasis":null},{"kind":"added","text":"group by provider_id","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to 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Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final 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month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","data_diff":{"given":[{"ordinal":1,"input":"ref('stg_synthea__patients')","diff":{"columns":[{"name":"patient_id","status":"present"},{"name":"state","status":"present"}],"rows":[{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"new":{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"new":{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},"changed":false},{"old":{"display":"Connecticutt","key":{"t":"str","v":"Connecticutt"}},"new":{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"new":{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},"changed":false},{"old":{"display":"Vermont","key":{"t":"str","v":"Vermont"}},"new":{"display":"","key":{"t":"null"}},"changed":true}]},{"kind":"unchanged","cells":[{"old":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"new":{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},"changed":false},{"old":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"new":{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},"changed":false}]}]}}],"expect":null},"given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The 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medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , 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select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}},"raw_dag":{"nodes":[{"id":"combined_metrics","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"encounter_metrics","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"medication_metrics","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"combined_metrics":"combined_metrics","encounter_metrics":"encounter_metrics","medication_metrics":"medication_metrics"}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) 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100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final 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select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","yaml_diff":{"lines":[{"kind":"context","text":"  # LEADING bracket for test 2 — sits between the blank-line","emphasis":null},{"kind":"context","text":"  # terminator above and `- name:` below with no further gap, so the","emphasis":null},{"kind":"context","text":"  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).","emphasis":null},{"kind":"context","text":"  - name: test_mart_dq_summary_zero_quarantined_when_all_valid","emphasis":null},{"kind":"context","text":"    description: |","emphasis":null},{"kind":"context","text":"      mart_dq_summary should report zero quarantined_count and 0.00","emphasis":null},{"kind":"context","text":"      quarantine_rate_pct when every upstream row passes data-quality","emphasis":null},{"kind":"context","text":"      validations. Uses sql format for the GIVEN mocks and dict format","emphasis":null},{"kind":"context","text":"      for the EXPECT — dict expects only compare listed columns, so","emphasis":null},{"kind":"context","text":"      non-deterministic outputs (`_generated_at` from","emphasis":null},{"kind":"context","text":"      `current_timestamp`) are excluded from comparison.","emphasis":null},{"kind":"context","text":"    model: mart_dq_summary","emphasis":null},{"kind":"context","text":"    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /","emphasis":null},{"kind":"context","text":"    # #73). Demonstrates that the slicer ride-along applies to every","emphasis":null},{"kind":"context","text":"    # unit_test, not just the first in the file.","emphasis":null},{"kind":"context","text":"    given:","emphasis":null},{"kind":"context","text":"      - input: ref('stg_synthea__encounters')","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_encounter_timestamps","emphasis":null},{"kind":"context","text":"            , true as no_future_encounter_dates","emphasis":null},{"kind":"context","text":"            , true as end_after_1900","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"      - input: ref(\"stg_synthea__medications\")","emphasis":null},{"kind":"context","text":"        format: sql","emphasis":null},{"kind":"context","text":"        rows: |","emphasis":null},{"kind":"context","text":"          select","emphasis":null},{"kind":"context","text":"            true as is_dq_valid","emphasis":null},{"kind":"context","text":"            , true as valid_medication_dates","emphasis":null},{"kind":"context","text":"            , true as no_future_medication_dates","emphasis":null},{"kind":"context","text":"            , true as start_after_1900","emphasis":null},{"kind":"context","text":"            , true as end_after_1900_if_present","emphasis":null},{"kind":"context","text":"    expect:","emphasis":null},{"kind":"context","text":"      format: dict","emphasis":null},{"kind":"context","text":"      rows:","emphasis":null},{"kind":"context","text":"        - entity_type: 'encounters'","emphasis":null},{"kind":"removed","text":"          quarantined_count: null","emphasis":[29,33]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"        - entity_type: 'medications'","emphasis":null},{"kind":"removed","text":"          quarantined_count: 1","emphasis":[29,30]},{"kind":"removed","text":"          total_count: 2","emphasis":[23,24]},{"kind":"added","text":"          quarantined_count: 0","emphasis":[29,30]},{"kind":"added","text":"          total_count: 1","emphasis":[23,24]},{"kind":"context","text":"          quarantine_rate_pct: 0.00","emphasis":null},{"kind":"context","text":"    overrides:","emphasis":null},{"kind":"context","text":"      macros:","emphasis":null},{"kind":"context","text":"        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"","emphasis":null},{"kind":"context","text":"      vars:","emphasis":null},{"kind":"removed","text":"        dq_quarantine_threshold: 10","emphasis":[33,35]},{"kind":"added","text":"        dq_quarantine_threshold: 5","emphasis":[33,34]},{"kind":"context","text":"      env_vars:","emphasis":null},{"kind":"context","text":"        DBT_DQ_REGION: 'us-east-1'","emphasis":null},{"kind":"context","text":"  # TRAILING bracket for test 2 — terminated by the blank line","emphasis":null},{"kind":"context","text":"  # separating it from test 3 (cute-dbt#69 / #73).","emphasis":null}]},"data_diff":{"given":[],"expect":{"columns":[{"name":"entity_type","status":"present"},{"name":"quarantine_rate_pct","status":"present"},{"name":"quarantined_count","status":"present"},{"name":"total_count","status":"present"}],"rows":[{"kind":"modified","cells":[{"old":{"display":"encounters","key":{"t":"str","v":"encounters"}},"new":{"display":"encounters","key":{"t":"str","v":"encounters"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"","key":{"t":"null"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]},{"kind":"modified","cells":[{"old":{"display":"medications","key":{"t":"str","v":"medications"}},"new":{"display":"medications","key":{"t":"str","v":"medications"}},"changed":false},{"old":{"display":"0.00","key":{"t":"number","v":"0"}},"new":{"display":"0.0","key":{"t":"number","v":"0"}},"changed":false},{"old":{"display":"1","key":{"t":"number","v":"1"}},"new":{"display":"0","key":{"t":"number","v":"0"}},"changed":true},{"old":{"display":"2","key":{"t":"number","v":"2"}},"new":{"display":"1","key":{"t":"number","v":"1"}},"changed":true}]}]}},"overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}],"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"var_references":[{"name":"dq_quarantine_threshold","tier":"direct"}],"axes":{"body":false,"config":true,"unit_test":true},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_encounter_summary","path":"models/marts/analytics/views/v_encounter_summary.sql","description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_encounter_summary.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , 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persisted) - used as a building block\n    Use cases: Ad-hoc queries, building blocks for other models, BI tool queries\n\n    Joins fct_encounters with all dimension tables to provide a denormalized\n    view with human-readable names instead of just keys.\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_at\n        , encounter_class\n        , encounter_description\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from {{ ref('fct_encounters') }}\n),\n\npatients as (\n    select\n        patient_key\n        , full_name as patient_name\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_name\n    from {{ ref('dim_providers') }}\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_name\n    from {{ ref('dim_organizations') }}\n),\n\npayers as (\n    select\n        payer_key\n        , payer_name\n    from {{ ref('dim_payers') }}\n),\n\nfinal as (\n    select\n        -- identifiers\n        e.encounter_id\n        , cast(e.encounter_start_at as date) as encounter_date\n\n        -- patient\n        , e.patient_id\n        , coalesce(p.patient_name, 'Unknown Patient') as patient_name\n\n        -- provider\n        , e.provider_id\n        , coalesce(pr.provider_name, 'Unknown Provider') as provider_name\n\n        -- organization\n        , e.organization_id\n        , coalesce(o.organization_name, 'Unknown Organization') as organization_name\n\n        -- payer\n        , e.payer_id\n        , coalesce(py.payer_name, 'Unknown Payer') as payer_name\n\n        -- encounter details\n        , e.encounter_class\n        , e.encounter_description\n        , e.duration_minutes\n\n        -- costs\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n\n    from encounters as e\n    left join patients as p on e.patient_key = p.patient_key\n    left join providers as pr on e.provider_key = pr.provider_key\n    left join organizations as o on e.organization_key = o.organization_key\n    left join payers as py on e.payer_key = py.payer_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_encounter_summary\n    description: |\n      Enriched encounter view with dimension attributes for ad-hoc analysis.\n      Ephemeral (not persisted) - used as a building block for other queries.\n      Joins encounters with patient, provider, organization, and payer details.\n\n      Use cases:\n      - Ad-hoc encounter analysis\n      - Building block for custom reports\n      - Quick encounter lookups with full context\n\n    config:\n      materialized: ephemeral\n      tags: ['analytics', 'marts', 'view']\n    columns:\n      - name: encounter_id\n        description: Unique encounter identifier\n\n      - name: encounter_date\n        description: Date of the encounter\n\n      - name: patient_id\n        description: Patient identifier\n\n      - name: patient_name\n        description: Full patient name\n\n      - name: provider_id\n        description: Provider identifier\n\n      - name: provider_name\n        description: Full provider name\n\n      - name: organization_id\n        description: Organization identifier\n\n      - name: organization_name\n        description: Organization name\n\n      - name: payer_id\n        description: Payer identifier\n\n      - name: payer_name\n        description: Payer name\n\n      - name: encounter_class\n        description: Type of encounter\n\n      - name: total_cost\n        description: Total claim cost\n\n      - name: payer_coverage\n        description: Amount covered by insurance\n\n      - name: patient_responsibility\n        description: Amount owed by patient"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_patient_current_conditions","path":"models/marts/analytics/views/v_patient_current_conditions.sql","description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","dag":{"nodes":[{"id":"condition_cohorts","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_patient_current_conditions.sql","role":"final"}],"edges":[{"from":"condition_cohorts","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","condition_cohorts":"condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n)","final":"final as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)"},"code_map":{"compiled":"\n\n\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\"\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":33,"col":1,"byte":623},"end":{"line":33,"col":20,"byte":642}},"condition_cohorts":{"start":{"line":5,"col":6,"byte":9},"end":{"line":18,"col":2,"byte":357}},"final":{"start":{"line":20,"col":1,"byte":360},"end":{"line":31,"col":2,"byte":621}}},"column_spans":{"(final 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}}","to":"ref('dim_conditions')"},"node_id":"test.healthcare_analytics.relationships_v_patient_current_conditions_condition_key__condition_key__ref_dim_conditions_.44a146ce81"}],"documented":true},"encounter_count":{"description":"Number of encounters with this condition","documented":true},"first_diagnosis_date":{"description":"Date of first diagnosis","documented":true},"months_with_condition":{"description":"Duration of condition in months","documented":true},"patient_id":{"description":"Natural patient identifier","documented":true},"patient_key":{"description":"Foreign key to dim_patients","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"fct_condition_cohorts"}},"column":"is_active"},"to_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"is_active"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_cohorts"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_diagnosis_date"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_diagnosis_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"months_with_condition"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"months_with_condition"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Patient Current Conditions View\n\n    Purpose: Current (active) conditions for each patient\n    Grain: One row per patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_patient_current_conditions\n    description: |\n      Current (active) conditions for each patient, filtered from fct_condition_cohorts.\n      Use for patient panels, disease management dashboards, and care gap identification.\n      Grain: (patient_id, condition_code) - only active conditions\n\n      Use cases:\n      - Patient panel management\n      - Active disease registries\n      - Care gap identification\n      - Real-time patient condition views\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: patient_id + condition_code\n      owner: healthcare_analytics\n      uses:\n        - \"Patient panel displays\"\n        - \"Active condition registries\"\n        - \"Care gap dashboards\"\n      example_queries:\n        - name: \"Patient active conditions lookup\"\n          description: \"Get all active conditions for a specific patient\"\n          sql: |\n            select\n                vpcc.patient_id\n                , vpcc.condition_code\n                , vpcc.condition_description\n                , vpcc.first_diagnosis_date\n                , vpcc.months_with_condition\n                , vpcc.encounter_count\n            from main_marts.v_patient_current_conditions vpcc\n            where vpcc.patient_id = 'patient-uuid-here'\n            order by vpcc.first_diagnosis_date\n\n        - name: \"Active condition summary\"\n          description: \"Count of patients by active condition for disease registries\"\n          sql: |\n            select\n                vpcc.condition_code\n                , vpcc.condition_description\n                , count(distinct vpcc.patient_id) as active_patient_count\n                , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\n                , round(avg(vpcc.encounter_count), 1) as avg_encounters\n            from main_marts.v_patient_current_conditions vpcc\n            group by 1, 2\n            order by active_patient_count desc\n            limit 20\n\n    columns:\n      - name: patient_key\n        description: Foreign key to dim_patients\n\n      - name: patient_id\n        description: Natural patient identifier\n\n      - name: condition_key\n        description: Foreign key to dim_conditions\n        data_tests:\n          - relationships:\n              arguments:\n                to: ref('dim_conditions')\n                field: condition_key\n\n      - name: condition_code\n        description: ICD/SNOMED condition code\n\n      - name: condition_description\n        description: Full condition name\n\n      - name: first_diagnosis_date\n        description: Date of first diagnosis\n\n      - name: months_with_condition\n        description: Duration of condition in months\n\n      - name: encounter_count\n        description: Number of encounters with this condition"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"v_provider_active_patients.sql","role":"final"}],"edges":[{"from":"patient_recent_encounters","to":"patient_provider_assignment","edge_type":"from"},{"from":"current_conditions","to":"active_conditions_by_provider","edge_type":"from"},{"from":"patient_provider_assignment","to":"active_conditions_by_provider","edge_type":"inner"},{"from":"active_conditions_by_provider","to":"provider_summary","edge_type":"from"},{"from":"providers","to":"final","edge_type":"from"},{"from":"provider_summary","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","active_conditions_by_provider":"active_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: v_provider_active_patients\n    description: |\n      Active patient count per provider for panel management and workload analysis.\n      Aggregates patients with active conditions by their treating provider.\n      Grain: provider_id\n\n      Use cases:\n      - Provider panel size management\n      - Workload distribution analysis\n      - Capacity planning\n      - Provider assignment optimization\n\n    config:\n      materialized: table\n      tags: ['analytics', 'marts', 'view']\n    meta:\n      grain: provider_id\n      owner: healthcare_analytics\n      uses:\n        - \"Provider panel management\"\n        - \"Workload balancing\"\n        - \"Capacity planning\"\n      example_queries:\n        - name: \"Provider panel sizes\"\n          description: \"View provider workload by active patient and condition counts\"\n          sql: |\n            select\n                vpap.provider_id\n                , vpap.provider_name\n                , vpap.active_patient_count\n                , vpap.total_active_conditions\n                , vpap.avg_conditions_per_patient\n            from main_marts.v_provider_active_patients vpap\n            where vpap.active_patient_count > 0\n            order by vpap.active_patient_count desc\n            limit 25\n\n        - name: \"Provider workload distribution\"\n          description: \"Analyze distribution of patient panels across providers\"\n          sql: |\n            select\n                case\n                    when vpap.active_patient_count = 0 then '0'\n                    when vpap.active_patient_count \u003c= 10 then '1-10'\n                    when vpap.active_patient_count \u003c= 50 then '11-50'\n                    when vpap.active_patient_count \u003c= 100 then '51-100'\n                    else '100+'\n                end as panel_size_bucket\n                , count(*) as provider_count\n                , sum(vpap.active_patient_count) as total_patients\n            from main_marts.v_provider_active_patients vpap\n            group by 1\n            order by 1\n\n    columns:\n      - name: provider_key\n        description: Foreign key to dim_providers\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_id\n        description: Natural provider identifier\n        data_tests:\n          - unique\n          - not_null\n\n      - name: provider_name\n        description: Full name of the provider\n\n      - name: organization_key\n        description: Foreign key to dim_organizations\n\n      - name: organization_id\n        description: Natural organization identifier\n\n      - name: active_patient_count\n        description: Number of patients with active conditions\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: total_active_conditions\n        description: Total number of active conditions managed\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error\n\n      - name: avg_conditions_per_patient\n        description: Average number of active conditions per patient\n        data_tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              arguments:\n                min_value: 0\n              config:\n                severity: error"},"tests":[],"is_recursive":false,"config_attributions":[{"key":"materialized","path":"models.healthcare_analytics.marts"}],"axes":{"body":false,"config":true,"unit_test":false},"config_file":"models/marts/analytics/_analytics__models.yml","state":"modified"}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"enforcement.constraint-unbacked":{"name":"Declared constraint without a backing test","group":"enforcement","tier":"total","instrument":"data-test","conditions":["the model declares a primary_key or unique constraint (model-level constraints[] or a column-level constraint) whose enforcement on the manifest's adapter is metadata-only (NotEnforced) — the warehouse accepts the declaration but does not enforce uniqueness at write time","no enabled generic uniqueness data test (unique, attached to the model) backs the constrained column — the constraint→test edge is INFERRED by column + test-name match, since the manifest never links a constraint to its test","the verdict is UNCOVERED only when the constraint is declared, metadata-only, AND has no inferred backing test; a backing test (even one cute-dbt could miss) keeps it silent"],"exclusions":["a constraint the adapter ENFORCES at write time (e.g. not_null / foreign_key on Postgres/DuckDB) is never a gap — the warehouse guarantees it","a constraint kind with no column-level generic-test backing (check / custom / foreign_key) is out of this inference and never reported here","the inferred edge can MISS a renamed test or a singular/custom test asserting the same uniqueness — the copy says \"backing test\" (an authoring-discipline cue), never that the warehouse lacks an index; columns are authored-YAML-only, so this is never a warehouse-truth claim","the whole `enforcement` group is gated behind the governance experiment — off by default, it never fires on a non-governance report"],"rationale":"A primary-key / unique constraint that the warehouse treats as metadata-only is a DECLARED guarantee with nothing checking it: duplicate rows load silently, and any downstream join or incremental merge that trusts the declared grain corrupts. A backing data test is the only thing that actually verifies the declared uniqueness on this adapter.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/enforcement.constraint-unbacked.html"},"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}},"project_definition":{"name":"healthcare_analytics","version":"1.0.0","require_dbt_version":">=1.8.0","vars":{"default_state_grid_density":"monthly","dq_quarantine_threshold":5},"vars_spans":{"default_state_grid_density":{"line":12,"column":3},"dq_quarantine_threshold":{"line":11,"column":3}},"config_trees":{"models":{"children":{"healthcare_analytics":{"configs":{"materialized":"view"},"children":{"marts":{"configs":{"materialized":"table"}},"staging":{"configs":{"materialized":"view"}}}}}}},"dispatch":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}],"on_run_start":["grant select on all tables in schema analytics to role reporting"],"paths":{"clean-targets":["target","dbt_packages"],"model-paths":["models"],"test-paths":["tests"]},"other":{"profile":"healthcare_analytics"}},"project_change_panel":{"kind":"categorized","changes":[{"category":"vars","label":"default_state_grid_density","old":"weekly","new":"monthly","vars":{"entries":[{"name":"default_state_grid_density","old":"weekly","new":"monthly","config":["model.healthcare_analytics.mart_date_state_grid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"vars","label":"dq_quarantine_threshold","old":10,"new":5,"vars":{"entries":[{"name":"dq_quarantine_threshold","old":10,"new":5,"direct":["model.healthcare_analytics.mart_dq_summary"],"via_macros":[{"model":"model.healthcare_analytics.stg_synthea__encounters","via":"macro.healthcare_analytics.add_dq_flags"},{"model":"model.healthcare_analytics.stg_synthea__medications","via":"macro.healthcare_analytics.add_dq_flags"}],"insulated_tests":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]}],"footprint":{"models_scanned":35,"macros_scanned":89,"python_models":0}}},{"category":"config_tree","label":"models.healthcare_analytics.marts: +materialized","old":"view","new":"table","tree":{"section":"models","segments":["healthcare_analytics","marts"],"key":"materialized"}},{"category":"dispatch","label":"dispatch","old":[{"macro_namespace":"dbt_utils","search_order":["dbt_utils","healthcare_analytics"]}],"new":[{"macro_namespace":"dbt_utils","search_order":["healthcare_analytics","dbt_utils"]}]},{"category":"hooks","label":"on-run-start","old":["grant usage on schema analytics to role reporting"],"new":["grant select on all tables in schema analytics to role reporting"],"hook":{"sql_diff":{"lines":[{"kind":"removed","text":"grant usage on schema analytics to role reporting","emphasis":[6,13]},{"kind":"added","text":"grant select on all tables in schema analytics to role reporting","emphasis":[6,28]}]},"operation_ids":["operation.healthcare_analytics.healthcare_analytics-on-run-start-0"],"manifest":"matched"}}]},"governance":{"group_chips":[{"group":"clinical_quality","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com"}],"blast_radius":[{"exposure_label":"provider_quality_dashboard","exposure_type":"dashboard","owner_name":"Clinical Quality Team","owner_email":"clinical-quality@example.com","in_scope_model_count":7}],"lifecycle_chips":[{"kind":"group-owner-touch","label":"Touches group clinical_quality (owner: clinical-quality@example.com)"}],"meta_tags":[{"model":"dim_conditions","tags":["analytics","dimension","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify the most prevalent conditions in the patient population\",\"name\":\"Top 10 most common conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , patient_count\\n    , encounter_count\\nfrom main_marts.dim_conditions\\norder by patient_count desc\\nlimit 10\"},{\"description\":\"Find conditions that drive the most healthcare utilization\",\"name\":\"Conditions by encounter frequency\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , encounter_count\\n    , patient_count\\n    , round(encounter_count * 1.0 / patient_count, 2) as avg_encounters_per_patient\\nfrom main_marts.dim_conditions\\nwhere patient_count > 0\\norder by encounter_count desc\\nlimit 20\"},{\"description\":\"Find conditions first diagnosed in the last year\",\"name\":\"Recently diagnosed conditions\",\"sql\":\"select\\n    condition_code\\n    , condition_description\\n    , first_occurrence_date\\n    , last_occurrence_date\\n    , patient_count\\nfrom main_marts.dim_conditions\\nwhere first_occurrence_date >= current_date - interval '1 year'\\norder by first_occurrence_date desc\"}]"},{"key":"grain","value":"condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Condition lookup and filtering\",\"Cohort analysis by diagnosis\",\"Disease prevalence reporting\"]"}],"columns":[]},{"model":"dim_payers","tags":[],"meta":[],"columns":[{"column":"payer_key","tags":["dimension_key"],"meta":[{"key":"owner","value":"clinical-quality"},{"key":"pii","value":"false"}]}]},{"model":"fct_condition_cohorts","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Find conditions with the most patients for population health programs\",\"name\":\"Largest condition cohorts\",\"sql\":\"select\\n    fcc.condition_code\\n    , fcc.condition_description\\n    , count(distinct fcc.patient_id) as patient_count\\n    , sum(fcc.encounter_count) as total_encounters\\n    , round(avg(fcc.months_with_condition), 1) as avg_months_with_condition\\n    , sum(case when fcc.is_active then 1 else 0 end) as active_patients\\nfrom main_marts.fct_condition_cohorts fcc\\ngroup by 1, 2\\norder by patient_count desc\\nlimit 15\"},{\"description\":\"Identify patients with long-standing conditions for care management\",\"name\":\"Chronic condition patients (long duration)\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fcc.condition_description\\n    , fcc.first_diagnosis_date\\n    , fcc.months_with_condition\\n    , fcc.encounter_count\\n    , fcc.is_active\\nfrom main_marts.fct_condition_cohorts fcc\\njoin main_marts.dim_patients p\\n    on fcc.patient_key = p.patient_key\\nwhere fcc.months_with_condition >= 24\\n    and fcc.is_active = true\\norder by fcc.months_with_condition desc\\nlimit 50\"},{\"description\":\"Find patients with comorbidities for complex care programs\",\"name\":\"Patients with multiple conditions\",\"sql\":\"with patient_condition_counts as (\\n    select\\n        patient_id\\n        , patient_key\\n        , count(distinct condition_code) as condition_count\\n        , sum(case when is_active then 1 else 0 end) as active_condition_count\\n    from main_marts.fct_condition_cohorts\\n    group by 1, 2\\n)\\nselect\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , pcc.condition_count\\n    , pcc.active_condition_count\\nfrom patient_condition_counts pcc\\njoin main_marts.dim_patients p\\n    on pcc.patient_key = p.patient_key\\nwhere pcc.condition_count >= 3\\norder by pcc.condition_count desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Disease management programs\",\"Cohort identification\",\"Chronic condition tracking\",\"Care gap analysis\"]"}],"columns":[]},{"model":"fct_cost_analysis","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Analyze cost distribution across different insurance payers\",\"name\":\"Cost breakdown by payer\",\"sql\":\"select\\n    py.payer_name\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(sum(fca.patient_responsibility), 2) as total_patient_responsibility\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\ngroup by py.payer_name\\norder by total_cost desc\"},{\"description\":\"Track cost trends over time for financial forecasting\",\"name\":\"Monthly cost trends\",\"sql\":\"select\\n    strftime(fca.encounter_date, '%Y-%m') as year_month\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost_per_encounter\\n    , round(sum(fca.payer_coverage), 2) as total_payer_coverage\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\nwhere fca.year_actual >= 2023\\ngroup by 1\\norder by 1 desc\"},{\"description\":\"Compare costs across different encounter classes\",\"name\":\"Cost by encounter type\",\"sql\":\"select\\n    fca.encounter_class\\n    , count(distinct fca.encounter_id) as encounter_count\\n    , round(sum(fca.total_cost), 2) as total_cost\\n    , round(avg(fca.total_cost), 2) as avg_cost\\n    , round(avg(fca.procedure_count), 2) as avg_procedures\\n    , round(avg(fca.cost_per_procedure), 2) as avg_cost_per_procedure\\n    , round(avg(fca.coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_cost_analysis fca\\ngroup by fca.encounter_class\\norder by total_cost desc\"},{\"description\":\"Identify individual high-cost encounters for review\",\"name\":\"High-cost encounters\",\"sql\":\"select\\n    fca.encounter_id\\n    , fca.encounter_date\\n    , fca.encounter_class\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , py.payer_name\\n    , fca.total_cost\\n    , fca.payer_coverage\\n    , fca.patient_responsibility\\n    , fca.procedure_count\\nfrom main_marts.fct_cost_analysis fca\\njoin main_marts.dim_patients p\\n    on fca.patient_key = p.patient_key\\njoin main_marts.dim_payers py\\n    on fca.payer_key = py.payer_key\\norder by fca.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"encounter_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Financial reporting\",\"Payer mix analysis\",\"Cost allocation tracking\",\"Revenue cycle management\"]"}],"columns":[]},{"model":"fct_patient_summary","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify high-cost patients for care management programs\",\"name\":\"Top 10 costliest patients by year\",\"sql\":\"select\\n    p.patient_id\\n    , p.first_name || ' ' || p.last_name as patient_name\\n    , fps.year_actual\\n    , fps.total_cost\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.payer_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\njoin main_marts.dim_patients p\\n    on fps.patient_key = p.patient_key\\nwhere fps.year_actual = 2024\\norder by fps.total_cost desc\\nlimit 10\"},{\"description\":\"Analyze patient population distribution and costs by age cohort\",\"name\":\"Patient count and cost by age group\",\"sql\":\"select\\n    case\\n        when fps.patient_age_at_year_end < 18 then '0-17'\\n        when fps.patient_age_at_year_end < 35 then '18-34'\\n        when fps.patient_age_at_year_end < 50 then '35-49'\\n        when fps.patient_age_at_year_end < 65 then '50-64'\\n        else '65+'\\n    end as age_group\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_cost\\n    , round(avg(fps.encounter_count), 2) as avg_encounters\\nfrom main_marts.fct_patient_summary fps\\nwhere fps.year_actual = 2024\\ngroup by 1\\norder by 1\"},{\"description\":\"Year-over-year comparison of costs and utilization metrics\",\"name\":\"Cost and utilization trends over time\",\"sql\":\"select\\n    fps.year_actual\\n    , count(distinct fps.patient_id) as patient_count\\n    , round(avg(fps.encounter_count), 2) as avg_encounters_per_patient\\n    , round(avg(fps.condition_count), 2) as avg_conditions_per_patient\\n    , round(avg(fps.total_cost), 2) as avg_annual_cost\\n    , round(sum(fps.total_cost), 2) as total_population_cost\\n    , round(avg(fps.payer_coverage_pct), 2) as avg_coverage_pct\\nfrom main_marts.fct_patient_summary fps\\ngroup by fps.year_actual\\norder by fps.year_actual desc\"},{\"description\":\"Find patients with above-average encounters and conditions\",\"name\":\"High utilization patients\",\"sql\":\"with averages as (\\n    select\\n        avg(encounter_count) as avg_encounters\\n        , avg(condition_count) as avg_conditions\\n    from main_marts.fct_patient_summary\\n    where year_actual = 2024\\n)\\nselect\\n    fps.patient_id\\n    , fps.encounter_count\\n    , fps.condition_count\\n    , fps.total_cost\\n    , fps.unique_providers\\nfrom main_marts.fct_patient_summary fps\\ncross join averages a\\nwhere fps.year_actual = 2024\\n    and fps.encounter_count > a.avg_encounters * 2\\n    and fps.condition_count > a.avg_conditions\\norder by fps.total_cost desc\\nlimit 25\"}]"},{"key":"grain","value":"patient_id + year_actual"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Population health reporting\",\"Cost and utilization analysis\",\"Patient stratification\",\"Year-over-year trending\"]"}],"columns":[]},{"model":"fct_provider_metrics","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"fct_provider_scorecard","tags":["analytics","fact","marts"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Identify highest-volume providers for capacity planning\",\"name\":\"Top providers by patient volume\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\norder by total_encounters desc\\nlimit 15\"},{\"description\":\"Track provider productivity over time\",\"name\":\"Provider metrics trend by month\",\"sql\":\"select\\n    fpm.year_month_str\\n    , count(distinct fpm.provider_id) as active_providers\\n    , sum(fpm.encounter_count) as total_encounters\\n    , sum(fpm.unique_patient_count) as total_patients\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.avg_encounter_duration_minutes), 2) as avg_duration_minutes\\nfrom main_marts.fct_provider_metrics fpm\\nwhere fpm.year_actual >= 2023\\ngroup by fpm.year_month_str\\norder by fpm.year_month_str desc\"},{\"description\":\"Compare providers by cost and volume efficiency\",\"name\":\"Provider efficiency comparison\",\"sql\":\"select\\n    p.provider_id\\n    , p.name as provider_name\\n    , p.specialty\\n    , sum(fpm.encounter_count) as total_encounters\\n    , round(sum(fpm.total_cost), 2) as total_cost\\n    , round(avg(fpm.avg_cost_per_encounter), 2) as avg_cost_per_encounter\\n    , round(avg(fpm.encounters_per_patient_avg), 2) as avg_encounters_per_patient\\n    , round(sum(fpm.total_procedures) * 1.0 / sum(fpm.encounter_count), 2) as procedures_per_encounter\\nfrom main_marts.fct_provider_metrics fpm\\njoin main_marts.dim_providers p\\n    on fpm.provider_key = p.provider_key\\nwhere fpm.year_actual = 2024\\ngroup by 1, 2, 3\\nhaving sum(fpm.encounter_count) >= 10\\norder by avg_cost_per_encounter\"}]"},{"key":"grain","value":"provider_id + year_month_str"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider productivity reporting\",\"Workload balancing\",\"Cost efficiency analysis\",\"Capacity planning\"]"}],"columns":[]},{"model":"mart_date_state_grid","tags":["analytics","marts","reporting"],"meta":[{"key":"grain","value":"month_key x state"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Dense reporting grids\",\"Zero-activity gap detection\"]"}],"columns":[]},{"model":"mart_dq_summary","tags":["analytics","data_quality","marts"],"meta":[{"key":"grain","value":"entity_type"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Data quality dashboards\",\"Quarantine monitoring\",\"Data quality alerting\"]"}],"columns":[]},{"model":"v_encounter_summary","tags":["analytics","marts","view"],"meta":[],"columns":[]},{"model":"v_patient_current_conditions","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"Get all active conditions for a specific patient\",\"name\":\"Patient active conditions lookup\",\"sql\":\"select\\n    vpcc.patient_id\\n    , vpcc.condition_code\\n    , vpcc.condition_description\\n    , vpcc.first_diagnosis_date\\n    , vpcc.months_with_condition\\n    , vpcc.encounter_count\\nfrom main_marts.v_patient_current_conditions vpcc\\nwhere vpcc.patient_id = 'patient-uuid-here'\\norder by vpcc.first_diagnosis_date\"},{\"description\":\"Count of patients by active condition for disease registries\",\"name\":\"Active condition summary\",\"sql\":\"select\\n    vpcc.condition_code\\n    , vpcc.condition_description\\n    , count(distinct vpcc.patient_id) as active_patient_count\\n    , round(avg(vpcc.months_with_condition), 1) as avg_duration_months\\n    , round(avg(vpcc.encounter_count), 1) as avg_encounters\\nfrom main_marts.v_patient_current_conditions vpcc\\ngroup by 1, 2\\norder by active_patient_count desc\\nlimit 20\"}]"},{"key":"grain","value":"patient_id + condition_code"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Patient panel displays\",\"Active condition registries\",\"Care gap dashboards\"]"}],"columns":[]},{"model":"v_provider_active_patients","tags":["analytics","marts","view"],"meta":[{"key":"example_queries","value":"[{\"description\":\"View provider workload by active patient and condition counts\",\"name\":\"Provider panel sizes\",\"sql\":\"select\\n    vpap.provider_id\\n    , vpap.provider_name\\n    , vpap.active_patient_count\\n    , vpap.total_active_conditions\\n    , vpap.avg_conditions_per_patient\\nfrom main_marts.v_provider_active_patients vpap\\nwhere vpap.active_patient_count > 0\\norder by vpap.active_patient_count desc\\nlimit 25\"},{\"description\":\"Analyze distribution of patient panels across providers\",\"name\":\"Provider workload distribution\",\"sql\":\"select\\n    case\\n        when vpap.active_patient_count = 0 then '0'\\n        when vpap.active_patient_count \u003c= 10 then '1-10'\\n        when vpap.active_patient_count \u003c= 50 then '11-50'\\n        when vpap.active_patient_count \u003c= 100 then '51-100'\\n        else '100+'\\n    end as panel_size_bucket\\n    , count(*) as provider_count\\n    , sum(vpap.active_patient_count) as total_patients\\nfrom main_marts.v_provider_active_patients vpap\\ngroup by 1\\norder by 1\"}]"},{"key":"grain","value":"provider_id"},{"key":"owner","value":"healthcare_analytics"},{"key":"uses","value":"[\"Provider panel management\",\"Workload balancing\",\"Capacity planning\"]"}],"columns":[]}]},"macro_lens":{"macros":[{"name":"quarantine_filter","package":"healthcare_analytics","path":"macros/data_quality/quarantine_filter.sql","diff":{"lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"removed","text":"    where {{ field_name }} is true","emphasis":[27,29]},{"kind":"added","text":"    where {{ field_name }} = true","emphasis":[27,28]},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}]},"body_lines":[{"kind":"context","text":"{% macro quarantine_filter(enabled=true, field_name='is_dq_valid') %}","emphasis":null},{"kind":"context","text":"    {% if enabled %}","emphasis":null},{"kind":"context","text":"    where {{ field_name }} = true","emphasis":null},{"kind":"context","text":"    {% endif %}","emphasis":null},{"kind":"context","text":"{% endmacro %}","emphasis":null}],"impacted_count":3,"tree":[{"kind":"dir","label":"models","depth":0,"model_id":""},{"kind":"dir","label":"intermediate","depth":1,"model_id":""},{"kind":"dir","label":"healthcare","depth":2,"model_id":""},{"kind":"model","label":"int_encounters__enriched","depth":3,"model_id":"model.healthcare_analytics.int_encounters__enriched"},{"kind":"dir","label":"marts","depth":1,"model_id":""},{"kind":"dir","label":"core","depth":2,"model_id":""},{"kind":"model","label":"fct_clinical_events","depth":3,"model_id":"model.healthcare_analytics.fct_clinical_events"},{"kind":"model","label":"fct_encounters","depth":3,"model_id":"model.healthcare_analytics.fct_encounters"}],"impacted_models":[{"model_id":"model.healthcare_analytics.fct_clinical_events","name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='clinical_event_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with valid_encounters as (","emphasis":null},{"kind":"context","text":"    select encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"conditions as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        condition_id as event_id","emphasis":null},{"kind":"context","text":"        , 'CONDITION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , cond.encounter_id","emphasis":null},{"kind":"context","text":"        , condition_start_date as event_start_date","emphasis":null},{"kind":"context","text":"        , condition_end_date as event_end_date","emphasis":null},{"kind":"context","text":"        , condition_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , condition_description as description","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_code","emphasis":null},{"kind":"context","text":"        , cast(null as varchar) as reason_description","emphasis":null},{"kind":"context","text":"        , cast(null as decimal(18, 2)) as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__conditions') }} as cond","emphasis":null},{"kind":"context","text":"    where cond.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"medications as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        medication_id as event_id","emphasis":null},{"kind":"context","text":"        , 'MEDICATION' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , med.encounter_id","emphasis":null},{"kind":"context","text":"        , cast(medication_start_at as date) as event_start_date","emphasis":null},{"kind":"context","text":"        , cast(medication_end_at as date) as event_end_date","emphasis":null},{"kind":"context","text":"        , medication_code as code","emphasis":null},{"kind":"context","text":"        , 'RXNORM' as code_system","emphasis":null},{"kind":"context","text":"        , medication_description as description","emphasis":null},{"kind":"context","text":"        , reason_code","emphasis":null},{"kind":"context","text":"        , reason_description","emphasis":null},{"kind":"context","text":"        , total_cost as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__medications') }} as med","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"    and med.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"procedures as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        procedure_id as event_id","emphasis":null},{"kind":"context","text":"        , 'PROCEDURE' as event_type","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , proc.encounter_id","emphasis":null},{"kind":"context","text":"        , cast(procedure_at as date) as event_start_date","emphasis":null},{"kind":"context","text":"        , cast(null as date) as event_end_date","emphasis":null},{"kind":"context","text":"        , procedure_code as code","emphasis":null},{"kind":"context","text":"        , 'SNOMED-CT' as code_system","emphasis":null},{"kind":"context","text":"        , procedure_description as description","emphasis":null},{"kind":"context","text":"        , reason_code","emphasis":null},{"kind":"context","text":"        , reason_description","emphasis":null},{"kind":"context","text":"        , base_cost as event_cost","emphasis":null},{"kind":"context","text":"    from {{ ref('stg_synthea__procedures') }} as proc","emphasis":null},{"kind":"context","text":"    where proc.encounter_id in (select encounter_id from valid_encounters)","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"all_events as (","emphasis":null},{"kind":"context","text":"    select * from conditions","emphasis":null},{"kind":"context","text":"    union all","emphasis":null},{"kind":"context","text":"    select * from medications","emphasis":null},{"kind":"context","text":"    union all","emphasis":null},{"kind":"context","text":"    select * from procedures","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"patients as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_patients') }}","emphasis":null},{"kind":"context","text":"    where is_current = true","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , date_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"encounters as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        encounter_key","emphasis":null},{"kind":"context","text":"        , encounter_id","emphasis":null},{"kind":"context","text":"    from {{ ref('fct_encounters') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        row_number() over (order by e.event_id) as clinical_event_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- event identifiers","emphasis":null},{"kind":"context","text":"        , e.event_type","emphasis":null},{"kind":"context","text":"        , e.event_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , coalesce(p.patient_key, -1) as patient_key","emphasis":null},{"kind":"context","text":"        , e.patient_id","emphasis":null},{"kind":"context","text":"        , coalesce(enc.encounter_key, -1) as encounter_key","emphasis":null},{"kind":"context","text":"        , e.encounter_id","emphasis":null},{"kind":"context","text":"        , coalesce(d.date_key, -1) as event_date_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dates","emphasis":null},{"kind":"context","text":"        , e.event_start_date","emphasis":null},{"kind":"context","text":"        , e.event_end_date","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- code information","emphasis":null},{"kind":"context","text":"        , e.code","emphasis":null},{"kind":"context","text":"        , e.code_system","emphasis":null},{"kind":"context","text":"        , e.description","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- reason","emphasis":null},{"kind":"context","text":"        , e.reason_code","emphasis":null},{"kind":"context","text":"        , e.reason_description","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- cost","emphasis":null},{"kind":"context","text":"        , e.event_cost","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- metadata","emphasis":null},{"kind":"context","text":"        , current_timestamp as _loaded_at","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"    from all_events as e","emphasis":null},{"kind":"context","text":"    left join patients as p on e.patient_id = p.patient_id","emphasis":null},{"kind":"context","text":"    left join encounters as enc on e.encounter_id = enc.encounter_id","emphasis":null},{"kind":"context","text":"    left join dim_date as d on e.event_start_date = d.date_actual","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select * from final","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"-- cute-dbt fixture: synthetic body mod","emphasis":null}],"call_sites":[{"line":11,"text":"{{ quarantine_filter() }}"},{"line":47,"text":"{{ quarantine_filter() }}"}],"call_site_total":2,"call_site_cap":3,"inline_body":true},{"model_id":"model.healthcare_analytics.fct_encounters","name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","sql_lines":[{"kind":"context","text":"{{","emphasis":null},{"kind":"context","text":"    config(","emphasis":null},{"kind":"context","text":"        materialized='table',","emphasis":null},{"kind":"context","text":"        unique_key='encounter_key'","emphasis":null},{"kind":"context","text":"    )","emphasis":null},{"kind":"context","text":"}}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"with encounters as (","emphasis":null},{"kind":"context","text":"    select * from {{ ref('stg_synthea__encounters') }}","emphasis":null},{"kind":"context","text":"    {{ quarantine_filter() }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"patients as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        patient_key","emphasis":null},{"kind":"context","text":"        , patient_id","emphasis":null},{"kind":"context","text":"        , birth_date","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_patients') }}","emphasis":null},{"kind":"context","text":"    where is_current = true","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"providers as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        provider_key","emphasis":null},{"kind":"context","text":"        , provider_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_providers') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"organizations as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        organization_key","emphasis":null},{"kind":"context","text":"        , organization_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_organizations') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"payers as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        payer_key","emphasis":null},{"kind":"context","text":"        , payer_id","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_payers') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"dim_date as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        date_key","emphasis":null},{"kind":"context","text":"        , date_actual","emphasis":null},{"kind":"context","text":"    from {{ ref('dim_date') }}","emphasis":null},{"kind":"context","text":"),","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"final as (","emphasis":null},{"kind":"context","text":"    select","emphasis":null},{"kind":"context","text":"        -- surrogate key","emphasis":null},{"kind":"context","text":"        row_number() over (order by e.encounter_id) as encounter_key","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- natural key","emphasis":null},{"kind":"context","text":"        , e.encounter_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- dimension keys","emphasis":null},{"kind":"context","text":"        , coalesce(p.patient_key, -1) as patient_key","emphasis":null},{"kind":"context","text":"        , e.patient_id","emphasis":null},{"kind":"context","text":"        , coalesce(pr.provider_key, -1) as provider_key","emphasis":null},{"kind":"context","text":"        , e.provider_id","emphasis":null},{"kind":"context","text":"        , coalesce(o.organization_key, -1) as organization_key","emphasis":null},{"kind":"context","text":"        , e.organization_id","emphasis":null},{"kind":"context","text":"        , coalesce(py.payer_key, -1) as payer_key","emphasis":null},{"kind":"context","text":"        , e.payer_id","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"        -- date keys","emphasis":null},{"kind":"context","text":"        , coalesce(ds.date_key, -1) as encounter_start_date_key","emphasis":null},{"kind":"context","text":"        , coalesce(de.date_key, -1) as 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
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diff --git a/examples/explore-macro/tests.html b/examples/explore-macro/tests.html
index e563854..21dfae6 100644
--- a/examples/explore-macro/tests.html
+++ b/examples/explore-macro/tests.html
@@ -1147,7 +1147,7 @@ <h2>v_provider_active_patients</h2>
 
 
 
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condition_description\n)","conditions_source":"conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n)","deduped":"deduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n)","final":"final as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n)","unknown_member":"unknown_member as (\n    select\n      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last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":105,"col":1,"byte":2681},"end":{"line":105,"col":20,"byte":2700}},"condition_aggregates":{"start":{"line":18,"col":1,"byte":420},"end":{"line":28,"col":2,"byte":854}},"conditions_source":{"start":{"line":5,"col":6,"byte":9},"end":{"line":16,"col":2,"byte":417}},"deduped":{"start":{"line":31,"col":1,"byte":917},"end":{"line":44,"col":2,"byte":1295}},"final":{"start":{"line":79,"col":1,"byte":2118},"end":{"line":103,"col":2,"byte":2679}},"sequenced":{"start":{"line":46,"col":1,"byte":1298},"end":{"line":65,"col":2,"byte":1735}},"unknown_member":{"start":{"line":68,"col":1,"byte":1795},"end":{"line":77,"col":2,"byte":2115}}},"column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":2688},"end":{"line":105,"col":9,"byte":2689}},"condition_aggregates\u001fcondition_code":{"start":{"line":20,"col":9,"byte":465},"end":{"line":20,"col":23,"byte":479}},"condition_aggregates\u001fcondition_description":{"start":{"line":21,"col":11,"byte":490},"end":{"line":21,"col":32,"byte":511}},"condition_aggregates\u001fencounter_count":{"start":{"line":25,"col":11,"byte":727},"end":{"line":25,"col":58,"byte":774}},"condition_aggregates\u001ffirst_occurrence_date":{"start":{"line":22,"col":11,"byte":522},"end":{"line":22,"col":61,"byte":572}},"condition_aggregates\u001flast_occurrence_date":{"start":{"line":23,"col":11,"byte":583},"end":{"line":23,"col":90,"byte":662}},"condition_aggregates\u001fpatient_count":{"start":{"line":24,"col":11,"byte":673},"end":{"line":24,"col":54,"byte":716}},"conditions_source\u001fcondition_code":{"start":{"line":8,"col":14,"byte":139},"end":{"line":8,"col":58,"byte":183}},"conditions_source\u001fcondition_description":{"start":{"line":9,"col":11,"byte":194},"end":{"line":9,"col":32,"byte":215}},"conditions_source\u001fcondition_end_date":{"start":{"line":13,"col":11,"byte":301},"end":{"line":13,"col":29,"byte":319}},"conditions_source\u001fcondition_start_date":{"start":{"line":12,"col":11,"byte":270},"end":{"line":12,"col":31,"byte":290}},"conditions_source\u001fencounter_id":{"start":{"line":11,"col":11,"byte":247},"end":{"line":11,"col":23,"byte":259}},"conditions_source\u001fpatient_id":{"start":{"line":10,"col":11,"byte":226},"end":{"line":10,"col":21,"byte":236}},"deduped\u001fcondition_code":{"start":{"line":33,"col":9,"byte":949},"end":{"line":33,"col":23,"byte":963}},"deduped\u001fcondition_description":{"start":{"line":34,"col":11,"byte":974},"end":{"line":34,"col":32,"byte":995}},"deduped\u001fencounter_count":{"start":{"line":38,"col":11,"byte":1093},"end":{"line":38,"col":26,"byte":1108}},"deduped\u001ffirst_occurrence_date":{"start":{"line":35,"col":11,"byte":1006},"end":{"line":35,"col":32,"byte":1027}},"deduped\u001flast_occurrence_date":{"start":{"line":36,"col":11,"byte":1038},"end":{"line":36,"col":31,"byte":1058}},"deduped\u001fpatient_count":{"start":{"line":37,"col":11,"byte":1069},"end":{"line":37,"col":24,"byte":1082}},"deduped\u001frn":{"start":{"line":39,"col":11,"byte":1119},"end":{"line":42,"col":16,"byte":1263}},"sequenced\u001fcondition_code":{"start":{"line":52,"col":11,"byte":1496},"end":{"line":52,"col":25,"byte":1510}},"sequenced\u001fcondition_description":{"start":{"line":55,"col":11,"byte":1544},"end":{"line":55,"col":32,"byte":1565}},"sequenced\u001fcondition_key":{"start":{"line":49,"col":9,"byte":1401},"end":{"line":49,"col":69,"byte":1461}},"sequenced\u001fencounter_count":{"start":{"line":61,"col":11,"byte":1683},"end":{"line":61,"col":26,"byte":1698}},"sequenced\u001ffirst_occurrence_date":{"start":{"line":56,"col":11,"byte":1576},"end":{"line":56,"col":32,"byte":1597}},"sequenced\u001flast_occurrence_date":{"start":{"line":57,"col":11,"byte":1608},"end":{"line":57,"col":31,"byte":1628}},"sequenced\u001fpatient_count":{"start":{"line":60,"col":11,"byte":1659},"end":{"line":60,"col":24,"byte":1672}},"unknown_member\u001fcondition_code":{"start":{"line":71,"col":11,"byte":1864},"end":{"line":71,"col":38,"byte":1891}},"unknown_member\u001fcondition_description":{"start":{"line":72,"col":11,"byte":1902},"end":{"line":72,"col":55,"byte":1946}},"unknown_member\u001fcondition_key":{"start":{"line":70,"col":10,"byte":1835},"end":{"line":70,"col":28,"byte":1853}},"unknown_member\u001fencounter_count":{"start":{"line":76,"col":11,"byte":2093},"end":{"line":76,"col":31,"byte":2113}},"unknown_member\u001ffirst_occurrence_date":{"start":{"line":73,"col":16,"byte":1962},"end":{"line":73,"col":54,"byte":2000}},"unknown_member\u001flast_occurrence_date":{"start":{"line":74,"col":16,"byte":2016},"end":{"line":74,"col":53,"byte":2053}},"unknown_member\u001fpatient_count":{"start":{"line":75,"col":11,"byte":2064},"end":{"line":75,"col":29,"byte":2082}}},"raw_node_spans":{"condition_aggregates":{"start":{"line":18,"col":1,"byte":932},"end":{"line":28,"col":2,"byte":1366}},"conditions_source":{"start":{"line":5,"col":6,"byte":534},"end":{"line":16,"col":2,"byte":929}},"deduped":{"start":{"line":31,"col":1,"byte":1429},"end":{"line":44,"col":2,"byte":1807}},"final":{"start":{"line":79,"col":1,"byte":2630},"end":{"line":103,"col":2,"byte":3191}},"sequenced":{"start":{"line":46,"col":1,"byte":1810},"end":{"line":65,"col":2,"byte":2247}},"unknown_member":{"start":{"line":68,"col":1,"byte":2307},"end":{"line":77,"col":2,"byte":2627}}},"raw_column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":3200},"end":{"line":105,"col":9,"byte":3201}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. 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as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}},"raw_node_spans":{"final":{"start":{"line":85,"col":1,"byte":2515},"end":{"line":89,"col":2,"byte":2602}},"sequenced":{"start":{"line":7,"col":1,"byte":158},"end":{"line":54,"col":2,"byte":1280}},"source":{"start":{"line":3,"col":6,"byte":91},"end":{"line":5,"col":2,"byte":155}},"unknown_member":{"start":{"line":57,"col":1,"byte":1340},"end":{"line":83,"col":2,"byte":2512}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__payers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"phone"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"phone"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_covered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_covered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_uncovered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_uncovered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"revenue"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"revenue"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"unique_customers"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"unique_customers"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"average_quality_of_life_score"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"average_quality_of_life_score"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"member_months"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"member_months"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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 -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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_loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}},"raw_node_spans":{"dim_date":{"start":{"line":38,"col":1,"byte":685},"end":{"line":43,"col":2,"byte":781}},"encounters":{"start":{"line":3,"col":6,"byte":95},"end":{"line":6,"col":2,"byte":197}},"final":{"start":{"line":45,"col":1,"byte":784},"end":{"line":103,"col":2,"byte":2732}},"organizations":{"start":{"line":24,"col":1,"byte":463},"end":{"line":29,"col":2,"byte":585}},"patients":{"start":{"line":8,"col":1,"byte":200},"end":{"line":15,"col":2,"byte":351}},"payers":{"start":{"line":31,"col":1,"byte":588},"end":{"line":36,"col":2,"byte":682}},"providers":{"start":{"line":17,"col":1,"byte":354},"end":{"line":22,"col":2,"byte":460}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":94,"col":32,"byte":2276},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter 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}}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) 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 , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)"}},"column":"total_event_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_condition_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_condition_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_medication_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_medication_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedure_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedure_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}},"raw_node_spans":{"chronic_count":{"start":{"line":56,"col":1,"byte":2545},"end":{"line":61,"col":2,"byte":2707}},"chronic_flags":{"start":{"line":33,"col":1,"byte":1731},"end":{"line":54,"col":2,"byte":2542}},"condition_stats":{"start":{"line":22,"col":1,"byte":1383},"end":{"line":31,"col":2,"byte":1728}},"conditions":{"start":{"line":12,"col":1,"byte":1170},"end":{"line":20,"col":2,"byte":1380}},"final":{"start":{"line":63,"col":1,"byte":2710},"end":{"line":78,"col":2,"byte":3483}},"patients":{"start":{"line":5,"col":6,"byte":1059},"end":{"line":10,"col":2,"byte":1167}}},"raw_column_spans":{"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":1286},"end":{"line":17,"col":25,"byte":1300}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final 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month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}},"raw_node_spans":{"combined_metrics":{"start":{"line":29,"col":1,"byte":1516},"end":{"line":53,"col":2,"byte":2430}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":103},"end":{"line":14,"col":2,"byte":797}},"medication_metrics":{"start":{"line":16,"col":1,"byte":800},"end":{"line":27,"col":2,"byte":1513}}},"raw_column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. 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valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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select)"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"active_patient_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"active_patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_active_conditions"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_active_conditions"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_conditions_per_patient"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_conditions_per_patient"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Provider Active Patients View\n\n    Purpose: Active patient count per provider for panel management\n    Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. 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One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
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patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n  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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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 -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. 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condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , 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condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}},"raw_node_spans":{"dim_date":{"start":{"line":38,"col":1,"byte":685},"end":{"line":43,"col":2,"byte":781}},"encounters":{"start":{"line":3,"col":6,"byte":95},"end":{"line":6,"col":2,"byte":197}},"final":{"start":{"line":45,"col":1,"byte":784},"end":{"line":103,"col":2,"byte":2732}},"organizations":{"start":{"line":24,"col":1,"byte":463},"end":{"line":29,"col":2,"byte":585}},"patients":{"start":{"line":8,"col":1,"byte":200},"end":{"line":15,"col":2,"byte":351}},"payers":{"start":{"line":31,"col":1,"byte":588},"end":{"line":36,"col":2,"byte":682}},"providers":{"start":{"line":17,"col":1,"byte":354},"end":{"line":22,"col":2,"byte":460}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":94,"col":32,"byte":2276},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}},"raw_dag":{"nodes":[{"id":"dim_date","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"encounters","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"final","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"organizations","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"patients","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"payers","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"providers","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"dim_date":"dim_date","encounters":"encounters","final":"final","organizations":"organizations","patients":"patients","payers":"payers","providers":"providers"}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. 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 , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , 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costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":141,"col":8,"byte":3661},"end":{"line":141,"col":9,"byte":3662}},"conditions\u001fcondition_code":{"start":{"line":46,"col":11,"byte":892},"end":{"line":46,"col":25,"byte":906}},"conditions\u001fcondition_year":{"start":{"line":47,"col":29,"byte":935},"end":{"line":47,"col":68,"byte":974}},"conditions\u001fpatient_id":{"start":{"line":45,"col":9,"byte":871},"end":{"line":45,"col":19,"byte":881}},"conditions_per_patient_year\u001fcondition_count":{"start":{"line":68,"col":11,"byte":1595},"end":{"line":68,"col":60,"byte":1644}},"conditions_per_patient_year\u001fpatient_id":{"start":{"line":66,"col":9,"byte":1534},"end":{"line":66,"col":19,"byte":1544}},"conditions_per_patient_year\u001fyear_actual":{"start":{"line":67,"col":11,"byte":1555},"end":{"line":67,"col":40,"byte":1584}},"dim_date\u001fdate_key":{"start":{"line":30,"col":9,"byte":584},"end":{"line":30,"col":17,"byte":592}},"dim_date\u001fyear_actual":{"start":{"line":31,"col":11,"byte":603},"end":{"line":31,"col":22,"byte":614}},"encounters\u001fencounter_class":{"start":{"line":18,"col":11,"byte":306},"end":{"line":18,"col":26,"byte":321}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_at":{"start":{"line":23,"col":11,"byte":452},"end":{"line":23,"col":29,"byte":470}},"encounters\u001fencounter_start_date_key":{"start":{"line":17,"col":11,"byte":271},"end":{"line":17,"col":35,"byte":295}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":22,"col":11,"byte":417},"end":{"line":22,"col":35,"byte":441}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpatient_responsibility":{"start":{"line":21,"col":11,"byte":384},"end":{"line":21,"col":33,"byte":406}},"encounters\u001fpayer_coverage":{"start":{"line":20,"col":11,"byte":359},"end":{"line":20,"col":25,"byte":373}},"encounters\u001fpayer_id":{"start":{"line":16,"col":11,"byte":252},"end":{"line":16,"col":19,"byte":260}},"encounters\u001fpayer_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":20,"byte":241}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":19,"col":11,"byte":332},"end":{"line":19,"col":27,"byte":348}},"encounters_with_year\u001f*":{"start":{"line":54,"col":9,"byte":1132},"end":{"line":54,"col":12,"byte":1135}},"encounters_with_year\u001fprocedure_count":{"start":{"line":56,"col":11,"byte":1170},"end":{"line":56,"col":61,"byte":1220}},"encounters_with_year\u001fyear_actual":{"start":{"line":55,"col":11,"byte":1146},"end":{"line":55,"col":24,"byte":1159}},"final\u001f_loaded_at":{"start":{"line":136,"col":11,"byte":3593},"end":{"line":136,"col":42,"byte":3624}},"final\u001fcondition_count":{"start":{"line":120,"col":11,"byte":3113},"end":{"line":120,"col":26,"byte":3128}},"final\u001fencounter_count":{"start":{"line":118,"col":11,"byte":3061},"end":{"line":118,"col":26,"byte":3076}},"final\u001fpatient_age_at_year_end":{"start":{"line":115,"col":11,"byte":2998},"end":{"line":115,"col":34,"byte":3021}},"final\u001fpatient_id":{"start":{"line":109,"col":11,"byte":2904},"end":{"line":109,"col":21,"byte":2914}},"final\u001fpatient_key":{"start":{"line":108,"col":11,"byte":2882},"end":{"line":108,"col":22,"byte":2893}},"final\u001fpatient_responsibility":{"start":{"line":127,"col":11,"byte":3329},"end":{"line":127,"col":72,"byte":3390}},"final\u001fpatient_summary_key":{"start":{"line":105,"col":9,"byte":2750},"end":{"line":105,"col":103,"byte":2844}},"final\u001fpayer_coverage":{"start":{"line":126,"col":11,"byte":3273},"end":{"line":126,"col":56,"byte":3318}},"final\u001fpayer_coverage_pct":{"start":{"line":130,"col":11,"byte":3430},"end":{"line":133,"col":34,"byte":3561}},"final\u001fprocedure_count":{"start":{"line":119,"col":11,"byte":3087},"end":{"line":119,"col":26,"byte":3102}},"final\u001ftotal_cost":{"start":{"line":125,"col":11,"byte":3225},"end":{"line":125,"col":48,"byte":3262}},"final\u001funique_organizations":{"start":{"line":122,"col":11,"byte":3166},"end":{"line":122,"col":31,"byte":3186}},"final\u001funique_providers":{"start":{"line":121,"col":11,"byte":3139},"end":{"line":121,"col":27,"byte":3155}},"final\u001fyear_actual":{"start":{"line":112,"col":11,"byte":2948},"end":{"line":112,"col":22,"b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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != 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     end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != 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   , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / 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tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__medications',\n    description='Medications quarantined due to data quality violations'\n) }}","tests":[],"is_recursive":false},{"name":"int_encounters__enriched","path":"models/intermediate/healthcare/int_encounters__enriched.sql","description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. 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select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"medication_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"medication_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"procedure_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"procedure_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_condition_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_condition_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_medication_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_medication_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedure_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedure_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}},"raw_dag":{"nodes":[{"id":"(final select)","role":"terminal","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"(final select)":"(final select)"}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final 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}}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_total_conditions.9dcd52a52e"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"patients"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"chronic_flags"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"chronic_flags"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"chronic_count"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"first_condition_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_condition_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"last_condition_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"last_condition_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body 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Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not 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tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final 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100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) 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count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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One row per condition instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__conditions.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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(cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 'valid_medication_dates' else null end\n        , \n        \n        case when not (cast(medication_start_at as date) <= current_date) then 'no_future_medication_dates' else null end\n        , \n        \n        case when not (medication_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        , \n        \n        case when not (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') then 'end_after_1900_if_present' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from with_surrogate_key\n\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as 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encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_start_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n),\n\nwith_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (medication_end_at is null or medication_end_at >= medication_start_at) as valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 <script>
 
 /* cute-dbt shared appearance engine v1 (cute-dbt#242)
diff --git a/examples/explore-seed/tests.html b/examples/explore-seed/tests.html
index 4d2dfaf..8f57b6c 100644
--- a/examples/explore-seed/tests.html
+++ b/examples/explore-seed/tests.html
@@ -829,7 +829,7 @@ <h2>stg_states</h2>
 
 
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"stg_states","path":"models/staging/stg_states.sql","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"stg_states.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select state_code, state_name, region from source\n","source":"source as (select * from raw_state_codes)"},"code_map":{"compiled":"with source as (select * from raw_state_codes)\nselect state_code, state_name, region from source\n","node_spans":{"(final select)":{"start":{"line":2,"col":1,"byte":47},"end":{"line":3,"col":1,"byte":97}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":1,"col":47,"byte":46}}},"column_spans":{"(final select)\u001fregion":{"start":{"line":2,"col":32,"byte":78},"end":{"line":2,"col":38,"byte":84}},"(final select)\u001fstate_code":{"start":{"line":2,"col":8,"byte":54},"end":{"line":2,"col":18,"byte":64}},"(final select)\u001fstate_name":{"start":{"line":2,"col":20,"byte":66},"end":{"line":2,"col":30,"byte":76}},"source\u001f*":{"start":{"line":1,"col":24,"byte":23},"end":{"line":1,"col":25,"byte":24}}},"raw_node_spans":{"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":65}}},"raw_column_spans":{"(final select)\u001fregion":{"start":{"line":4,"col":32,"byte":97},"end":{"line":4,"col":38,"byte":103}},"(final select)\u001fstate_code":{"start":{"line":4,"col":8,"byte":73},"end":{"line":4,"col":18,"byte":83}},"(final select)\u001fstate_name":{"start":{"line":4,"col":20,"byte":85},"end":{"line":4,"col":30,"byte":95}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"raw_state_codes"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state_code"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"region"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"region"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"with source as (\n    select * from {{ ref('raw_state_codes') }}\n)\nselect state_code, state_name, region\nfrom source\n","tests":[{"id":"unit_test.analytics.stg_states.test_stg_states_passthrough","name":"test_stg_states_passthrough","target_model":"stg_states","changed":false,"defined_in":"models/staging/unit_tests.yml","given":[{"input":"ref('raw_state_codes')","bound_to_node":"source","table":{"columns":["region","state_code","state_name"],"rows":[{"cells":[{"display":"Northeast","key":{"t":"str","v":"Northeast"}},{"display":"CT","key":{"t":"str","v":"CT"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]}]},"rows":[{"region":"Northeast","state_code":"CT","state_name":"Connecticut"}],"format":"dict"}],"expected":{"table":{"columns":["region","state_code","state_name"],"rows":[{"cells":[{"display":"Northeast","key":{"t":"str","v":"Northeast"}},{"display":"CT","key":{"t":"str","v":"CT"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]}]},"rows":[{"region":"Northeast","state_code":"CT","state_name":"Connecticut"}],"format":"dict"}}],"is_recursive":false}]}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"stg_states","path":"models/staging/stg_states.sql","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"stg_states.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select state_code, state_name, region from source\n","source":"source as (select * from raw_state_codes)"},"code_map":{"compiled":"with source as (select * from raw_state_codes)\nselect state_code, state_name, region from source\n","node_spans":{"(final select)":{"start":{"line":2,"col":1,"byte":47},"end":{"line":3,"col":1,"byte":97}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":1,"col":47,"byte":46}}},"column_spans":{"(final select)\u001fregion":{"start":{"line":2,"col":32,"byte":78},"end":{"line":2,"col":38,"byte":84}},"(final select)\u001fstate_code":{"start":{"line":2,"col":8,"byte":54},"end":{"line":2,"col":18,"byte":64}},"(final select)\u001fstate_name":{"start":{"line":2,"col":20,"byte":66},"end":{"line":2,"col":30,"byte":76}},"source\u001f*":{"start":{"line":1,"col":24,"byte":23},"end":{"line":1,"col":25,"byte":24}}},"raw_node_spans":{"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":65}}},"raw_column_spans":{"(final select)\u001fregion":{"start":{"line":4,"col":32,"byte":97},"end":{"line":4,"col":38,"byte":103}},"(final select)\u001fstate_code":{"start":{"line":4,"col":8,"byte":73},"end":{"line":4,"col":18,"byte":83}},"(final select)\u001fstate_name":{"start":{"line":4,"col":20,"byte":85},"end":{"line":4,"col":30,"byte":95}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}},"raw_dag":{"nodes":[{"id":"source","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"source":"source"}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"raw_state_codes"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state_code"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"region"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"region"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"with source as (\n    select * from {{ ref('raw_state_codes') }}\n)\nselect state_code, state_name, region\nfrom source\n","tests":[{"id":"unit_test.analytics.stg_states.test_stg_states_passthrough","name":"test_stg_states_passthrough","target_model":"stg_states","changed":false,"defined_in":"models/staging/unit_tests.yml","given":[{"input":"ref('raw_state_codes')","bound_to_node":"source","table":{"columns":["region","state_code","state_name"],"rows":[{"cells":[{"display":"Northeast","key":{"t":"str","v":"Northeast"}},{"display":"CT","key":{"t":"str","v":"CT"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]}]},"rows":[{"region":"Northeast","state_code":"CT","state_name":"Connecticut"}],"format":"dict"}],"expected":{"table":{"columns":["region","state_code","state_name"],"rows":[{"cells":[{"display":"Northeast","key":{"t":"str","v":"Northeast"}},{"display":"CT","key":{"t":"str","v":"CT"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]}]},"rows":[{"region":"Northeast","state_code":"CT","state_name":"Connecticut"}],"format":"dict"}}],"is_recursive":false}]}</script>
 <script>
 
 /* cute-dbt shared appearance engine v1 (cute-dbt#242)
diff --git a/examples/explore/tests.html b/examples/explore/tests.html
index 3731706..6d715ce 100644
--- a/examples/explore/tests.html
+++ b/examples/explore/tests.html
@@ -1144,7 +1144,7 @@ <h2>v_provider_active_patients</h2>
 
 
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"dim_conditions","path":"models/marts/analytics/dim_conditions.sql","description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","dag":{"nodes":[{"id":"conditions_source","role":"import"},{"id":"condition_aggregates","role":"transform"},{"id":"deduped","role":"transform"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":105,"col":1,"byte":2681},"end":{"line":105,"col":20,"byte":2700}},"condition_aggregates":{"start":{"line":18,"col":1,"byte":420},"end":{"line":28,"col":2,"byte":854}},"conditions_source":{"start":{"line":5,"col":6,"byte":9},"end":{"line":16,"col":2,"byte":417}},"deduped":{"start":{"line":31,"col":1,"byte":917},"end":{"line":44,"col":2,"byte":1295}},"final":{"start":{"line":79,"col":1,"byte":2118},"end":{"line":103,"col":2,"byte":2679}},"sequenced":{"start":{"line":46,"col":1,"byte":1298},"end":{"line":65,"col":2,"byte":1735}},"unknown_member":{"start":{"line":68,"col":1,"byte":1795},"end":{"line":77,"col":2,"byte":2115}}},"column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":2688},"end":{"line":105,"col":9,"byte":2689}},"condition_aggregates\u001fcondition_code":{"start":{"line":20,"col":9,"byte":465},"end":{"line":20,"col":23,"byte":479}},"condition_aggregates\u001fcondition_description":{"start":{"line":21,"col":11,"byte":490},"end":{"line":21,"col":32,"byte":511}},"condition_aggregates\u001fencounter_count":{"start":{"line":25,"col":11,"byte":727},"end":{"line":25,"col":58,"byte":774}},"condition_aggregates\u001ffirst_occurrence_date":{"start":{"line":22,"col":11,"byte":522},"end":{"line":22,"col":61,"byte":572}},"condition_aggregates\u001flast_occurrence_date":{"start":{"line":23,"col":11,"byte":583},"end":{"line":23,"col":90,"byte":662}},"condition_aggregates\u001fpatient_count":{"start":{"line":24,"col":11,"byte":673},"end":{"line":24,"col":54,"byte":716}},"conditions_source\u001fcondition_code":{"start":{"line":8,"col":14,"byte":139},"end":{"line":8,"col":58,"byte":183}},"conditions_source\u001fcondition_description":{"start":{"line":9,"col":11,"byte":194},"end":{"line":9,"col":32,"byte":215}},"conditions_source\u001fcondition_end_date":{"start":{"line":13,"col":11,"byte":301},"end":{"line":13,"col":29,"byte":319}},"conditions_source\u001fcondition_start_date":{"start":{"line":12,"col":11,"byte":270},"end":{"line":12,"col":31,"byte":290}},"conditions_source\u001fencounter_id":{"start":{"line":11,"col":11,"byte":247},"end":{"line":11,"col":23,"byte":259}},"conditions_source\u001fpatient_id":{"start":{"line":10,"col":11,"byte":226},"end":{"line":10,"col":21,"byte":236}},"deduped\u001fcondition_code":{"start":{"line":33,"col":9,"byte":949},"end":{"line":33,"col":23,"byte":963}},"deduped\u001fcondition_description":{"start":{"line":34,"col":11,"byte":974},"end":{"line":34,"col":32,"byte":995}},"deduped\u001fencounter_count":{"start":{"line":38,"col":11,"byte":1093},"end":{"line":38,"col":26,"byte":1108}},"deduped\u001ffirst_occurrence_date":{"start":{"line":35,"col":11,"byte":1006},"end":{"line":35,"col":32,"byte":1027}},"deduped\u001flast_occurrence_date":{"start":{"line":36,"col":11,"byte":1038},"end":{"line":36,"col":31,"byte":1058}},"deduped\u001fpatient_count":{"start":{"line":37,"col":11,"byte":1069},"end":{"line":37,"col":24,"byte":1082}},"deduped\u001frn":{"start":{"line":39,"col":11,"byte":1119},"end":{"line":42,"col":16,"byte":1263}},"sequenced\u001fcondition_code":{"start":{"line":52,"col":11,"byte":1496},"end":{"line":52,"col":25,"byte":1510}},"sequenced\u001fcondition_description":{"start":{"line":55,"col":11,"byte":1544},"end":{"line":55,"col":32,"byte":1565}},"sequenced\u001fcondition_key":{"start":{"line":49,"col":9,"byte":1401},"end":{"line":49,"col":69,"byte":1461}},"sequenced\u001fencounter_count":{"start":{"line":61,"col":11,"byte":1683},"end":{"line":61,"col":26,"byte":1698}},"sequenced\u001ffirst_occurrence_date":{"start":{"line":56,"col":11,"byte":1576},"end":{"line":56,"col":32,"byte":1597}},"sequenced\u001flast_occurrence_date":{"start":{"line":57,"col":11,"byte":1608},"end":{"line":57,"col":31,"byte":1628}},"sequenced\u001fpatient_count":{"start":{"line":60,"col":11,"byte":1659},"end":{"line":60,"col":24,"byte":1672}},"unknown_member\u001fcondition_code":{"start":{"line":71,"col":11,"byte":1864},"end":{"line":71,"col":38,"byte":1891}},"unknown_member\u001fcondition_description":{"start":{"line":72,"col":11,"byte":1902},"end":{"line":72,"col":55,"byte":1946}},"unknown_member\u001fcondition_key":{"start":{"line":70,"col":10,"byte":1835},"end":{"line":70,"col":28,"byte":1853}},"unknown_member\u001fencounter_count":{"start":{"line":76,"col":11,"byte":2093},"end":{"line":76,"col":31,"byte":2113}},"unknown_member\u001ffirst_occurrence_date":{"start":{"line":73,"col":16,"byte":1962},"end":{"line":73,"col":54,"byte":2000}},"unknown_member\u001flast_occurrence_date":{"start":{"line":74,"col":16,"byte":2016},"end":{"line":74,"col":53,"byte":2053}},"unknown_member\u001fpatient_count":{"start":{"line":75,"col":11,"byte":2064},"end":{"line":75,"col":29,"byte":2082}}},"raw_node_spans":{"condition_aggregates":{"start":{"line":18,"col":1,"byte":932},"end":{"line":28,"col":2,"byte":1366}},"conditions_source":{"start":{"line":5,"col":6,"byte":534},"end":{"line":16,"col":2,"byte":929}},"deduped":{"start":{"line":31,"col":1,"byte":1429},"end":{"line":44,"col":2,"byte":1807}},"final":{"start":{"line":79,"col":1,"byte":2630},"end":{"line":103,"col":2,"byte":3191}},"sequenced":{"start":{"line":46,"col":1,"byte":1810},"end":{"line":65,"col":2,"byte":2247}},"unknown_member":{"start":{"line":68,"col":1,"byte":2307},"end":{"line":77,"col":2,"byte":2627}}},"raw_column_spans":{"(final select)\u001f*":{"start":{"line":105,"col":8,"byte":3200},"end":{"line":105,"col":9,"byte":3201}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"condition_code":{"description":"ICD/SNOMED code (natural key)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_code.193c903fa2"},{"kind":"unique","kwargs":{"column_name":"condition_code","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_code.99cd9f3457"}],"documented":true},"condition_description":{"description":"Full condition name/description","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_description","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_description.dc79ef5b8c"}],"documented":true},"condition_key":{"description":"Surrogate key (row_number)","tests":[{"kind":"not_null","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_conditions_condition_key.5e35e4562f"},{"kind":"unique","kwargs":{"column_name":"condition_key","model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"}],"documented":true},"encounter_count":{"description":"Total number of encounters with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"encounter_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_encounter_count__0.9a92420a5b"}],"documented":true},"first_occurrence_date":{"description":"Earliest date this condition was diagnosed in the dataset","documented":true},"last_occurrence_date":{"description":"Most recent date this condition was diagnosed","documented":true},"patient_count":{"description":"Number of unique patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_conditions_patient_count__0.4c1de369ac"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions_source"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_aggregates"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"first_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"first_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"last_occurrence_date"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"last_occurrence_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"patient_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"deduped"}},"column":"encounter_count"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"encounter_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. 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as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}},"raw_node_spans":{"final":{"start":{"line":85,"col":1,"byte":2515},"end":{"line":89,"col":2,"byte":2602}},"sequenced":{"start":{"line":7,"col":1,"byte":158},"end":{"line":54,"col":2,"byte":1280}},"source":{"start":{"line":3,"col":6,"byte":91},"end":{"line":5,"col":2,"byte":155}},"unknown_member":{"start":{"line":57,"col":1,"byte":1340},"end":{"line":83,"col":2,"byte":2512}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__payers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"phone"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"phone"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_covered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_covered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_uncovered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_uncovered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"revenue"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"revenue"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"unique_customers"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"unique_customers"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"average_quality_of_life_score"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"average_quality_of_life_score"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"member_months"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"member_months"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith providers as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by p.provider_id) as provider_key\n\n        -- natural key\n        , p.provider_id\n\n        -- organization foreign key\n        , o.organization_key\n        , p.organization_id\n\n        -- attributes\n        , p.provider_name\n        , p.gender\n        , p.specialty\n\n        -- location\n        , p.address\n        , p.city\n        , p.state\n        , p.zip_code\n        , p.latitude\n        , p.longitude\n\n        -- metrics\n        , p.utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","node_spans":{"(final 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 -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('dim_organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_key: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}\n- input: ref('stg_synthea__providers')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., gender: ..., latitude: ..., longitude: ..., organization_id: ..., provider_id: ..., provider_name: ..., specialty: ..., state: ..., utilization: ..., zip_code: ...}"]},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.dim_providers","construct":"left_join[sequenced:organizations]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"sequenced — LEFT JOIN organizations ON providers.organization_id = organizations.organization_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"sequenced","sketches":["- input: ref('stg_synthea__providers')\n  rows:\n    - {organization_id: 404}   # 404 has no match below — the no-match path\n- input: ref('dim_organizations')\n  rows:\n    - {organization_id: 1}\n# expect: the no-match row with NULL dim_organizations columns (or the intended fallback)"]}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. 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_loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end 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timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}},"raw_node_spans":{"dim_date":{"start":{"line":38,"col":1,"byte":685},"end":{"line":43,"col":2,"byte":781}},"encounters":{"start":{"line":3,"col":6,"byte":95},"end":{"line":6,"col":2,"byte":197}},"final":{"start":{"line":45,"col":1,"byte":784},"end":{"line":103,"col":2,"byte":2732}},"organizations":{"start":{"line":24,"col":1,"byte":463},"end":{"line":29,"col":2,"byte":585}},"patients":{"start":{"line":8,"col":1,"byte":200},"end":{"line":15,"col":2,"byte":351}},"payers":{"start":{"line":31,"col":1,"byte":588},"end":{"line":36,"col":2,"byte":682}},"providers":{"start":{"line":17,"col":1,"byte":354},"end":{"line":22,"col":2,"byte":460}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":94,"col":32,"byte":2276},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"base_encounter_cost":{"description":"Base cost of the encounter","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"base_encounter_cost","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"base_encounter_cost is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_base_encounter_cost__0__base_encounter_cost_is_not_null.a096f214be"}],"documented":true},"duration_minutes":{"description":"Duration of encounter in minutes","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"duration_minutes","min_value":0,"model":"{{ get_where_subquery(ref('fct_encounters')) }}","row_condition":"duration_minutes is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_fct_encounters_duration_minutes__0__duration_minutes_is_not_null.ce51123f28"}],"documented":true},"encounter_class":{"description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},"node_id":"test.healthcare_analytics.accepted_values_fct_encounters_encounter_class__ambulatory__wellness__emergency__urgentcare__outpatient__inpatient.59f26de6a5"},{"kind":"not_null","kwargs":{"column_name":"encounter_class","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_class.9419876296"}],"documented":true},"encounter_code":{"description":"SNOMED CT code for the encounter 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}}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_encounter_start_date_key.73ce57ba43"},{"kind":"relationships","kwargs":{"column_name":"encounter_start_date_key","field":"date_key","model":"{{ get_where_subquery(ref('fct_encounters')) }}","to":"ref('dim_date')"},"node_id":"test.healthcare_analytics.relationships_fct_encounters_encounter_start_date_key__date_key__ref_dim_date_.4f1d88383c"}],"documented":true},"organization_id":{"description":"Natural organization identifier","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_id","model":"{{ get_where_subquery(ref('fct_encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_organization_id.acfb633ecb"}],"documented":true},"organization_key":{"description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"kind":"not_null","kwargs":{"column_name":"organization_key","model":"{{ get_where_subquery(ref('fct_encounters')) 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 , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , 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   where e.encounter_start_date_key != -1\n),\n\nfinal as (\n    select\n        year_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , cast(count(*) as decimal(18, 2)) / nullif(count(distinct patient_id), 0) as encounters_per_patient\n        , current_timestamp as _loaded_at\n    from encounters_with_date\n    group by year_actual, encounter_class\n)\n\nselect * from 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e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct 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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- aggregate by provider and year-month\nprovider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / encounter_count, 2)\n            else 0\n        end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)\n\nselect * from final","node_spans":{"(final 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select)"}},"column":"total_event_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_condition_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_condition_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_medication_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_medication_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedure_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedure_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final select)\u001f*":{"start":{"line":80,"col":8,"byte":2466},"end":{"line":80,"col":9,"byte":2467}},"chronic_count\u001fchronic_condition_count":{"start":{"line":59,"col":11,"byte":1578},"end":{"line":59,"col":89,"byte":1656}},"chronic_count\u001fpatient_id":{"start":{"line":58,"col":9,"byte":1557},"end":{"line":58,"col":19,"byte":1567}},"chronic_flags\u001fhas_diabetes":{"start":{"line":36,"col":11,"byte":764},"end":{"line":39,"col":29,"byte":891}},"chronic_flags\u001fhas_heart_disease":{"start":{"line":45,"col":11,"byte":1125},"end":{"line":51,"col":34,"byte":1470}},"chronic_flags\u001fhas_hypertension":{"start":{"line":40,"col":11,"byte":902},"end":{"line":44,"col":33,"byte":1114}},"chronic_flags\u001fpatient_id":{"start":{"line":35,"col":9,"byte":743},"end":{"line":35,"col":19,"byte":753}},"condition_stats\u001factive_conditions":{"start":{"line":26,"col":11,"byte":457},"end":{"line":26,"col":91,"byte":537}},"condition_stats\u001ffirst_condition_date":{"start":{"line":27,"col":11,"byte":548},"end":{"line":27,"col":60,"byte":597}},"condition_stats\u001flast_condition_date":{"start":{"line":28,"col":11,"byte":608},"end":{"line":28,"col":59,"byte":656}},"condition_stats\u001fpatient_id":{"start":{"line":24,"col":9,"byte":397},"end":{"line":24,"col":19,"byte":407}},"condition_stats\u001ftotal_conditions":{"start":{"line":25,"col":11,"byte":418},"end":{"line":25,"col":39,"byte":446}},"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":247},"end":{"line":17,"col":25,"byte":261}},"conditions\u001fcondition_description":{"start":{"line":18,"col":11,"byte":272},"end":{"line":18,"col":32,"byte":293}},"conditions\u001fcondition_end_date":{"start":{"line":16,"col":11,"byte":218},"end":{"line":16,"col":29,"byte":236}},"conditions\u001fcondition_start_date":{"start":{"line":15,"col":11,"byte":187},"end":{"line":15,"col":31,"byte":207}},"conditions\u001fpatient_id":{"start":{"line":14,"col":9,"byte":166},"end":{"line":14,"col":19,"byte":176}},"final\u001factive_conditions":{"start":{"line":67,"col":11,"byte":1800},"end":{"line":67,"col":65,"byte":1854}},"final\u001fchronic_condition_count":{"start":{"line":73,"col":11,"byte":2178},"end":{"line":73,"col":77,"byte":2244}},"final\u001ffirst_condition_date":{"start":{"line":68,"col":11,"byte":1865},"end":{"line":68,"col":34,"byte":1888}},"final\u001fhas_diabetes":{"start":{"line":70,"col":11,"byte":1932},"end":{"line":70,"col":76,"byte":1997}},"final\u001fhas_heart_disease":{"start":{"line":72,"col":11,"byte":2092},"end":{"line":72,"col":86,"byte":2167}},"final\u001fhas_hypertension":{"start":{"line":71,"col":11,"byte":2008},"end":{"line":71,"col":84,"byte":2081}},"final\u001flast_condition_date":{"start":{"line":69,"col":11,"byte":1899},"end":{"line":69,"col":33,"byte":1921}},"final\u001fpatient_id":{"start":{"line":65,"col":9,"byte":1714},"end":{"line":65,"col":21,"byte":1726}},"final\u001ftotal_conditions":{"start":{"line":66,"col":11,"byte":1737},"end":{"line":66,"col":63,"byte":1789}},"patients\u001fpatient_id":{"start":{"line":7,"col":9,"byte":42},"end":{"line":7,"col":19,"byte":52}}},"raw_node_spans":{"chronic_count":{"start":{"line":56,"col":1,"byte":2545},"end":{"line":61,"col":2,"byte":2707}},"chronic_flags":{"start":{"line":33,"col":1,"byte":1731},"end":{"line":54,"col":2,"byte":2542}},"condition_stats":{"start":{"line":22,"col":1,"byte":1383},"end":{"line":31,"col":2,"byte":1728}},"conditions":{"start":{"line":12,"col":1,"byte":1170},"end":{"line":20,"col":2,"byte":1380}},"final":{"start":{"line":63,"col":1,"byte":2710},"end":{"line":78,"col":2,"byte":3483}},"patients":{"start":{"line":5,"col":6,"byte":1059},"end":{"line":10,"col":2,"byte":1167}}},"raw_column_spans":{"conditions\u001fcondition_code":{"start":{"line":17,"col":11,"byte":1286},"end":{"line":17,"col":25,"byte":1300}}}},"column_lineage":{"context":{"active_conditions":{"description":"Number of currently active conditions (no end date)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"active_conditions","min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_active_conditions__0.4537ef7532"},{"kind":"not_null","kwargs":{"column_name":"active_conditions","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_active_conditions.4aeeebebb6"}],"documented":true},"chronic_condition_count":{"description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"chronic_condition_count","max_value":3,"min_value":0,"model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_chronic_condition_count__3__0.b747d1158d"},{"kind":"not_null","kwargs":{"column_name":"chronic_condition_count","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_chronic_condition_count.9d5583fe6d"}],"documented":true},"first_condition_date":{"description":"Date of earliest condition diagnosis for this patient","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"first_condition_date","max_value":"current_date","min_value":"'1900-01-01'::date","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","row_condition":"first_condition_date is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_int_patients__with_conditions_first_condition_date__current_date___1900_01_01_date__first_condition_date_is_not_null.bb01167c58"}],"documented":true},"has_diabetes":{"description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"kind":"accepted_values","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}","quote":false,"values":[true,false]},"node_id":"test.healthcare_analytics.accepted_values_int_patients__with_conditions_has_diabetes__False__True__False.d905d84361"},{"kind":"not_null","kwargs":{"column_name":"has_diabetes","model":"{{ get_where_subquery(ref('int_patients__with_conditions')) }}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_has_diabetes.bdb31b80da"}],"documented":true},"has_heart_disease":{"description":"Boolean flag indicating if patient has any heart 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final 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month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","rows":null,"format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv"}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}},"raw_node_spans":{"combined_metrics":{"start":{"line":29,"col":1,"byte":1516},"end":{"line":53,"col":2,"byte":2430}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":103},"end":{"line":14,"col":2,"byte":797}},"medication_metrics":{"start":{"line":16,"col":1,"byte":800},"end":{"line":27,"col":2,"byte":1513}}},"raw_column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. 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valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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select)"}},"column":"organization_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"active_patient_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"active_patient_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_active_conditions"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_active_conditions"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"avg_conditions_per_patient"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"avg_conditions_per_patient"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'marts', 'view']\n    )\n}}\n\n{#\n    Provider Active Patients View\n\n    Purpose: Active patient count per provider for panel management\n    Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). Includes an unknown member row (provider_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"provider_id","description":"Natural provider identifier (UUID)","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. 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One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
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patients with this condition","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"patient_count","min_value":0,"model":"{{ get_where_subquery(ref('dim_conditions')) 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='condition_key',\n        tags=['analytics', 'marts', 'dimension']\n    )\n}}\n\n{#\n    Condition Master Dimension\n\n    Sources: ICD/SNOMED condition codes from Synthea\n    Grain: One row per unique condition code\n    Primary use: Filtering encounters/observations by condition, disease management analysis\n\n    Deduplication: Aggregates from stg_synthea__conditions to get one row per code\n    with summary statistics (patient count, encounter count, date ranges).\n#}\n\nwith conditions_source as (\n    select\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        cast(condition_code as varchar) as condition_code\n        , condition_description\n        , patient_id\n        , encounter_id\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n),\n\ncondition_aggregates as (\n    select\n        condition_code\n        , condition_description\n        , min(condition_start_date) as first_occurrence_date\n        , max(coalesce(condition_end_date, condition_start_date)) as last_occurrence_date\n        , count(distinct patient_id) as patient_count\n        , count(distinct encounter_id) as encounter_count\n    from conditions_source\n    group by condition_code, condition_description\n),\n\n-- deduplicate in case same code has different descriptions\ndeduped as (\n    select\n        condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , row_number() over (\n            partition by condition_code\n            order by encounter_count desc, condition_description asc\n        ) as rn\n    from condition_aggregates\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by condition_code) as condition_key\n\n        -- natural key\n        , condition_code\n\n        -- attributes\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n\n        -- metrics\n        , patient_count\n        , encounter_count\n\n    from deduped\n    where rn = 1\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as condition_key\n        , 'UNKNOWN' as condition_code\n        , 'Unknown Condition' as condition_description\n        , cast(null as date) as first_occurrence_date\n        , cast(null as date) as last_occurrence_date\n        , 0 as patient_count\n        , 0 as encounter_count\n),\n\nfinal as (\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from unknown_member\n\n    union all\n\n    select\n        condition_key\n        , condition_code\n        , condition_description\n        , first_occurrence_date\n        , last_occurrence_date\n        , patient_count\n        , encounter_count\n        , current_timestamp as _loaded_at\n    from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_conditions_condition_key.93802fac5a"]},"evidence":[{"label":"unique_key","value":"condition_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_conditions","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__conditions')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}"]}]},{"name":"dim_date","path":"models/marts/core/dim_date.sql","description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","dag":{"nodes":[{"id":"date_spine","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_date.sql","role":"final"}],"edges":[{"from":"date_spine","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","date_spine":"date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n)","final":"final as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)"},"code_map":{"compiled":"\n\n\n\nwith date_spine as (\n    \n    \n    with\n        base_dates as (\n    with\n        date_spine as (\n\n            \n\n    \n    with\n        rawdata as (\n\n            \n\n    \n\n    with\n        p as (\n            select 0 as generated_number\n            union all\n            select 1\n        ),\n        unioned as (\n\n            select\n\n                \n                    p0.generated_number * power(2, 0)\n                     + \n                \n                    p1.generated_number * power(2, 1)\n                     + \n                \n                    p2.generated_number * power(2, 2)\n                     + \n                \n                    p3.generated_number * power(2, 3)\n                     + \n                \n                    p4.generated_number * power(2, 4)\n                     + \n                \n                    p5.generated_number * power(2, 5)\n                     + \n                \n                    p6.generated_number * power(2, 6)\n                     + \n                \n                    p7.generated_number * power(2, 7)\n                     + \n                \n                    p8.generated_number * power(2, 8)\n                     + \n                \n                    p9.generated_number * power(2, 9)\n                     + \n                \n                    p10.generated_number * power(2, 10)\n                     + \n                \n                    p11.generated_number * power(2, 11)\n                     + \n                \n                    p12.generated_number * power(2, 12)\n                     + \n                \n                    p13.generated_number * power(2, 13)\n                     + \n                \n                    p14.generated_number * power(2, 14)\n                     + \n                \n                    p15.generated_number * power(2, 15)\n                    \n                \n                + 1 as generated_number\n\n            from\n\n            \n                    p as p0  cross join \n            \n                    p as p1  cross join \n            \n                    p as p2  cross join \n            \n                    p as p3  cross join \n            \n                    p as p4  cross join \n            \n                    p as p5  cross join \n            \n                    p as p6  cross join \n            \n                    p as p7  cross join \n            \n                    p as p8  cross join \n            \n                    p as p9  cross join \n            \n                    p as p10  cross join \n            \n                    p as p11  cross join \n            \n                    p as p12  cross join \n            \n                    p as p13  cross join \n            \n                    p as p14  cross join \n            \n                    p as p15 \n            \n\n        )\n\n    select *\n    from unioned\n    where generated_number <= 42733\n    order by generated_number\n\n\n\n        ),\n\n        all_periods as (\n\n            select\n                (\n                    \n\n    (cast('1909-01-01' as timestamp) + cast((row_number() over (order by 1) - 1) as bigint) * interval 1 day)\n                ) as date_day\n            from rawdata\n\n        ),\n\n        filtered as (\n\n            select * from all_periods where date_day <= cast('2025-12-31' as timestamp)\n\n        )\n\n    select *\n    from filtered\n\n\n\n        )\n    select\n        cast(d.date_day as timestamp) as date_day\n    from date_spine d\n\n),\n        dates_with_prior_year_dates as (\n\n            select\n                cast(d.date_day as date) as date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-1 as bigint) * interval 1 year) as date\n                ) as prior_year_date_day,\n                cast(\n                    \n\n    (d.date_day + cast(-364 as bigint) * interval 1 day) as date\n                ) as prior_year_over_year_date_day\n            from base_dates d\n\n        )\n    select\n        d.date_day,\n        cast(\n\n    (d.date_day + cast(-1 as bigint) * interval 1 day) as date) as prior_date_day,\n        cast(\n\n    (d.date_day + cast(1 as bigint) * interval 1 day) as date) as next_date_day,\n        d.prior_year_date_day as prior_year_date_day,\n        d.prior_year_over_year_date_day,\n        -- Sunday(1) to Saturday (7)\n        cast(date_part('dow', d.date_day) + 1 as integer) as day_of_week,\n        -- Monday(1) to Sunday (7)\n        cast(date_part('isodow', d.date_day) as integer) as day_of_week_iso,\n        dayname(d.date_day) as day_of_week_name,\n        substr(dayname(d.date_day), 1, 3) as day_of_week_name_short,\n        date_part('day', d.date_day) as day_of_month,\n        date_part('dayofyear', d.date_day) as day_of_year,\n\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) as week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date) as week_end_date,\n        -- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    )\n        as prior_year_week_start_date,\n        cast(\n\n    (-- Sunday as week start date\n    cast(\n        \n\n    (date_trunc('week', \n\n    (d.prior_year_over_year_date_day + cast(1 as bigint) * interval 1 day)) + cast(-1 as bigint) * interval 1 day) as date\n    ) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_week_end_date,\n        cast(ceil(dayofyear(d.date_day) / 7) as int) as week_of_year,\n\n        cast(date_trunc('week', d.date_day) as date) as iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date) as iso_week_end_date,\n        cast(date_trunc('week', d.prior_year_over_year_date_day) as date)\n        as prior_year_iso_week_start_date,\n        cast(\n\n    (cast(date_trunc('week', d.prior_year_over_year_date_day) as date) + cast(6 as bigint) * interval 1 day) as date)\n        as prior_year_iso_week_end_date,\n        \n    cast(date_part('week', d.date_day) as integer) as iso_week_of_year,\n        case\n        when \n    cast(date_part('week', d.date_day) as integer) = 1\n        then\n            concat(\n                date_part('year', cast(\n\n    (cast(date_trunc('week', d.date_day) as date) + cast(6 as bigint) * interval 1 day) as date)),\n                '-W',\n                lpad(concat('',\n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n        else\n            concat(\n                date_part('year', cast(date_trunc('week', d.date_day) as date)),\n                '-W',\n                lpad(concat('', \n    cast(date_part('week', d.date_day) as integer)), 2, '0')\n            )\n    end as iso_year_week,\n\n        cast(ceil(dayofyear(d.prior_year_over_year_date_day) / 7) as int)\n        as prior_year_week_of_year,\n        \n    cast(date_part('week', d.prior_year_over_year_date_day) as integer)\n        as prior_year_iso_week_of_year,\n\n        cast(\n            date_part('month', d.date_day) as integer\n        ) as month_of_year,\n        monthname(d.date_day) as month_name,\n        substr(monthname(d.date_day), 1, 3) as month_name_short,\n\n        cast(date_trunc('month', d.date_day) as date) as month_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('month', d.date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as month_end_date,\n\n        cast(\n            date_trunc('month', d.prior_year_date_day) as date\n        ) as prior_year_month_start_date,\n        cast(\n            cast(\n        \n\n    (\n\n    (date_trunc('month', d.prior_year_date_day) + cast(1 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date\n        ) as prior_year_month_end_date,\n\n        cast(\n            date_part('quarter', d.date_day) as integer\n        ) as quarter_of_year,\n        cast(\n            date_trunc('quarter', d.date_day) as date\n        ) as quarter_start_date,\n        cast(-- duckdb dateadd does not support quarter interval.\n    cast(\n        \n\n    (\n\n    (date_trunc('quarter', d.date_day) + cast(3 as bigint) * interval 1 month) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as quarter_end_date,\n\n        cast(\n            date_part('year', d.date_day) as integer\n        ) as year_number,\n        cast(date_trunc('year', d.date_day) as date) as year_start_date,\n        cast(cast(\n        \n\n    (\n\n    (date_trunc('year', d.date_day) + cast(1 as bigint) * interval 1 year) + cast(-1 as bigint) * interval 1 day)\n        as date) as date) as year_end_date\n    from dates_with_prior_year_dates d\n    order by 1\n\n\n),\n\nfinal as (\n    select\n        -- surrogate key (YYYYMMDD format, valid for years up to 2147483)\n        cast(year_number * 10000 + month_of_year * 100 + day_of_month as integer) as date_key\n\n        -- actual date\n        , date_day as date_actual\n\n        -- day attributes (dbt_date convention: Sunday=1, Monday=2, ..., Saturday=7)\n        , day_of_week\n        , day_of_week_name as day_name\n        , day_of_week_name_short as day_name_short\n        , day_of_month\n        , day_of_year\n\n        -- week attributes\n        , week_of_year\n\n        -- month attributes\n        , month_of_year as month_actual\n        , month_name\n        , month_name_short\n\n        -- quarter attributes\n        , quarter_of_year as quarter_actual\n        , 'Q' || cast(quarter_of_year as varchar) as quarter_name\n\n        -- year attributes\n        , year_number as year_actual\n\n        -- flags (Sunday=1, Saturday=7 in dbt_date convention)\n        , case when day_of_week in (1, 7) then true else false end as is_weekend\n\n        -- us holidays (basic implementation, not comprehensive)\n        -- uses dbt_date convention: Sunday=1, Monday=2, Thursday=5, Saturday=7\n        , case\n            when month_of_year = 1 and day_of_month = 1 then true  -- new year's day\n            when month_of_year = 7 and day_of_month = 4 then true  -- independence day\n            when month_of_year = 12 and day_of_month = 25 then true  -- christmas\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then true  -- thanksgiving (4th thursday)\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- mlk day (3rd monday)\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then true  -- presidents day (3rd monday)\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then true  -- memorial day (last monday)\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then true  -- labor day (1st monday)\n            when month_of_year = 11 and day_of_month = 11 then true  -- veterans day\n            else false\n        end as is_us_holiday\n\n        , case\n            when month_of_year = 1 and day_of_month = 1 then 'New Year''s Day'\n            when month_of_year = 7 and day_of_month = 4 then 'Independence Day'\n            when month_of_year = 12 and day_of_month = 25 then 'Christmas Day'\n            when month_of_year = 11 and day_of_week = 5\n                and day_of_month between 22 and 28 then 'Thanksgiving'\n            when month_of_year = 1 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Martin Luther King Jr. Day'\n            when month_of_year = 2 and day_of_week = 2\n                and day_of_month between 15 and 21 then 'Presidents Day'\n            when month_of_year = 5 and day_of_week = 2\n                and day_of_month between 25 and 31 then 'Memorial Day'\n            when month_of_year = 9 and day_of_week = 2\n                and day_of_month between 1 and 7 then 'Labor Day'\n            when month_of_year = 11 and day_of_month = 11 then 'Veterans Day'\n            else null\n        end as holiday_name\n\n    from date_spine\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":409,"col":1,"byte":12325},"end":{"line":409,"col":20,"byte":12344}},"date_spine":{"start":{"line":5,"col":6,"byte":9},"end":{"line":333,"col":2,"byte":8986}},"final":{"start":{"line":335,"col":1,"byte":8989},"end":{"line":407,"col":2,"byte":12323}}},"column_spans":{"(final 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)\n}}\n\n{#\n    Calendar dimension covering 1909-01-01 to 2025-12-31.\n\n    Note on date_key: Uses YYYYMMDD integer format. 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organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__organizations\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n  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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='organization_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__organizations') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by organization_id) as organization_key\n\n        -- natural key\n        , organization_id\n\n        -- attributes\n        , organization_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- location\n        , latitude\n        , longitude\n\n        -- metrics\n        , revenue\n        , utilization\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Organization' as organization_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as revenue\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_organizations_organization_key.096582bf56"]},"evidence":[{"label":"unique_key","value":"organization_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_organizations","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__organizations')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__organizations')\n  rows:\n    - {_loaded_at: ..., address: ..., city: ..., latitude: ..., longitude: ..., organization_id: ..., organization_name: ..., phone: ..., revenue: ..., state: ..., utilization: ..., zip_code: ...}"]}]},{"name":"dim_patients","path":"models/marts/core/dim_patients.sql","description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","dag":{"nodes":[{"id":"snapshot_source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_patients.sql","role":"final"}],"edges":[{"from":"snapshot_source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n)","snapshot_source":"snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n)","unknown_member":"unknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\n-- depends_on: \"memory\".\"snapshots\".\"snp_patients\"\n-- depends_on: \"memory\".\"main_staging\".\"stg_synthea__patients\"\n\n\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    \n        \n    \n\n    \n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    \n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , 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Each row represents a\n    specific version of a patient's record.\n\n    SCD Type 2 Columns:\n    - patient_key: Surrogate key (unique per version)\n    - patient_id: Natural key (same across versions)\n    - valid_from: When this version became active\n    - valid_to: When this version was superseded (null = current)\n    - is_current: True for the active version\n\n    To query current patients only:\n        WHERE is_current = true\n\n    To query patient state at a point in time:\n        WHERE valid_from <= @date AND (valid_to > @date OR valid_to IS NULL)\n#}\n\nwith snapshot_source as (\n    -- source from snapshot for SCD2 history\n    -- falls back to staging if snapshot doesn't exist yet\n    {% if execute %}\n        {% set snapshot_exists = adapter.get_relation(\n            database=target.database,\n            schema='snapshots',\n            identifier='snp_patients'\n        ) is not none %}\n    {% else %}\n        {% set snapshot_exists = false %}\n    {% endif %}\n\n    {% if snapshot_exists %}\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- snapshot SCD2 columns\n        , dbt_scd_id\n        , dbt_valid_from\n        , dbt_valid_to\n    from {{ ref('snp_patients') }}\n    {% else %}\n    -- fallback to staging if snapshot hasn't been run yet\n    select\n        patient_id\n        , first_name\n        , last_name\n        , name_prefix\n        , name_suffix\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n        , birth_date\n        , death_date\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n        , healthcare_expenses\n        , healthcare_coverage\n        -- mock SCD2 columns for initial state\n        , patient_id as dbt_scd_id\n        , birth_date as dbt_valid_from\n        , cast(null as timestamp) as dbt_valid_to\n    from {{ ref('stg_synthea__patients') }}\n    {% endif %}\n),\n\nsequenced as (\n    select\n        -- surrogate key (unique per version, using dbt_scd_id for ordering)\n        row_number() over (order by dbt_scd_id) as patient_key\n\n        -- natural key\n        , patient_id\n\n        -- name attributes\n        , first_name\n        , last_name\n        , first_name || ' ' || last_name as full_name\n        , name_prefix\n        , name_suffix\n\n        -- dates\n        , birth_date\n        , death_date\n\n        -- derived age (current age or age at death)\n        , case\n            when death_date is not null\n            then date_diff('year', birth_date, death_date)\n            else date_diff('year', birth_date, current_date)\n        end as age_years\n\n        -- derived flags\n        , case when death_date is not null then true else false end as is_deceased\n\n        -- demographics\n        , gender\n        , race\n        , ethnicity\n        , marital_status\n\n        -- location\n        , address\n        , city\n        , state\n        , county\n        , zip_code\n        , latitude\n        , longitude\n\n        -- financial\n        , healthcare_expenses\n        , healthcare_coverage\n\n        -- scd type 2 columns from snapshot\n        , cast(dbt_valid_from as date) as valid_from\n        , cast(dbt_valid_to as date) as valid_to\n        , case when dbt_valid_to is null then true else false end as is_current\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from snapshot_source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as patient_key\n        , 'UNKNOWN' as patient_id\n        , 'Unknown' as first_name\n        , 'Patient' as last_name\n        , 'Unknown Patient' as full_name\n        , cast(null as varchar) as name_prefix\n        , cast(null as varchar) as name_suffix\n        , cast(null as date) as birth_date\n        , cast(null as date) as death_date\n        , cast(null as bigint) as age_years\n        , false as is_deceased\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as race\n        , cast(null as varchar) as ethnicity\n        , cast(null as varchar) as marital_status\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as county\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as double) as healthcare_expenses\n        , cast(null as double) as healthcare_coverage\n        , cast('1900-01-01' as date) as valid_from\n        , cast(null as date) as valid_to\n        , true as is_current\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_patients","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_patients_patient_key.a372c8896a"]},"evidence":[{"label":"unique_key","value":"patient_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_patients","construct":"union[final]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unexercised arm","value":"sequenced — no given row reaches ref('stg_synthea__patients')"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"final","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {_loaded_at: ..., address: ..., birth_date: ..., birth_place: ..., city: ..., county: ..., death_date: ..., drivers_license: ..., ethnicity: ..., first_name: ..., gender: ..., healthcare_coverage: ..., healthcare_expenses: ..., last_name: ..., latitude: ..., longitude: ..., maiden_name: ..., marital_status: ..., name_prefix: ..., name_suffix: ..., passport_number: ..., patient_id: ..., race: ..., ssn_hash: ..., state: ..., zip_code: ...}"]}]},{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as 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      -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as 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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme 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 -- metadata\n        , current_timestamp as _loaded_at\n\n    from providers p\n    left join organizations o\n        on p.organization_id = o.organization_id\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as provider_key\n        , 'UNKNOWN' as provider_id\n        , -1 as organization_key\n        , 'UNKNOWN' as organization_id\n        , 'Unknown Provider' as provider_name\n        , cast(null as varchar) as gender\n        , cast(null as varchar) as specialty\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as double) as latitude\n        , cast(null as double) as longitude\n        , cast(null as bigint) as utilization\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from 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reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. 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condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)","patients":"patients as (\n    select\n        patient_key\n        , 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condition_end_date\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from \"memory\".\"main_marts\".\"dim_conditions\"\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","node_spans":{"(final 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unique_key='condition_cohort_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Patient-Condition Cohort Fact\n\n    Purpose: Patient-condition relationships for disease management and outcomes analysis\n    Grain: One row per patient-condition combination (first occurrence)\n    Use cases: Disease management, outcomes by condition, cohort analysis\n\n    Aggregates condition occurrences by patient to show:\n    - First and last diagnosis dates\n    - Duration of condition\n    - Number of encounters with the condition\n    - Active status (no end date = currently active)\n#}\n\nwith conditions_source as (\n    select\n        patient_id\n        , encounter_id\n        -- cast to varchar for consistent handling (source is BIGINT SNOMED codes)\n        , cast(condition_code as varchar) as condition_code\n        , condition_description\n        , condition_start_date\n        , condition_end_date\n    from {{ ref('stg_synthea__conditions') }}\n    where condition_code is not null\n      and patient_id is not null\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_conditions as (\n    select\n        condition_key\n        , condition_code\n    from {{ ref('dim_conditions') }}\n),\n\n-- aggregate conditions by patient and condition code\ncondition_windows as (\n    select\n        patient_id\n        , condition_code\n        , max(condition_description) as condition_description\n        , min(condition_start_date) as first_diagnosis_date\n        , max(condition_end_date) as last_diagnosis_date\n        , count(distinct encounter_id) as encounter_count\n        -- if any occurrence has no end date, condition is considered active\n        , count(case when condition_end_date is null then 1 end) > 0 as is_active\n    from conditions_source\n    group by patient_id, condition_code\n),\n\n-- join to dimensions\nwith_dimensions as (\n    select\n        cw.patient_id\n        , cw.condition_code\n        , cw.condition_description\n        , cw.first_diagnosis_date\n        , cw.last_diagnosis_date\n        , cw.encounter_count\n        , cw.is_active\n        , coalesce(p.patient_key, -1) as patient_key\n        , coalesce(dc.condition_key, -1) as condition_key\n    from condition_windows as cw\n    left join patients as p on cw.patient_id = p.patient_id\n    left join dim_conditions as dc on cw.condition_code = dc.condition_code\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || condition_code) as condition_cohort_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n\n        -- condition details\n        , condition_description\n\n        -- date dimensions\n        , first_diagnosis_date\n        , last_diagnosis_date\n        , extract(year from first_diagnosis_date) as first_diagnosis_year\n\n        -- measures: duration\n        , case\n            when last_diagnosis_date is not null and first_diagnosis_date is not null\n            then greatest(\n                0,\n                (extract(year from last_diagnosis_date) - extract(year from first_diagnosis_date)) * 12\n                + (extract(month from last_diagnosis_date) - extract(month from first_diagnosis_date))\n            )\n            else 0\n        end as months_with_condition\n\n        -- measures: counts\n        , encounter_count\n\n        -- measures: status\n        , is_active\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_dimensions\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_condition_cohorts","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_condition_cohorts_condition_cohort_key.f8c8f721d9"]},"evidence":[{"label":"unique_key","value":"condition_cohort_key"}],"pin_node":"(final select)"}]},{"name":"fct_cost_analysis","path":"models/marts/analytics/fct_cost_analysis.sql","description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"dim_date","role":"import"},{"id":"procedures","role":"import"},{"id":"encounters_enriched","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_cost_analysis.sql","role":"final"}],"edges":[{"from":"encounters","to":"encounters_enriched","edge_type":"from"},{"from":"dim_date","to":"encounters_enriched","edge_type":"left"},{"from":"procedures","to":"encounters_enriched","edge_type":"left"},{"from":"encounters_enriched","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_enriched":"encounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n)","final":"final as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","node_spans":{"(final 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unique_key='cost_analysis_key',\n        tags=['analytics', 'marts', 'fact']\n    )\n}}\n\n{#\n    Encounter Cost Analysis Fact\n\n    Purpose: Detailed cost breakdown for financial analysis and payer management\n    Grain: One row per encounter (simplified from original spec)\n    Use cases: Financial analysis, payer contract optimization, cost allocation\n\n    Provides cost breakdown including:\n    - Total cost, payer coverage, patient responsibility\n    - Coverage percentages\n    - Cost per procedure\n#}\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , encounter_start_at\n    from {{ ref('fct_encounters') }}\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n        , year_actual\n    from {{ ref('dim_date') }}\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , sum(base_cost) as procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\n-- join encounters to date and procedures\nencounters_enriched as (\n    select\n        e.encounter_key\n        , e.encounter_id\n        , e.patient_key\n        , e.patient_id\n        , e.payer_key\n        , e.payer_id\n        , e.provider_key\n        , e.provider_id\n        , e.encounter_start_date_key\n        , e.encounter_class\n        , coalesce(e.total_claim_cost, 0) as total_cost\n        , coalesce(e.payer_coverage, 0) as payer_coverage\n        , coalesce(e.patient_responsibility, 0) as patient_responsibility\n        , d.date_actual as encounter_date\n        , d.year_actual\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , p.procedure_cost\n    from encounters as e\n    left join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as p on e.encounter_id = p.encounter_id\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(encounter_id) as cost_analysis_key\n\n        -- encounter reference\n        , encounter_key\n        , encounter_id\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n        , payer_key\n        , payer_id\n        , provider_key\n        , provider_id\n\n        -- date dimensions\n        , encounter_start_date_key as encounter_date_key\n        , encounter_date\n        , year_actual\n\n        -- encounter details\n        , encounter_class\n\n        -- measures: costs\n        , total_cost\n        , payer_coverage\n        , patient_responsibility\n\n        -- measures: percentages\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n            else 0\n        end as coverage_pct\n\n        , case\n            when total_cost > 0\n            then round(patient_responsibility / total_cost * 100, 2)\n            else 0\n        end as patient_cost_pct\n\n        -- measures: procedure costs\n        , procedure_count\n        , case\n            when procedure_count > 0\n            then round(total_cost / procedure_count, 2)\n        end as cost_per_procedure\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters_enriched\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_cost_analysis_cost_analysis_key.2aecf62159"]},"evidence":[{"label":"unique_key","value":"cost_analysis_key"}],"pin_node":"(final select)"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:dim_date]","verdict":{"status":"unknown"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN dim_date ON encounters.encounter_start_date_key = dim_date.date_key"},{"label":"unattributable join","value":"encounters_enriched — join key or side binding not statically recoverable"}],"pin_node":"encounters_enriched"},{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.fct_cost_analysis","construct":"left_join[encounters_enriched:procedures]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"encounters_enriched — LEFT JOIN procedures ON encounters.encounter_id = procedures.encounter_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"encounters_enriched","sketches":["- input: ref('fct_encounters')\n  rows:\n    - {encounter_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__procedures')\n  rows:\n    - {encounter_id: 1}\n# expect: the no-match row with NULL stg_synthea__procedures columns (or the intended fallback)"]}]},{"name":"fct_encounters","path":"models/marts/core/fct_encounters.sql","description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","dag":{"nodes":[{"id":"encounters","role":"import"},{"id":"patients","role":"import"},{"id":"providers","role":"import"},{"id":"organizations","role":"import"},{"id":"payers","role":"import"},{"id":"dim_date","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_encounters.sql","role":"final"}],"edges":[{"from":"encounters","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"providers","to":"final","edge_type":"left"},{"from":"organizations","to":"final","edge_type":"left"},{"from":"payers","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)","organizations":"organizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","payers":"payers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n)"},"code_map":{"compiled":"\n\nwith encounters as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n        , birth_date\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nproviders as (\n    select\n        provider_key\n        , provider_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n),\n\norganizations as (\n    select\n        organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_organizations\"\n),\n\npayers as (\n    select\n        payer_key\n        , payer_id\n    from \"memory\".\"main_marts\".\"dim_payers\"\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.encounter_id) as encounter_key\n\n        -- natural key\n        , e.encounter_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(pr.provider_key, -1) as provider_key\n        , e.provider_id\n        , coalesce(o.organization_key, -1) as organization_key\n        , e.organization_id\n        , coalesce(py.payer_key, -1) as payer_key\n        , e.payer_id\n\n        -- date keys\n        , coalesce(ds.date_key, -1) as encounter_start_date_key\n        , coalesce(de.date_key, -1) as encounter_end_date_key\n\n        -- attributes\n        , e.encounter_class\n        , e.encounter_code\n        , e.encounter_description\n        , e.reason_code\n        , e.reason_description\n\n        -- timestamps\n        , e.encounter_start_at\n        , e.encounter_end_at\n\n        -- derived duration\n        , date_diff('minute', e.encounter_start_at, e.encounter_end_at) as duration_minutes\n\n        -- costs\n        , e.base_encounter_cost\n        , e.total_claim_cost\n        , e.payer_coverage\n        , e.total_claim_cost - coalesce(e.payer_coverage, 0) as patient_responsibility\n\n        -- derived patient context\n        , case\n            when p.birth_date is not null\n            then date_diff('year', p.birth_date, cast(e.encounter_start_at as date))\n        end as patient_age_at_encounter\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from encounters as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join providers as pr on e.provider_id = pr.provider_id\n    left join organizations as o on e.organization_id = o.organization_id\n    left join payers as py on e.payer_id = py.payer_id\n    left join dim_date as ds on cast(e.encounter_start_at as date) = ds.date_actual\n    left join dim_date as de on cast(e.encounter_end_at as date) = de.date_actual\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":109,"col":1,"byte":2729},"end":{"line":109,"col":20,"byte":2748}},"dim_date":{"start":{"line":42,"col":1,"byte":669},"end":{"line":47,"col":2,"byte":776}},"encounters":{"start":{"line":3,"col":6,"byte":7},"end":{"line":10,"col":2,"byte":137}},"final":{"start":{"line":49,"col":1,"byte":779},"end":{"line":107,"col":2,"byte":2727}},"organizations":{"start":{"line":28,"col":1,"byte":425},"end":{"line":33,"col":2,"byte":558}},"patients":{"start":{"line":12,"col":1,"byte":140},"end":{"line":19,"col":2,"byte":302}},"payers":{"start":{"line":35,"col":1,"byte":561},"end":{"line":40,"col":2,"byte":666}},"providers":{"start":{"line":21,"col":1,"byte":305},"end":{"line":26,"col":2,"byte":422}}},"column_spans":{"(final select)\u001f*":{"start":{"line":109,"col":8,"byte":2736},"end":{"line":109,"col":9,"byte":2737}},"dim_date\u001fdate_actual":{"start":{"line":45,"col":11,"byte":721},"end":{"line":45,"col":22,"byte":732}},"dim_date\u001fdate_key":{"start":{"line":44,"col":9,"byte":702},"end":{"line":44,"col":17,"byte":710}},"encounters\u001f*":{"start":{"line":4,"col":12,"byte":34},"end":{"line":4,"col":13,"byte":35}},"final\u001f_loaded_at":{"start":{"line":98,"col":11,"byte":2250},"end":{"line":98,"col":42,"byte":2281}},"final\u001fbase_encounter_cost":{"start":{"line":86,"col":11,"byte":1836},"end":{"line":86,"col":32,"byte":1857}},"final\u001fduration_minutes":{"start":{"line":83,"col":11,"byte":1726},"end":{"line":83,"col":92,"byte":1807}},"final\u001fencounter_class":{"start":{"line":72,"col":11,"byte":1470},"end":{"line":72,"col":28,"byte":1487}},"final\u001fencounter_code":{"start":{"line":73,"col":11,"byte":1498},"end":{"line":73,"col":27,"byte":1514}},"final\u001fencounter_description":{"start":{"line":74,"col":11,"byte":1525},"end":{"line":74,"col":34,"byte":1548}},"final\u001fencounter_end_at":{"start":{"line":80,"col":11,"byte":1668},"end":{"line":80,"col":29,"byte":1686}},"final\u001fencounter_end_date_key":{"start":{"line":69,"col":11,"byte":1385},"end":{"line":69,"col":62,"byte":1436}},"final\u001fencounter_id":{"start":{"line":55,"col":11,"byte":929},"end":{"line":55,"col":25,"byte":943}},"final\u001fencounter_key":{"start":{"line":52,"col":9,"byte":834},"end":{"line":52,"col":69,"byte":894}},"final\u001fencounter_start_at":{"start":{"line":79,"col":11,"byte":1637},"end":{"line":79,"col":31,"byte":1657}},"final\u001fencounter_start_date_key":{"start":{"line":68,"col":11,"byte":1321},"end":{"line":68,"col":64,"byte":1374}},"final\u001forganization_id":{"start":{"line":63,"col":11,"byte":1200},"end":{"line":63,"col":28,"byte":1217}},"final\u001forganization_key":{"start":{"line":62,"col":11,"byte":1137},"end":{"line":62,"col":63,"byte":1189}},"final\u001fpatient_age_at_encounter":{"start":{"line":92,"col":11,"byte":2047},"end":{"line":95,"col":40,"byte":2218}},"final\u001fpatient_id":{"start":{"line":59,"col":11,"byte":1034},"end":{"line":59,"col":23,"byte":1046}},"final\u001fpatient_key":{"start":{"line":58,"col":11,"byte":981},"end":{"line":58,"col":53,"byte":1023}},"final\u001fpatient_responsibility":{"start":{"line":89,"col":11,"byte":1924},"end":{"line":89,"col":87,"byte":2000}},"final\u001fpayer_coverage":{"start":{"line":88,"col":11,"byte":1897},"end":{"line":88,"col":27,"byte":1913}},"final\u001fpayer_id":{"start":{"line":65,"col":11,"byte":1278},"end":{"line":65,"col":21,"byte":1288}},"final\u001fpayer_key":{"start":{"line":64,"col":11,"byte":1228},"end":{"line":64,"col":50,"byte":1267}},"final\u001fprovider_id":{"start":{"line":61,"col":11,"byte":1113},"end":{"line":61,"col":24,"byte":1126}},"final\u001fprovider_key":{"start":{"line":60,"col":11,"byte":1057},"end":{"line":60,"col":56,"byte":1102}},"final\u001freason_code":{"start":{"line":75,"col":11,"byte":1559},"end":{"line":75,"col":24,"byte":1572}},"final\u001freason_description":{"start":{"line":76,"col":11,"byte":1583},"end":{"line":76,"col":31,"byte":1603}},"final\u001ftotal_claim_cost":{"start":{"line":87,"col":11,"byte":1868},"end":{"line":87,"col":29,"byte":1886}},"organizations\u001forganization_id":{"start":{"line":31,"col":11,"byte":490},"end":{"line":31,"col":26,"byte":505}},"organizations\u001forganization_key":{"start":{"line":30,"col":9,"byte":463},"end":{"line":30,"col":25,"byte":479}},"patients\u001fbirth_date":{"start":{"line":16,"col":11,"byte":216},"end":{"line":16,"col":21,"byte":226}},"patients\u001fpatient_id":{"start":{"line":15,"col":11,"byte":195},"end":{"line":15,"col":21,"byte":205}},"patients\u001fpatient_key":{"start":{"line":14,"col":9,"byte":173},"end":{"line":14,"col":20,"byte":184}},"payers\u001fpayer_id":{"start":{"line":38,"col":11,"byte":612},"end":{"line":38,"col":19,"byte":620}},"payers\u001fpayer_key":{"start":{"line":37,"col":9,"byte":592},"end":{"line":37,"col":18,"byte":601}},"providers\u001fprovider_id":{"start":{"line":24,"col":11,"byte":362},"end":{"line":24,"col":22,"byte":373}},"providers\u001fprovider_key":{"start":{"line":23,"col":9,"byte":339},"end":{"line":23,"col":21,"byte":351}}},"raw_node_spans":{"dim_date":{"start":{"line":38,"col":1,"byte":685},"end":{"line":43,"col":2,"byte":781}},"encounters":{"start":{"line":3,"col":6,"byte":95},"end":{"line":6,"col":2,"byte":197}},"final":{"start":{"line":45,"col":1,"byte":784},"end":{"line":103,"col":2,"byte":2732}},"organizations":{"start":{"line":24,"col":1,"byte":463},"end":{"line":29,"col":2,"byte":585}},"patients":{"start":{"line":8,"col":1,"byte":200},"end":{"line":15,"col":2,"byte":351}},"payers":{"start":{"line":31,"col":1,"byte":588},"end":{"line":36,"col":2,"byte":682}},"providers":{"start":{"line":17,"col":1,"byte":354},"end":{"line":22,"col":2,"byte":460}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":94,"col":32,"byte":2276},"end":{"line":94,"col":42,"byte":2286}},"final\u001fbase_encounter_cost":{"start":{"line":82,"col":13,"byte":1843},"end":{"line":82,"col":32,"byte":1862}},"final\u001fduration_minutes":{"start":{"line":79,"col":76,"byte":1796},"end":{"line":79,"col":92,"byte":1812}},"final\u001fencounter_class":{"start":{"line":68,"col":13,"byte":1477},"end":{"line":68,"col":28,"byte":1492}},"final\u001fencounter_code":{"start":{"line":69,"col":13,"byte":1505},"end":{"line":69,"col":27,"byte":1519}},"final\u001fencounter_description":{"start":{"line":70,"col":13,"byte":1532},"end":{"line":70,"col":34,"byte":1553}},"final\u001fencounter_end_date_key":{"start":{"line":65,"col":40,"byte":1419},"end":{"line":65,"col":62,"byte":1441}},"final\u001fencounter_key":{"start":{"line":48,"col":56,"byte":886},"end":{"line":48,"col":69,"byte":899}},"final\u001fencounter_start_date_key":{"start":{"line":64,"col":40,"byte":1355},"end":{"line":64,"col":64,"byte":1379}},"final\u001fpatient_age_at_encounter":{"start":{"line":91,"col":16,"byte":2199},"end":{"line":91,"col":40,"byte":2223}},"final\u001fpatient_responsibility":{"start":{"line":85,"col":65,"byte":1983},"end":{"line":85,"col":87,"byte":2005}},"final\u001freason_code":{"start":{"line":71,"col":13,"byte":1566},"end":{"line":71,"col":24,"byte":1577}},"final\u001freason_description":{"start":{"line":72,"col":13,"byte":1590},"end":{"line":72,"col":31,"byte":1608}}},"raw_dag":{"nodes":[{"id":"dim_date","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"encounters","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"final","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"organizations","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"patients","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"payers","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"providers","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"dim_date":"dim_date","encounters":"encounters","final":"final","organizations":"organizations","patients":"patients","payers":"payers","providers":"providers"}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp 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This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"fct_encounters_monthly","path":"models/marts/core/fct_encounters_monthly.sql","description":"Monthly aggregated encounter statistics by encounter class. 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 , payer_coverage\n        , patient_responsibility\n        , duration_minutes\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar) || '-' ||\n            lpad(cast(d.month_actual as varchar), 2, '0') as year_month\n    from encounters e\n    inner join dim_date d on e.encounter_start_date_key = d.date_key\n    where e.encounter_start_date_key != -1\n)","final":"final as (\n    select\n        year_month\n        , year_actual\n        , month_actual\n        , encounter_class\n        , count(*) as encounter_count\n        , count(distinct patient_id) as unique_patients\n        , sum(total_claim_cost) as total_claim_cost\n        , sum(payer_coverage) as total_payer_coverage\n        , sum(patient_responsibility) as total_patient_responsibility\n        , avg(duration_minutes) as avg_duration_minutes\n        , 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costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)","patient_year_agg":"patient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","with_conditions":"with_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , payer_key\n        , payer_id\n        , encounter_start_date_key\n        , encounter_class\n        , total_claim_cost\n        , payer_coverage\n        , patient_responsibility\n        , patient_age_at_encounter\n        , encounter_start_at\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where patient_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_code\n        , extract(year from condition_start_date) as condition_year\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\n-- join encounters to date dimension to get year\nencounters_with_year as (\n    select\n        e.*\n        , d.year_actual\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != -1\n),\n\n-- count distinct conditions per patient per year\nconditions_per_patient_year as (\n    select\n        patient_id\n        , condition_year as year_actual\n        , count(distinct condition_code) as condition_count\n    from conditions\n    group by patient_id, condition_year\n),\n\n-- aggregate by patient and year\npatient_year_agg as (\n    select\n        e.patient_id\n        , e.year_actual\n        , max(e.patient_key) as patient_key\n        , count(*) as encounter_count\n        , sum(e.procedure_count) as procedure_count\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as payer_coverage\n        , sum(e.patient_responsibility) as patient_responsibility\n        , count(distinct e.provider_id) as unique_providers\n        , count(distinct e.organization_id) as unique_organizations\n        , max(e.patient_age_at_encounter) as patient_age_at_year_end\n    from encounters_with_year as e\n    group by e.patient_id, e.year_actual\n),\n\n-- join condition counts\nwith_conditions as (\n    select\n        pya.*\n        , coalesce(c.condition_count, 0) as condition_count\n    from patient_year_agg as pya\n    left join conditions_per_patient_year as c\n        on pya.patient_id = c.patient_id\n        and pya.year_actual = c.year_actual\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(patient_id as varchar) || '-' || cast(year_actual as varchar)) as patient_summary_key\n\n        -- dimension keys\n        , patient_key\n        , patient_id\n\n        -- time grain\n        , year_actual\n\n        -- patient context\n        , patient_age_at_year_end\n\n        -- measures: counts\n        , encounter_count\n        , procedure_count\n        , condition_count\n        , unique_providers\n        , unique_organizations\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(payer_coverage, 0) as payer_coverage\n        , coalesce(patient_responsibility, 0) as patient_responsibility\n\n        -- measures: ratios\n        , case\n            when total_cost > 0\n            then round(payer_coverage / total_cost * 100, 2)\n        end as payer_coverage_pct\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from with_conditions\n)\n\nselect * from final","node_spans":{"(final 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select)\u001f*":{"start":{"line":141,"col":8,"byte":3661},"end":{"line":141,"col":9,"byte":3662}},"conditions\u001fcondition_code":{"start":{"line":46,"col":11,"byte":892},"end":{"line":46,"col":25,"byte":906}},"conditions\u001fcondition_year":{"start":{"line":47,"col":29,"byte":935},"end":{"line":47,"col":68,"byte":974}},"conditions\u001fpatient_id":{"start":{"line":45,"col":9,"byte":871},"end":{"line":45,"col":19,"byte":881}},"conditions_per_patient_year\u001fcondition_count":{"start":{"line":68,"col":11,"byte":1595},"end":{"line":68,"col":60,"byte":1644}},"conditions_per_patient_year\u001fpatient_id":{"start":{"line":66,"col":9,"byte":1534},"end":{"line":66,"col":19,"byte":1544}},"conditions_per_patient_year\u001fyear_actual":{"start":{"line":67,"col":11,"byte":1555},"end":{"line":67,"col":40,"byte":1584}},"dim_date\u001fdate_key":{"start":{"line":30,"col":9,"byte":584},"end":{"line":30,"col":17,"byte":592}},"dim_date\u001fyear_actual":{"start":{"line":31,"col":11,"byte":603},"end":{"line":31,"col":22,"byte":614}},"encounters\u001fencounter_class":{"start":{"line":18,"col":11,"byte":306},"end":{"line":18,"col":26,"byte":321}},"encounters\u001fencounter_id":{"start":{"line":8,"col":11,"byte":68},"end":{"line":8,"col":23,"byte":80}},"encounters\u001fencounter_key":{"start":{"line":7,"col":9,"byte":44},"end":{"line":7,"col":22,"byte":57}},"encounters\u001fencounter_start_at":{"start":{"line":23,"col":11,"byte":452},"end":{"line":23,"col":29,"byte":470}},"encounters\u001fencounter_start_date_key":{"start":{"line":17,"col":11,"byte":271},"end":{"line":17,"col":35,"byte":295}},"encounters\u001forganization_id":{"start":{"line":14,"col":11,"byte":206},"end":{"line":14,"col":26,"byte":221}},"encounters\u001forganization_key":{"start":{"line":13,"col":11,"byte":179},"end":{"line":13,"col":27,"byte":195}},"encounters\u001fpatient_age_at_encounter":{"start":{"line":22,"col":11,"byte":417},"end":{"line":22,"col":35,"byte":441}},"encounters\u001fpatient_id":{"start":{"line":10,"col":11,"byte":113},"end":{"line":10,"col":21,"byte":123}},"encounters\u001fpatient_key":{"start":{"line":9,"col":11,"byte":91},"end":{"line":9,"col":22,"byte":102}},"encounters\u001fpatient_responsibility":{"start":{"line":21,"col":11,"byte":384},"end":{"line":21,"col":33,"byte":406}},"encounters\u001fpayer_coverage":{"start":{"line":20,"col":11,"byte":359},"end":{"line":20,"col":25,"byte":373}},"encounters\u001fpayer_id":{"start":{"line":16,"col":11,"byte":252},"end":{"line":16,"col":19,"byte":260}},"encounters\u001fpayer_key":{"start":{"line":15,"col":11,"byte":232},"end":{"line":15,"col":20,"byte":241}},"encounters\u001fprovider_id":{"start":{"line":12,"col":11,"byte":157},"end":{"line":12,"col":22,"byte":168}},"encounters\u001fprovider_key":{"start":{"line":11,"col":11,"byte":134},"end":{"line":11,"col":23,"byte":146}},"encounters\u001ftotal_claim_cost":{"start":{"line":19,"col":11,"byte":332},"end":{"line":19,"col":27,"byte":348}},"encounters_with_year\u001f*":{"start":{"line":54,"col":9,"byte":1132},"end":{"line":54,"col":12,"byte":1135}},"encounters_with_year\u001fprocedure_count":{"start":{"line":56,"col":11,"byte":1170},"end":{"line":56,"col":61,"byte":1220}},"encounters_with_year\u001fyear_actual":{"start":{"line":55,"col":11,"byte":1146},"end":{"line":55,"col":24,"byte":1159}},"final\u001f_loaded_at":{"start":{"line":136,"col":11,"byte":3593},"end":{"line":136,"col":42,"byte":3624}},"final\u001fcondition_count":{"start":{"line":120,"col":11,"byte":3113},"end":{"line":120,"col":26,"byte":3128}},"final\u001fencounter_count":{"start":{"line":118,"col":11,"byte":3061},"end":{"line":118,"col":26,"byte":3076}},"final\u001fpatient_age_at_year_end":{"start":{"line":115,"col":11,"byte":2998},"end":{"line":115,"col":34,"byte":3021}},"final\u001fpatient_id":{"start":{"line":109,"col":11,"byte":2904},"end":{"line":109,"col":21,"byte":2914}},"final\u001fpatient_key":{"start":{"line":108,"col":11,"byte":2882},"end":{"line":108,"col":22,"byte":2893}},"final\u001fpatient_responsibility":{"start":{"line":127,"col":11,"byte":3329},"end":{"line":127,"col":72,"byte":3390}},"final\u001fpatient_summary_key":{"start":{"line":105,"col":9,"byte":2750},"end":{"line":105,"col":103,"byte":2844}},"final\u001fpayer_coverage":{"start":{"line":126,"col":11,"byte":3273},"end":{"line":126,"col":56,"byte":3318}},"final\u001fpayer_coverage_pct":{"start":{"line":130,"col":11,"byte":3430},"end":{"line":133,"col":34,"byte":3561}},"final\u001fprocedure_count":{"start":{"line":119,"col":11,"byte":3087},"end":{"line":119,"col":26,"byte":3102}},"final\u001ftotal_cost":{"start":{"line":125,"col":11,"byte":3225},"end":{"line":125,"col":48,"byte":3262}},"final\u001funique_organizations":{"start":{"line":122,"col":11,"byte":3166},"end":{"line":122,"col":31,"byte":3186}},"final\u001funique_providers":{"start":{"line":121,"col":11,"byte":3139},"end":{"line":121,"col":27,"byte":3155}},"final\u001fyear_actual":{"start":{"line":112,"col":11,"byte":2948},"end":{"line":112,"col":22,"b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 select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","encounters_with_date":"encounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != 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     end as avg_cost_per_encounter\n\n        , case\n            when unique_patient_count > 0\n            then round(cast(encounter_count as decimal) / unique_patient_count, 2)\n            else 0\n        end as encounters_per_patient_avg\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from provider_month_agg\n)","procedures":"procedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n)","provider_month_agg":"provider_month_agg as (\n    select\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n\n        -- counts\n        , count(*) as encounter_count\n        , count(distinct e.patient_id) as unique_patient_count\n\n        -- patient metrics\n        , avg(e.patient_age_at_encounter) as avg_patient_age\n\n        -- encounter metrics\n        , avg(e.duration_minutes) as avg_encounter_duration_minutes\n\n        -- procedure metrics\n        , sum(e.procedure_count) as total_procedures\n\n        -- cost metrics\n        , sum(e.total_claim_cost) as total_cost\n        , sum(e.payer_coverage) as total_payer_coverage\n\n    from encounters_with_date as e\n    group by\n        e.provider_id\n        , e.provider_key\n        , e.organization_id\n        , e.organization_key\n        , e.year_actual\n        , e.month_actual\n        , e.year_month_str\n)"},"code_map":{"compiled":"\n\n\n\nwith encounters as (\n    select\n        encounter_key\n        , encounter_id\n        , patient_key\n        , patient_id\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n        , encounter_start_date_key\n        , total_claim_cost\n        , payer_coverage\n        , duration_minutes\n        , patient_age_at_encounter\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\ndim_date as (\n    select\n        date_key\n        , year_actual\n        , month_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\"\n    group by encounter_id\n),\n\n-- join encounters to date dimension to get year/month\nencounters_with_date as (\n    select\n        e.*\n        , d.year_actual\n        , d.month_actual\n        , cast(d.year_actual as varchar)\n            || '-'\n            || lpad(cast(d.month_actual as varchar), 2, '0') as year_month_str\n        , coalesce(pr.procedure_count, 0) as procedure_count\n    from encounters as e\n    inner join dim_date as d on e.encounter_start_date_key = d.date_key\n    left join procedures as pr on e.encounter_id = pr.encounter_id\n    where e.encounter_start_date_key != 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   , e.month_actual\n        , e.year_month_str\n),\n\nfinal as (\n    select\n        -- surrogate key\n        md5(cast(provider_id as varchar) || '-' || year_month_str) as provider_metrics_key\n\n        -- dimension keys\n        , provider_key\n        , provider_id\n        , organization_key\n        , organization_id\n\n        -- time grain\n        , year_actual\n        , month_actual\n        , year_month_str\n\n        -- measures: counts\n        , encounter_count\n        , unique_patient_count\n        , total_procedures\n\n        -- measures: averages\n        , round(avg_patient_age, 2) as avg_patient_age\n        , round(avg_encounter_duration_minutes, 2) as avg_encounter_duration_minutes\n\n        -- measures: costs\n        , coalesce(total_cost, 0) as total_cost\n        , coalesce(total_payer_coverage, 0) as total_payer_coverage\n\n        -- measures: derived ratios\n        , case\n            when encounter_count > 0\n            then round(total_cost / 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tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__medications',\n    description='Medications quarantined due to data quality violations'\n) }}","tests":[],"is_recursive":false},{"name":"int_encounters__enriched","path":"models/intermediate/healthcare/int_encounters__enriched.sql","description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. 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select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"condition_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"condition_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"medication_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"medication_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"procedure_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"procedure_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_condition_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_condition_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_medication_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_medication_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_procedure_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_procedure_cost"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"total_event_cost"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_event_cost"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='view'\n    )\n}}\n\nwith encounters as (\n    select\n        encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nconditions as (\n    select\n        encounter_id\n        , count(*) as condition_count\n        , cast(0 as decimal(18, 2)) as total_condition_cost\n    from {{ ref('stg_synthea__conditions') }}\n    group by encounter_id\n),\n\nmedications as (\n    select\n        encounter_id\n        , count(*) as medication_count\n        , coalesce(sum(total_cost), 0) as total_medication_cost\n    from {{ ref('stg_synthea__medications') }}\n    {{ quarantine_filter() }}\n    group by encounter_id\n),\n\nprocedures as (\n    select\n        encounter_id\n        , count(*) as procedure_count\n        , coalesce(sum(base_cost), 0) as total_procedure_cost\n    from {{ ref('stg_synthea__procedures') }}\n    group by encounter_id\n),\n\nfinal as (\n    select\n        e.encounter_id\n        , coalesce(c.condition_count, 0) as condition_count\n        , coalesce(m.medication_count, 0) as medication_count\n        , coalesce(p.procedure_count, 0) as procedure_count\n        , coalesce(c.condition_count, 0)\n            + coalesce(m.medication_count, 0)\n            + coalesce(p.procedure_count, 0) as total_event_count\n        , coalesce(c.total_condition_cost, 0) as total_condition_cost\n        , coalesce(m.total_medication_cost, 0) as total_medication_cost\n        , coalesce(p.total_procedure_cost, 0) as total_procedure_cost\n        , coalesce(c.total_condition_cost, 0)\n            + coalesce(m.total_medication_cost, 0)\n            + coalesce(p.total_procedure_cost, 0) as total_event_cost\n    from encounters as e\n    left join conditions as c on e.encounter_id = c.encounter_id\n    left join medications as m on e.encounter_id = m.encounter_id\n    left join procedures as p on e.encounter_id = p.encounter_id\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}},"raw_dag":{"nodes":[{"id":"(final select)","role":"terminal","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"(final select)":"(final select)"}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"int_patients__with_conditions","path":"models/intermediate/healthcare/int_patients__with_conditions.sql","description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","dag":{"nodes":[{"id":"patients","role":"import"},{"id":"conditions","role":"import"},{"id":"condition_stats","role":"transform"},{"id":"chronic_flags","role":"transform"},{"id":"chronic_count","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"int_patients__with_conditions.sql","role":"final"}],"edges":[{"from":"conditions","to":"condition_stats","edge_type":"from"},{"from":"conditions","to":"chronic_flags","edge_type":"from"},{"from":"chronic_flags","to":"chronic_count","edge_type":"from"},{"from":"patients","to":"final","edge_type":"from"},{"from":"condition_stats","to":"final","edge_type":"left"},{"from":"chronic_flags","to":"final","edge_type":"left"},{"from":"chronic_count","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","chronic_count":"chronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n)","chronic_flags":"chronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n)","condition_stats":"condition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n)","conditions":"conditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n)","final":"final as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)","patients":"patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)"},"code_map":{"compiled":"\n\n\n\nwith patients as (\n    select\n        patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\"\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","node_spans":{"(final select)":{"start":{"line":80,"col":1,"byte":2459},"end":{"line":80,"col":20,"byte":2478}},"chronic_count":{"start":{"line":56,"col":1,"byte":1519},"end":{"line":61,"col":2,"byte":1681}},"chronic_flags":{"start":{"line":33,"col":1,"byte":705},"end":{"line":54,"col":2,"byte":1516}},"condition_stats":{"start":{"line":22,"col":1,"byte":357},"end":{"line":31,"col":2,"byte":702}},"conditions":{"start":{"line":12,"col":1,"byte":131},"end":{"line":20,"col":2,"byte":354}},"final":{"start":{"line":63,"col":1,"byte":1684},"end":{"line":78,"col":2,"byte":2457}},"patients":{"start":{"line":5,"col":6,"byte":9},"end":{"line":10,"col":2,"byte":128}}},"column_spans":{"(final 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}}"},"node_id":"test.healthcare_analytics.not_null_int_patients__with_conditions_total_conditions.9dcd52a52e"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"patients"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_start_date"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_start_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_end_date"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_end_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_code"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__conditions"}},"column":"condition_description"},"to_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"condition_description"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"conditions"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"chronic_flags"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"chronic_flags"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"chronic_count"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"first_condition_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"first_condition_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"condition_stats"}},"column":"last_condition_date"},"to_col":{"scope":{"intra":{"node_id":"final"}},"column":"last_condition_date"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final 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materialized='view'\n    )\n}}\n\n{#\n    Patient condition history with chronic disease flags.\n\n    IMPORTANT: Chronic Condition Detection Limitations\n    --------------------------------------------------\n    The chronic condition flags (has_diabetes, has_hypertension, has_heart_disease)\n    use simplified text matching on condition_description. This is NOT clinical-grade\n    detection and should be used for analytics/reporting only, not clinical decisions.\n\n    Detection patterns:\n    - Diabetes: description contains 'diabetes'\n    - Hypertension: description contains 'hypertension' or 'high blood pressure'\n    - Heart disease: description contains 'heart disease', 'cardiac', 'coronary',\n                     or 'atrial fibrillation'\n\n    For production clinical use, consider:\n    1. Using SNOMED codes instead of text matching\n    2. Implementing ICD-10 code mappings\n    3. Consulting clinical informaticists for proper condition groupings\n    4. Moving patterns to a seed file or reference table for maintainability\n#}\n\nwith patients as (\n    select\n        patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\nconditions as (\n    select\n        patient_id\n        , condition_start_date\n        , condition_end_date\n        , condition_code\n        , condition_description\n    from {{ ref('stg_synthea__conditions') }}\n),\n\ncondition_stats as (\n    select\n        patient_id\n        , count(*) as total_conditions\n        , sum(case when condition_end_date is null then 1 else 0 end) as active_conditions\n        , min(condition_start_date) as first_condition_date\n        , max(condition_start_date) as last_condition_date\n    from conditions\n    group by patient_id\n),\n\nchronic_flags as (\n    select\n        patient_id\n        , max(case\n            when lower(condition_description) like '%diabetes%' then 1\n            else 0\n        end) as has_diabetes\n        , max(case\n            when lower(condition_description) like '%hypertension%'\n                or lower(condition_description) like '%high blood pressure%' then 1\n            else 0\n        end) as has_hypertension\n        , max(case\n            when lower(condition_description) like '%heart disease%'\n                or lower(condition_description) like '%cardiac%'\n                or lower(condition_description) like '%coronary%'\n                or lower(condition_description) like '%atrial fibrillation%' then 1\n            else 0\n        end) as has_heart_disease\n    from conditions\n    group by patient_id\n),\n\nchronic_count as (\n    select\n        patient_id\n        , has_diabetes + has_hypertension + has_heart_disease as chronic_condition_count\n    from chronic_flags\n),\n\nfinal as (\n    select\n        p.patient_id\n        , coalesce(cs.total_conditions, 0) as total_conditions\n        , coalesce(cs.active_conditions, 0) as active_conditions\n        , cs.first_condition_date\n        , cs.last_condition_date\n        , coalesce(cast(cf.has_diabetes as boolean), false) as has_diabetes\n        , coalesce(cast(cf.has_hypertension as boolean), false) as has_hypertension\n        , coalesce(cast(cf.has_heart_disease as boolean), false) as has_heart_disease\n        , coalesce(cc.chronic_condition_count, 0) as chronic_condition_count\n    from patients p\n    left join condition_stats cs on p.patient_id = cs.patient_id\n    left join chronic_flags cf on p.patient_id = cf.patient_id\n    left join chronic_count cc on p.patient_id = cc.patient_id\n)\n\nselect * from final","tests":[],"is_recursive":false,"findings":[{"check":"join.left-null-propagation","tier":"high","instrument":"both","model_id":"model.healthcare_analytics.int_patients__with_conditions","construct":"left_join[final:condition_stats]","verdict":{"status":"uncovered"},"evidence":[{"label":"left join","value":"final — LEFT JOIN condition_stats ON patients.patient_id = condition_stats.patient_id"}],"recommendation":"Add a no-match given: one left-side row whose join key is absent from the right-side given rows, then extend `expect` with that row carrying NULL right-side columns (or the intended fallback). This finding's evidence carries a copy-pasteable given sketch.","pin_node":"final","sketches":["- input: ref('dim_patients')\n  rows:\n    - {patient_id: 404}   # 404 has no match below — the no-match path\n- input: ref('stg_synthea__conditions')\n  rows:\n    - {patient_id: 1}\n# expect: the no-match row with NULL stg_synthea__conditions columns (or the intended fallback)"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body 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Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not 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tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final 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100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) 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count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. 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One row per condition instance with surrogate key.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"with_surrogate_key","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__conditions.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"with_surrogate_key","edge_type":"from"},{"from":"with_surrogate_key","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)","renamed":"renamed as (\n    select\n        -- foreign keys\n        PATIENT as patient_id\n        , ENCOUNTER as encounter_id\n\n        -- dates\n        , START as condition_start_date\n        , STOP as condition_end_date\n\n        -- condition details\n        , CODE as 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condition_end_date\n\n        -- condition details\n        , CODE as condition_code\n        , DESCRIPTION as condition_description\n    from source\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (no natural key exists)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(condition_start_date as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as condition_id\n        , *\n    from renamed\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from with_surrogate_key\n)\n\nselect * from final","node_spans":{"(final 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(cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 'valid_medication_dates' else null end\n        , \n        \n        case when not (cast(medication_start_at as date) <= current_date) then 'no_future_medication_dates' else null end\n        , \n        \n        case when not (medication_start_at >= timestamp '1900-01-01') then 'start_after_1900' else null end\n        , \n        \n        case when not (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') then 'end_after_1900_if_present' else null end\n        \n        \n    )\n as failed_dq_tests\n\n    from with_surrogate_key\n\n)","with_row_number":"with_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n)","with_surrogate_key":"with_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as 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encounter_id\n\n        -- timestamps\n        , cast(START as timestamp) as medication_start_at\n        , cast(STOP as timestamp) as medication_end_at\n\n        -- medication details\n        , CODE as medication_code\n        , DESCRIPTION as medication_description\n\n        -- costs\n        , BASE_COST as base_cost\n        , PAYER_COVERAGE as payer_coverage\n        , DISPENSES as dispenses\n        , TOTALCOST as total_cost\n\n        -- reason for medication\n        , REASONCODE as reason_code\n        , REASONDESCRIPTION as reason_description\n    from source\n),\n\nwith_row_number as (\n    select\n        *\n        , row_number() over (\n            partition by patient_id, encounter_id, medication_code, medication_start_at\n            order by medication_end_at asc nulls last, total_cost desc\n        ) as _row_num\n    from renamed\n),\n\nwith_surrogate_key as (\n    select\n        -- surrogate primary key (includes row_num for true duplicates)\n        md5(cast(coalesce(cast(patient_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(encounter_id as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_code as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(medication_start_at as TEXT), '_dbt_utils_surrogate_key_null_') || '-' || coalesce(cast(_row_num as TEXT), '_dbt_utils_surrogate_key_null_') as TEXT)) as medication_id\n        , patient_id\n        , payer_id\n        , encounter_id\n        , medication_start_at\n        , medication_end_at\n        , medication_code\n        , medication_description\n        , base_cost\n        , payer_coverage\n        , dispenses\n        , total_cost\n        , reason_code\n        , reason_description\n    from with_row_number\n),\n\nwith_dq_flags as (\n    \n    select\n        *\n\n        -- individual validation flags\n        \n        , (medication_end_at is null or medication_end_at >= medication_start_at) as valid_medication_dates\n        \n        , (cast(medication_start_at as date) <= current_date) as no_future_medication_dates\n        \n        , (medication_start_at >= timestamp '1900-01-01') as start_after_1900\n        \n        , (medication_end_at is null or medication_end_at >= timestamp '1900-01-01') as end_after_1900_if_present\n        \n\n        -- overall validity flag\n        , \n    (\n        \n        (medication_end_at is null or medication_end_at >= medication_start_at)\n         and \n        \n        (cast(medication_start_at as date) <= current_date)\n         and \n        \n        (medication_start_at >= timestamp '1900-01-01')\n         and \n        \n        (medication_end_at is null or medication_end_at >= timestamp '1900-01-01')\n        \n        \n    )\n as is_dq_valid\n\n        -- array of failed test names\n        , \n    list_value(\n        \n        case when not (medication_end_at is null or medication_end_at >= medication_start_at) then 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patient-condition combination where condition is active\n    Use cases: Patient panels, disease management dashboards, care gap analysis\n\n    Filters fct_condition_cohorts to only active conditions (no end date).\n#}\n\nwith condition_cohorts as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n        , is_active\n    from {{ ref('fct_condition_cohorts') }}\n    where is_active = true\n),\n\nfinal as (\n    select\n        patient_key\n        , patient_id\n        , condition_key\n        , condition_code\n        , condition_description\n        , first_diagnosis_date\n        , months_with_condition\n        , encounter_count\n    from condition_cohorts\n)\n\nselect * from final","tests":[],"is_recursive":false},{"name":"v_provider_active_patients","path":"models/marts/analytics/views/v_provider_active_patients.sql","description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","dag":{"nodes":[{"id":"providers","role":"import"},{"id":"current_conditions","role":"import"},{"id":"patient_recent_encounters","role":"import"},{"id":"patient_provider_assignment","role":"transform"},{"id":"active_conditions_by_provider","role":"transform"},{"id":"provider_summary","role":"transform"},{"id":"final","role":"transform"},{"id":"(final 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ppa.patient_id\n)","current_conditions":"current_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n)","final":"final as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)","patient_provider_assignment":"patient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n)","patient_recent_encounters":"patient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n)","provider_summary":"provider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n)","providers":"providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n)"},"code_map":{"compiled":"\n\n\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from \"memory\".\"main_marts\".\"dim_providers\"\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from \"memory\".\"main_marts\".\"fct_condition_cohorts\" as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, 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Grain: One row per provider\n    Use cases: Provider workload analysis, panel management, capacity planning\n\n    Aggregates patients with active conditions by their most recent provider.\n    A patient is attributed to the provider who saw them most recently for an active condition.\n#}\n\nwith providers as (\n    select\n        provider_key\n        , provider_id\n        , provider_name\n        , organization_key\n        , organization_id\n    from {{ ref('dim_providers') }}\n    where provider_key != -1\n),\n\n-- get current conditions with their most recent encounter\ncurrent_conditions as (\n    select\n        cc.patient_key\n        , cc.patient_id\n        , cc.condition_key\n        , cc.condition_code\n    from {{ ref('fct_condition_cohorts') }} as cc\n    where cc.is_active = true\n),\n\n-- get the most recent encounter per patient to determine their provider\npatient_recent_encounters as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n        , row_number() over (\n            partition by patient_id\n            order by encounter_start_at desc\n        ) as rn\n    from {{ ref('fct_encounters') }}\n    where provider_key != -1\n),\n\n-- assign patients to their most recent provider\npatient_provider_assignment as (\n    select\n        patient_id\n        , provider_key\n        , provider_id\n    from patient_recent_encounters\n    where rn = 1\n),\n\n-- join active conditions to provider assignments\nactive_conditions_by_provider as (\n    select\n        ppa.provider_key\n        , ppa.provider_id\n        , cc.patient_id\n        , cc.condition_key\n    from current_conditions as cc\n    inner join patient_provider_assignment as ppa\n        on cc.patient_id = ppa.patient_id\n),\n\n-- aggregate by provider\nprovider_summary as (\n    select\n        provider_key\n        , provider_id\n        , count(distinct patient_id) as active_patient_count\n        , count(*) as total_active_conditions\n    from active_conditions_by_provider\n    group by provider_key, provider_id\n),\n\nfinal as (\n    select\n        p.provider_key\n        , p.provider_id\n        , p.provider_name\n        , p.organization_key\n        , p.organization_id\n        , coalesce(ps.active_patient_count, 0) as active_patient_count\n        , coalesce(ps.total_active_conditions, 0) as total_active_conditions\n        , case\n            when coalesce(ps.active_patient_count, 0) > 0\n            then round(\n                cast(ps.total_active_conditions as decimal) / ps.active_patient_count,\n                2\n            )\n            else 0\n        end as avg_conditions_per_patient\n    from providers as p\n    left join provider_summary as ps\n        on p.provider_key = ps.provider_key\n)\n\nselect * from final","tests":[],"is_recursive":false}],"manifest_nodes":{"dim_conditions":{"description":"Master condition dimension for healthcare analytics.\nOne row per unique ICD/SNOMED condition code, deduplicated from staging.\nUse for filtering and grouping by condition across fact tables.\n\nUse cases:\n- Condition-based cohort analysis\n- Disease prevalence tracking\n- Condition frequency and distribution reporting\n- Join target for condition-related facts\n","materialized":"table","tags":["analytics","marts","dimension"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED code (natural key)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name/description","tests":[{"name":"not null"}]},{"name":"condition_key","description":"Surrogate key (row_number)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_count","description":"Total number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_occurrence_date","description":"Earliest date this condition was diagnosed in the dataset"},{"name":"last_occurrence_date","description":"Most recent date this condition was diagnosed"},{"name":"patient_count","description":"Number of unique patients with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_organizations":{"description":"Organization dimension containing healthcare facilities. Includes an unknown member row (organization_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Natural organization identifier (UUID)","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_patients":{"description":"Patient dimension with SCD Type 2 history tracking. Each row represents a specific version of a patient's record. Includes an unknown member row (patient_key = -1) for handling missing lookups.\nSCD Type 2 columns: patient_key (surrogate), patient_id (natural), valid_from, valid_to, is_current.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"age_years","description":"Current age or age at death (in years)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"full_name","description":"Patient's full name (first + last)","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_current","description":"Boolean flag indicating if this is the current version","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_deceased","description":"Boolean flag indicating if patient has died","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"patient_id","description":"Natural patient identifier (UUID), same across SCD2 versions","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Surrogate key (unique per version, -1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"state","description":"State of residence (full state name)"},{"name":"valid_from","description":"Date when this SCD2 version became active","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"valid_to","description":"Date when this SCD2 version was superseded (null = current)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_providers":{"description":"Provider dimension containing healthcare providers (doctors, nurses, etc.). 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identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_condition_cohorts":{"description":"Patient-condition cohort analysis for disease management and population health.\nOne row per unique patient-condition combination with timeline and encounter details.\nGrain: (patient_id, condition_code)\n\nUse cases:\n- Disease management program enrollment\n- Cohort identification for clinical trials\n- Chronic condition tracking\n- Care gap analysis\n- Risk stratification by condition\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_code","description":"ICD/SNOMED condition code","tests":[{"name":"not null"}]},{"name":"condition_cohort_key","description":"Surrogate key (MD5 hash of patient_id + condition_code)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"first_diagnosis_date","description":"Date of first diagnosis","tests":[{"name":"not null"}]},{"name":"first_diagnosis_year","description":"Year of first diagnosis","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"is_active","description":"True if condition has no end date (currently active)","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_diagnosis_date","description":"Date of most recent diagnosis (null if first occurrence only)"},{"name":"months_with_condition","description":"Months between first and last diagnosis (0 if single occurrence)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_cost_analysis":{"description":"Detailed cost breakdown for financial analysis and revenue cycle management.\nOne row per encounter with cost allocation details across payer and patient.\nGrain: encounter_id\n\nUse cases:\n- Financial performance reporting\n- Payer mix analysis\n- Patient out-of-pocket tracking\n- Cost driver identification\n- Revenue cycle optimization\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"cost_analysis_key","description":"Surrogate key (MD5 hash of encounter_id)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"cost_per_procedure","description":"Average cost per procedure (null if no procedures)"},{"name":"coverage_pct","description":"Percentage covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, etc.)"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_date_key","description":"Foreign key to dim_date"},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"patient_cost_pct","description":"Percentage owed by patient (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"},{"name":"patient_responsibility","description":"Amount owed by patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier"},{"name":"payer_key","description":"Foreign key to dim_payers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"procedure_count","description":"Number of procedures in the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"provider_id","description":"Natural provider identifier"},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"total_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]},"fct_encounters":{"description":"Encounter fact table containing healthcare visits/encounters. Links to patient, provider, organization, and payer dimensions. Includes date keys, costs, and derived duration.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"duration_minutes","description":"Duration of encounter in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_end_date_key","description":"Foreign key to dim_date for encounter end"},{"name":"encounter_id","description":"Natural encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_key","description":"Surrogate key for the encounter","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_start_date_key","description":"Foreign key to dim_date for encounter start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"organization_id","description":"Natural organization identifier","tests":[{"name":"not null"}]},{"name":"organization_key","description":"Foreign key to dim_organizations (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_organizations.organization_key"}]},{"name":"patient_age_at_encounter","description":"Patient's age at the time of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount patient is responsible for (total_claim_cost - payer_coverage)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Natural payer identifier","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Foreign key to dim_payers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_payers.payer_key"}]},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"fct_encounters_monthly":{"description":"Monthly aggregated encounter statistics by encounter class. Provides counts, costs, and patient metrics grouped by year-month and encounter type.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month","description":"Year-month string (YYYY-MM format)","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_yearly":{"description":"Yearly aggregated encounter statistics by encounter class. Provides counts, costs, patient metrics, and encounters per patient ratio.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient","description":"Average number of encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_claim_cost","description":"Sum of total claim costs","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_patient_responsibility","description":"Sum of patient responsibility amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Sum of payer coverage amounts","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patients","description":"Number of unique patients with encounters","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_patient_summary":{"description":"Annual patient-level aggregations for outcomes tracking and population health analysis.\nOne row per patient per calendar year with comprehensive utilization and cost metrics.\nGrain: (patient_id, year_actual)\n\nUse cases:\n- Patient outcomes tracking over time\n- Annual cost summaries and trends\n- Cohort analysis and patient stratification\n- Health trajectory visualization\n- High-cost patient identification\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"condition_count","description":"Number of distinct conditions diagnosed in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_age_at_year_end","description":"Patient age as of December 31 of the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"patient_responsibility","description":"Amount owed by patient (copay + coinsurance)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_summary_key","description":"Surrogate key (MD5 hash of patient_id + year_actual)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_coverage_pct","description":"Percentage of costs covered by insurance (0-100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"procedure_count","description":"Number of procedures in the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_cost","description":"Total claim cost for the year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_organizations","description":"Number of distinct organizations visited","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_providers","description":"Number of distinct providers seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year of the summary","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_metrics":{"description":"Monthly provider-level utilization and performance metrics.\nOne row per provider per calendar month with patient volumes, costs, and efficiency metrics.\nGrain: (provider_id, year_actual, month_actual)\n\nUse cases:\n- Provider performance tracking\n- Workload distribution analysis\n- Cost efficiency comparisons\n- Capacity planning and staffing\n- Quality improvement initiatives\n","materialized":"table","tags":["analytics","marts","fact"],"columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"avg_cost_per_encounter","description":"Average cost per encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_encounter_duration_minutes","description":"Average encounter duration in minutes","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_patient_age","description":"Average age of patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_count","description":"Number of encounters in the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounters_per_patient_avg","description":"Average encounters per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"month_actual","description":"Calendar month (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_providers.provider_key"}]},{"name":"provider_metrics_key","description":"Surrogate key (MD5 hash of provider_id + year_month_str)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_cost","description":"Total claim cost for the month","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_payer_coverage","description":"Amount covered by insurance","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"total_procedures","description":"Total procedures performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"unique_patient_count","description":"Number of distinct patients seen","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"year_actual","description":"Calendar year","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_month_str","description":"Year-month string (YYYY-MM)"}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"fct_provider_scorecard":{"materialized":"table","tags":["analytics","marts","fact"]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at <= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at <= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_dq_quarantine__medications":{"description":"Medications that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_medication_dates: medication_end_at is null or medication_end_at >= medication_start_at\n- no_future_medication_dates: cast(medication_start_at as date) <= current_date\n- start_after_1900: medication_start_at >= timestamp '1900-01-01'\n- end_after_1900_if_present: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"end_after_1900_if_present","description":"Flag: medication_end_at is null or medication_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"no_future_medication_dates","description":"Flag: cast(medication_start_at as date) <= current_date"},{"name":"start_after_1900","description":"Flag: medication_start_at >= timestamp '1900-01-01'"},{"name":"valid_medication_dates","description":"Flag: medication_end_at is null or medication_end_at >= medication_start_at"}]},"int_encounters__enriched":{"description":"Enriched encounter model that aggregates clinical event counts and costs per encounter. Joins conditions, medications, and procedures to provide a complete view of each encounter's clinical activity and associated costs.\n","materialized":"view","columns":[{"name":"condition_count","description":"Number of conditions diagnosed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID), sourced from fct_encounters","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_count","description":"Number of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_count","description":"Number of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_condition_cost","description":"Total cost associated with conditions (currently always 0 per model logic)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_cost","description":"Total cost of all clinical events (conditions + medications + procedures). Note: Condition costs are currently set to 0 in the model.\n","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_event_count","description":"Total count of all clinical events (conditions + medications + procedures)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_medication_cost","description":"Total cost of medications prescribed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"total_procedure_cost","description":"Total cost of procedures performed during this encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"int_patients__with_conditions":{"description":"Patient-level condition history with aggregated statistics and chronic disease flags. Provides counts of total and active conditions, condition date ranges, and boolean flags for common chronic conditions (diabetes, hypertension, heart disease).\nIMPORTANT: Chronic condition flags use simplified text matching on condition descriptions. This is for analytics/reporting only, not clinical decisions.\n","materialized":"view","columns":[{"name":"active_conditions","description":"Number of currently active conditions (no end date)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"chronic_condition_count","description":"Count of tracked chronic conditions (0-3, based on diabetes + hypertension + heart disease flags)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"first_condition_date","description":"Date of earliest condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"has_diabetes","description":"Boolean flag indicating if patient has any diabetes-related condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_heart_disease","description":"Boolean flag indicating if patient has any heart disease condition","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"has_hypertension","description":"Boolean flag indicating if patient has hypertension or high blood pressure","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"last_condition_date","description":"Date of most recent condition diagnosis for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"patient_id","description":"Unique patient identifier (UUID), sourced from current dim_patients records","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"total_conditions","description":"Total number of conditions ever diagnosed for this patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__conditions":{"description":"Staging model for patient conditions/diagnoses. One row per condition instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"condition_code","description":"SNOMED CT code for the condition","tests":[{"name":"not null"}]},{"name":"condition_description","description":"Description of the condition","tests":[{"name":"not null"}]},{"name":"condition_end_date","description":"Date when condition resolved (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"condition_id","description":"Surrogate key for the condition record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"condition_start_date","description":"Date when condition was diagnosed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__observations":{"description":"Staging model for clinical observations (vital signs, lab results). One row per observation with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"encounter_id","description":"Foreign key to the encounter. Note: ~10% of observations have null encounter_id (from external sources or historical data imports).\n","tests":[{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"observation_at","description":"Timestamp when observation was recorded","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"observation_code","description":"LOINC code for the observation","tests":[{"name":"not null"}]},{"name":"observation_description","description":"Description of the observation","tests":[{"name":"not null"}]},{"name":"observation_id","description":"Surrogate key for the observation record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"observation_type","description":"Type of observation (numeric, text, etc.)"},{"name":"observation_value","description":"Value of the observation (stored as string)"},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"units","description":"Units of measurement"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__organizations":{"description":"Staging model for healthcare organizations/facilities. One row per organization with location and performance metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Unique organization identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"organization_name","description":"Name of the healthcare organization","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__procedures":{"description":"Staging model for medical procedures performed. One row per procedure instance with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost of the procedure","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"procedure_at","description":"Timestamp when the procedure was performed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"procedure_code","description":"SNOMED CT code for the procedure","tests":[{"name":"not null"}]},{"name":"procedure_description","description":"Description of the procedure","tests":[{"name":"not null"}]},{"name":"procedure_id","description":"Surrogate key for the procedure record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of the procedure"},{"name":"reason_description","description":"Description of the reason for the procedure"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__providers":{"description":"Staging model for healthcare providers (doctors, nurses, etc.). One row per provider with specialization and affiliated organization.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"city","description":"City"},{"name":"gender","description":"Provider gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate"},{"name":"longitude","description":"Longitude coordinate"},{"name":"organization_id","description":"Foreign key to the provider's organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"provider_id","description":"Unique provider identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider","tests":[{"name":"not null"}]},{"name":"specialty","description":"Medical specialty"},{"name":"state","description":"State (full state name)"},{"name":"utilization","description":"Total utilization count","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"v_encounter_summary":{"description":"Enriched encounter view with dimension attributes for ad-hoc analysis.\nEphemeral (not persisted) - used as a building block for other queries.\nJoins encounters with patient, provider, organization, and payer details.\n\nUse cases:\n- Ad-hoc encounter analysis\n- Building block for custom reports\n- Quick encounter lookups with full context\n","materialized":"ephemeral","tags":["analytics","marts","view"],"columns":[{"name":"encounter_class","description":"Type of encounter"},{"name":"encounter_date","description":"Date of the encounter"},{"name":"encounter_id","description":"Unique encounter identifier"},{"name":"organization_id","description":"Organization identifier"},{"name":"organization_name","description":"Organization name"},{"name":"patient_id","description":"Patient identifier"},{"name":"patient_name","description":"Full patient name"},{"name":"patient_responsibility","description":"Amount owed by patient"},{"name":"payer_coverage","description":"Amount covered by insurance"},{"name":"payer_id","description":"Payer identifier"},{"name":"payer_name","description":"Payer name"},{"name":"provider_id","description":"Provider identifier"},{"name":"provider_name","description":"Full provider name"},{"name":"total_cost","description":"Total claim cost"}]},"v_patient_current_conditions":{"description":"Current (active) conditions for each patient, filtered from fct_condition_cohorts.\nUse for patient panels, disease management dashboards, and care gap identification.\nGrain: (patient_id, condition_code) - only active conditions\n\nUse cases:\n- Patient panel management\n- Active disease registries\n- Care gap identification\n- Real-time patient condition views\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"condition_code","description":"ICD/SNOMED condition code"},{"name":"condition_description","description":"Full condition name"},{"name":"condition_key","description":"Foreign key to dim_conditions","tests":[{"name":"relationships","detail":"dim_conditions.condition_key"}]},{"name":"encounter_count","description":"Number of encounters with this condition"},{"name":"first_diagnosis_date","description":"Date of first diagnosis"},{"name":"months_with_condition","description":"Duration of condition in months"},{"name":"patient_id","description":"Natural patient identifier"},{"name":"patient_key","description":"Foreign key to dim_patients"}]},"v_provider_active_patients":{"description":"Active patient count per provider for panel management and workload analysis.\nAggregates patients with active conditions by their treating provider.\nGrain: provider_id\n\nUse cases:\n- Provider panel size management\n- Workload distribution analysis\n- Capacity planning\n- Provider assignment optimization\n","materialized":"table","tags":["analytics","marts","view"],"columns":[{"name":"active_patient_count","description":"Number of patients with active conditions","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"avg_conditions_per_patient","description":"Average number of active conditions per patient","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"organization_id","description":"Natural organization identifier"},{"name":"organization_key","description":"Foreign key to dim_organizations"},{"name":"provider_id","description":"Natural provider identifier","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_key","description":"Foreign key to dim_providers","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"provider_name","description":"Full name of the provider"},{"name":"total_active_conditions","description":"Total number of active conditions managed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"join.left-null-propagation":{"name":"LEFT JOIN null propagation untested","group":"join","tier":"high","instrument":"both","conditions":["the model LEFT-JOINs a relation and right-side columns provably reach the containing SELECT's projection: a direct `\u003cright>.\u003ccolumn>` item, a `\u003cright>.*` qualified wildcard, or a bare `*`","satisfaction: some unit test's literal givens carry a left-side row whose ON equi-key has no match among the right-side given rows — cells compare on the value-normalized equality key, and a left row whose key cell is NULL or absent never matches (SQL join semantics), so it exercises the no-match path","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind to givens directly by external leaf name or through an upstream closure of simple-FROM CTEs reading exactly one external relation","instrument routing (catalog C4/C10): when the SELECT containing the LEFT JOIN dedups its output with DISTINCT — the dedup-after-fan-out signal — the data-test recommendation wins: prove the right key's grain with a uniqueness data test at the source instead of adding fixtures","verdict order: any test exercising an unmatched left row makes the construct COVERED with attribution; otherwise any statically-unattributable binding or given makes it UNKNOWN; otherwise UNCOVERED"],"exclusions":["right-side columns reaching the output only through expressions (COALESCE, CASE, function calls) are not attributed — the construct stays silent unless a direct right-qualified item or wildcard projects (conservative: never a false fire)","non-equi or non-column ON predicates, USING / NATURAL constraints, and unqualified key columns leave the join key statically unrecoverable — verdict UNKNOWN, never UNCOVERED","a derived-table join side emits no fact; a CTE side whose upstream closure is not a single-external chain of simple-FROM CTEs is UNKNOWN, never UNCOVERED","external `fixture:` files and non-literal `format: sql` givens make a test statically uncountable — UNKNOWN, never UNCOVERED","an ungiven seed-side input reads real seed data — UNKNOWN, never UNCOVERED"],"rationale":"A LEFT JOIN whose right-side columns reach the output propagates NULLs on every unmatched left row — the most common real dbt unit-test catch. When every left given row has a right match, the no-match path runs on zero rows in every test, so an unhandled NULL (or a wrong fallback) ships silently.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.left-null-propagation.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. 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+
+</style>
+<style>
+
+/* ============================================================================
+   cute-dbt design tokens v1 (cute-dbt#242)
+   ----------------------------------------------------------------------------
+   The design-system ROOT: the semantic token layer + ALL EIGHT [data-theme]
+   blocks, extracted VERBATIM from templates/report.css (the cute-dbt#177
+   chassis) at cute-dbt#242 — exact current values, zero value changes.
+   Shared by BOTH page families through an askama include (report.html and
+   explore-dag.html / explore-tests.html each inline this partial into their
+   own <style> block at compile time), so the report and the explore pages
+   consume ONE design system structurally. Single-file outputs hold: the
+   sharing happens at the template layer, never via a runtime asset.
+
+   FIRST-PARTY asset — authored for cute-dbt. Deliberately NOT listed in
+   `assets/MANIFEST.toml` (that index is third-party vendoring provenance);
+   its integrity gates are the banner-pin + comment-balance tests in
+   `src/adapters/asset_embed.rs`.
+
+   Design sessions: treat THIS file as the design-system root (see
+   DESIGN.md). Tokens are law — no new color literals; per-theme,
+   surface-scoped AA overrides live with their surfaces in the page
+   layers, never here.
+
+   PORTING-BUG GUARD (handoff §2.1): never write a `*` immediately followed
+   by `/` inside a comment body — it closes the comment early and silently
+   eats the next rule. A comment-balance test pins this.
+   ========================================================================== */
+
+/* ============================================================================
+   AA pins — do not re-brighten
+   ----------------------------------------------------------------------------
+   Every theme passes WCAG-AA (>= 4.5:1) on the load-bearing diff add/remove
+   pair (pass-2 handoff §2.3, re-pinned for 8 themes at cute-dbt#198). These
+   contrast ratios are verified and LOAD-BEARING; do not re-brighten any
+   theme's add/remove colors. Re-run the WCAG math on the diff add/remove
+   pair for every theme if any code-surface color changes.
+
+   | theme     | add-fg on surface | remove-fg on surface | white on add-chip | white on remove-chip |
+   |-----------|-------------------|----------------------|-------------------|----------------------|
+   | Light     | 5.02              | 4.83                 | 5.02              | 4.83                 |
+   | Solarized | 6.10              | 5.68                 | 4.81              | 5.41                 |
+   | Latte     | 5.42              | 5.63                 | 4.98              | 6.01                 |
+   | Rosé Pine | 5.37              | 6.24                 | 5.31              | 6.36                 |
+   | Dark      | 9.65              | 7.52                 | 5.02              | 4.58                 |
+   | Tokyo     | 8.81              | 6.08                 | 5.22              | 4.99                 |
+   | Gruvbox   | 6.74              | 4.85                 | 4.86              | 5.47                 |
+   | Dracula   | 9.83              | 4.95                 | 4.96              | 5.03                 |
+
+   cute-dbt#251 — the per-theme AA token repair (the #206 deepening
+   pattern, applied at TOKEN level — since #264 the tokens live in ONE
+   place, so deepening here repairs the report AND explore pages
+   simultaneously; the older surface-scoped overrides in report.css
+   predate this and stay). The #264 adversarial verification measured
+   the shared palette sub-AA on 5 of 8 themes; the values below were
+   adjusted minimally (lightness-only shifts, hue/saturation kept) so
+   EVERY combo clears 4.5:1 — pinned by the report + explore AA matrix
+   guards in tests/headless_toggle.rs (universal: all 8 themes,
+   effective composited backdrops). Do not re-brighten:
+     · --text-muted on the worst of bg/bg-alt/surface/surface-2/
+       code-header-bg/code-bg (solarized 4.58, latte 5.14,
+       rosepine 4.82, tokyo 4.58, gruvbox 4.59);
+     · --accent (and --accent-hover) on the worst of bg/bg-alt/surface
+       (accent: solarized 4.57, latte 4.77, rosepine 4.59; hover:
+       latte 4.55, rosepine 4.56);
+     · --code-add/rem-sigil on their composited add/rem rows (all
+       themes >= 4.55 at FULL opacity — `.diff-sigil` opacity dimming
+       was removed at #251 per the #227 composited-opacity rule);
+     · body --text on --code-add/rem-word composited over row + code-bg
+       (all themes >= 4.56; the dark themes' word-tint alphas were
+       lowered, the light themes' rem-word hexes lightened).
+   Latte/rosepine muted + latte accent adopt the themes' OWN deeper
+   steps already established by the #198/#227 scoped stand-ins
+   (Catppuccin subtext1 #5c5f77, latte blue #1a5cdd, rosepine #5d5780).
+   Light and Dark are untouched (they already passed everywhere).
+   ========================================================================== */
+
+/* ============================================================================
+   1. TOKEN LAYER  (engine/tokens.css)
+   ----------------------------------------------------------------------------
+   Semantic design tokens (the ~50 hardcoded report hexes collapsed to ~24
+   themeable tokens) + a density layer (set by the *direction* stylesheet, not
+   the theme) + eight [data-theme] blocks.
+
+   TWO ORTHOGONAL AXES
+     · THEME  (data-theme on <html>): colour identity. 8 of them.
+     · DIRECTION (a separate stylesheet): density + feel. 4 of them.
+
+   Theme families (kept in lockstep with the settings-panel theme grid):
+     · light : Light, Solarized, Catppuccin Latte, Rosé Pine Dawn
+     · dark  : Dark, Tokyo Night, Gruvbox, Dracula
+
+   FIXED-ACROSS-THEMES (intentionally NOT overridden per theme):
+     · the DAG role/edge palette (Okabe-Ito, colourblind-safe) — handoff §5.
+     · the diff "strong marker" chips (white-on-fill, AA everywhere).
+   Everything else themes.
+   ========================================================================== */
+
+:root {
+  /* ---- surfaces ------------------------------------------------------- */
+  --bg:            #ffffff;   /* page */
+  --bg-alt:        #f5f6f8;   /* code blocks, callouts, sunken wells */
+  --surface:       #ffffff;   /* cards / panels */
+  --surface-2:     #f1f2f4;   /* control chrome, switch tracks */
+
+  /* ---- text ----------------------------------------------------------- */
+  --text:          #33363d;   /* body */
+  --text-muted:    #6a6e78;   /* labels, hints, captions */
+  --text-faint:    #9aa0ab;   /* absent cells, disabled */
+
+  /* ---- lines ---------------------------------------------------------- */
+  --border:        #d9dce1;   /* dividers, inputs, panels */
+  --border-strong: #b9bdc6;   /* emphasised dividers */
+
+  /* ---- accent (themes + Tweakable) ------------------------------------ */
+  --accent:         #1d7484;  /* links, focus, active control, table rule */
+  --accent-hover:   #155c69;
+  --accent-tint:    #e3f0f2;  /* faint accent wash */
+  --accent-on:      #ffffff;  /* text on a solid accent fill */
+
+  /* ---- control chrome (segmented toggles, settings switches) ---------- */
+  --control-bg:        var(--surface-2);
+  --control-fg:        var(--text);
+  --control-border:    var(--border);
+  --control-active-bg: #2a2d34;
+  --control-active-fg: #ffffff;
+
+  /* ---- diff: text-on-surface (LOAD-BEARING, AA per theme) ------------- */
+  --diff-add-fg:    #15803d;  /* "new" scalar text */
+  --diff-remove-fg: #dc2626;  /* "old" scalar text */
+  --diff-arrow:     var(--text-muted);
+  --diff-add-row:    rgba(46, 160, 67, 0.07);   /* whole-row tint */
+  --diff-remove-row: rgba(244, 63, 94, 0.07);
+  --diff-add-edge:    rgba(46, 160, 67, 0.85);  /* inset left accent */
+  --diff-remove-edge: rgba(244, 63, 94, 0.85);
+
+  /* ---- diff: strong marker chip (CONSTANT — white-on-fill, AA) -------- */
+  --diff-add-chip:    #15803d;  /* green fill, white text ≈ 5.0:1   */
+  --diff-remove-chip: #dc2626;  /* red fill,   white text ≈ 4.8:1   */
+  --diff-chip-on:     #ffffff;
+  --col-add-chip:     #a6f0b8;  --col-add-on:    #04260f;
+  --col-remove-chip:  #ffabd6;  --col-remove-on: #26000a;
+
+  /* ---- badges / incremental ------------------------------------------ */
+  --badge-amber:    #b07400;  /* incremental affordances */
+  --badge-amber-bg: #fff8e1;
+  --badge-amber-edge: #E69F00;
+
+  /* ---- syntax (cool/neutral; never red/green) ------------------------ */
+  --syntax-keyword: #4338ca;
+  --syntax-string:  #0e7490;
+  --syntax-comment: #555a66;
+  --syntax-jinja:   #7c3aed;
+  --syntax-fold:    #4338ca;
+  --syntax-fold-bg: rgba(67, 56, 202, 0.07);
+
+  /* ---- code surface (GitHub-grade frame + unified diff) -------------- */
+  --code-bg:          #ffffff;   /* the code canvas */
+  --code-header-bg:   #f6f8fa;   /* toolbar strip + file header */
+  --code-gutter-bg:   #ffffff;   /* line-number rail (plain blocks) */
+  --code-gutter-fg:   #9aa0ab;   /* line numbers */
+  --code-line-hover:  rgba(27,31,36,0.035);
+  /* unified-diff rows (full-bleed tint) + their gutter rail + word-emphasis */
+  --code-add-bg:        #e6ffec;  --code-add-gutter: #ccffd6;  --code-add-word: #abf2bc;
+  --code-rem-bg:        #ffebe9;  --code-rem-gutter: #ffd7d5;  --code-rem-word: #ff9c98;
+  --code-hunk-bg:       #ddf4ff;  --code-hunk-fg:    #4b5563;  /* fold / "show N" rows */
+  --code-add-sigil:     #1a7f37;  --code-rem-sigil:  #cf222e;
+
+  /* ---- feedback ------------------------------------------------------- */
+  --warn:     #a06320;
+  --error:    #b3261e;
+  --error-bg: #fdecea;
+
+  /* ---- tooltip (dark bubble, constant) ------------------------------- */
+  --tooltip-bg: #1d1d22;
+  --tooltip-fg: #f5f5f7;
+
+  /* ====================================================================
+     FIXED DAG PALETTE — identical in every theme (handoff §5).
+     The DAG must read the same colourblind-safe way regardless of skin.
+     ==================================================================== */
+  --dag-import-fill:   #e8f1f8;  --dag-import-stroke:   #0072B2;
+  --dag-transform-fill:#f4f4f5;  --dag-transform-stroke:#8a8a90;
+  --dag-final-fill:    #fdf2dc;  --dag-final-stroke:    #E69F00;
+  --dag-node-text:     #1c1c1f;
+  --dag-selected:      #E91E63;  /* pink, reserved for selection only */
+  /* edge types (Okabe-Ito) */
+  --edge-from:  #1c1c1f;  --edge-inner:  #009E73;  --edge-left: #0072B2;
+  --edge-right: #56B4E9;  --edge-cross:  #CC79A7;  --edge-full: #982c61;
+  --edge-union-all: #E69F00;  --edge-union-distinct: #0072B2;
+
+  /* ---- DENSITY (defaults; the DIRECTION stylesheet overrides these) --- */
+  --fs-base:   13px;
+  --lh-base:   1.5;
+  --fs-label:  10.5px;
+  --pad-cell:  0.2rem 0.5rem;
+  --pad-panel: 1rem;
+  --gap-section: 1rem;
+  --radius-sm:  4px;
+  --radius-pan: 6px;
+  --rule-w:     1px;        /* hairline weight */
+  --font-ui:   system-ui, -apple-system, "Segoe UI", Roboto, sans-serif;
+  --font-mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, monospace;
+  --label-font: var(--font-ui);   /* directions can flip labels to mono */
+  --label-tracking: 0.06em;
+}
+
+/* ==========================================================================
+   THEME · DARK  — neutral, high-contrast
+   ========================================================================== */
+:root[data-theme="dark"] {
+  --bg:#15171c; --bg-alt:#1b1e25; --surface:#191c22; --surface-2:#23262e;
+  --text:#d7dae1; --text-muted:#9098a4; --text-faint:#646b77;
+  --border:#2e323b; --border-strong:#3c4150;
+  --accent:#5fb3c4; --accent-hover:#85cad9; --accent-tint:#16323a; --accent-on:#08191d;
+  --control-bg:#23262e; --control-active-bg:#5fb3c4; --control-active-fg:#08191d;
+  --diff-add-fg:#5fd99a; --diff-remove-fg:#ff8a8a;
+  --diff-add-row:rgba(95,217,154,0.10); --diff-remove-row:rgba(255,138,138,0.10);
+  --diff-add-edge:rgba(95,217,154,0.85); --diff-remove-edge:rgba(255,138,138,0.85);
+  --diff-add-chip:#15803d; --diff-remove-chip:#d83a3a;
+  --col-add-chip:#1f8a4c; --col-add-on:#eafff1; --col-remove-chip:#b3344e; --col-remove-on:#ffe9f1;
+  --badge-amber:#e0a93a; --badge-amber-bg:#2a2310; --badge-amber-edge:#e0a93a;
+  --syntax-keyword:#9aa6ff; --syntax-string:#56c8d8; --syntax-comment:#7e8595; --syntax-jinja:#c8a6ff;
+  --syntax-fold:#9aa6ff; --syntax-fold-bg:rgba(154,166,255,0.10);
+  --warn:#e0a93a; --error:#ff8a8a; --error-bg:#2a1414;
+  --tooltip-bg:#000000; --tooltip-fg:#f5f5f7;
+  --code-bg:#0d1117; --code-header-bg:#161b22; --code-gutter-bg:#0d1117; --code-gutter-fg:#6e7681;
+  --code-line-hover:rgba(255,255,255,0.04);
+  --code-add-bg:rgba(46,160,67,0.15); --code-add-gutter:rgba(46,160,67,0.30); --code-add-word:rgba(46,160,67,0.40);
+  --code-rem-bg:rgba(248,81,73,0.15); --code-rem-gutter:rgba(248,81,73,0.30); --code-rem-word:rgba(248,81,73,0.40);
+  --code-hunk-bg:rgba(56,139,253,0.15); --code-hunk-fg:#79c0ff;
+  --code-add-sigil:#3fb950; --code-rem-sigil:#f85149;
+}
+
+/* ==========================================================================
+   THEME · TOKYO NIGHT  — cool blue-purple
+   ========================================================================== */
+:root[data-theme="tokyo"] {
+  --bg:#1a1b26; --bg-alt:#1f2335; --surface:#1e2030; --surface-2:#292e42;
+  --text:#c0caf5; --text-muted:#8e95ba; --text-faint:#565f89;
+  --border:#2c3150; --border-strong:#3b4261;
+  --accent:#7aa2f7; --accent-hover:#9cb8fb; --accent-tint:#212d4f; --accent-on:#0d1020;
+  --control-bg:#292e42; --control-active-bg:#7aa2f7; --control-active-fg:#0d1020;
+  --diff-add-fg:#9ece6a; --diff-remove-fg:#f7768e;
+  --diff-add-row:rgba(158,206,106,0.10); --diff-remove-row:rgba(247,118,142,0.10);
+  --diff-add-edge:rgba(158,206,106,0.85); --diff-remove-edge:rgba(247,118,142,0.85);
+  --diff-add-chip:#3d7a2e; --diff-remove-chip:#c14358;
+  --col-add-chip:#3d7a2e; --col-add-on:#eafff1; --col-remove-chip:#a83b54; --col-remove-on:#ffe9f1;
+  --badge-amber:#e0af68; --badge-amber-bg:#2a2417; --badge-amber-edge:#e0af68;
+  --syntax-keyword:#bb9af7; --syntax-string:#7dcfff; --syntax-comment:#565f89; --syntax-jinja:#ff9e64;
+  --syntax-fold:#bb9af7; --syntax-fold-bg:rgba(187,154,247,0.10);
+  --warn:#e0af68; --error:#f7768e; --error-bg:#2d1620;
+  --tooltip-bg:#0d1020; --tooltip-fg:#c0caf5;
+  --code-bg:#1a1b26; --code-header-bg:#1f2335; --code-gutter-bg:#1a1b26; --code-gutter-fg:#565f89;
+  --code-line-hover:rgba(255,255,255,0.04);
+  --code-add-bg:rgba(158,206,106,0.15); --code-add-gutter:rgba(158,206,106,0.28); --code-add-word:rgba(158,206,106,0.24);
+  --code-rem-bg:rgba(247,118,142,0.15); --code-rem-gutter:rgba(247,118,142,0.28); --code-rem-word:rgba(247,118,142,0.35);
+  --code-hunk-bg:rgba(122,162,247,0.15); --code-hunk-fg:#7aa2f7;
+  --code-add-sigil:#9ece6a; --code-rem-sigil:#f7768e;
+}
+
+/* ==========================================================================
+   THEME · SOLARIZED  — warm cream (the "warm" slot)
+   ========================================================================== */
+:root[data-theme="solarized"] {
+  --bg:#fdf6e3; --bg-alt:#f3ecd6; --surface:#fdf6e3; --surface-2:#eee8d5;
+  --text:#4f5b62; --text-muted:#566b72; --text-faint:#9aa8ab;
+  --border:#ddd6c1; --border-strong:#c6bfa8;
+  --accent:#1d6fa8; --accent-hover:#155a8a; --accent-tint:#e3eedd; --accent-on:#ffffff;
+  --control-bg:#eee8d5; --control-active-bg:#586e75; --control-active-fg:#fdf6e3;
+  --diff-add-fg:#4a6600; --diff-remove-fg:#b82d18;
+  --diff-add-row:rgba(133,153,0,0.12); --diff-remove-row:rgba(220,50,47,0.10);
+  --diff-add-edge:rgba(110,130,0,0.85); --diff-remove-edge:rgba(220,50,47,0.85);
+  --diff-add-chip:#5a7d04; --diff-remove-chip:#c5341c;
+  --col-add-chip:#cfe08a; --col-add-on:#1f2b00; --col-remove-chip:#f4b8c4; --col-remove-on:#3a0008;
+  --badge-amber:#b58900; --badge-amber-bg:#f3e9c8; --badge-amber-edge:#b58900;
+  --syntax-keyword:#6c71c4; --syntax-string:#2aa198; --syntax-comment:#93a1a1; --syntax-jinja:#d33682;
+  --syntax-fold:#6c71c4; --syntax-fold-bg:rgba(108,113,196,0.10);
+  --warn:#b58900; --error:#dc322f; --error-bg:#f7e2dd;
+  --tooltip-bg:#073642; --tooltip-fg:#fdf6e3;
+  --code-bg:#fdf6e3; --code-header-bg:#f3ecd6; --code-gutter-bg:#fdf6e3; --code-gutter-fg:#93a1a1;
+  --code-line-hover:rgba(0,43,54,0.035);
+  --code-add-bg:#eef3d4; --code-add-gutter:#dde6b0; --code-add-word:#c8d98a;
+  --code-rem-bg:#fbe3da; --code-rem-gutter:#f5cdbf; --code-rem-word:#f4c8b8;
+  --code-hunk-bg:#e3ecdd; --code-hunk-fg:#586e75;
+  --code-add-sigil:#647300; --code-rem-sigil:#c82522;
+}
+
+/* ==========================================================================
+   THEME · GRUVBOX  — warm retro-dark (5th pick)
+   ========================================================================== */
+:root[data-theme="gruvbox"] {
+  --bg:#282828; --bg-alt:#32302f; --surface:#2e2c2a; --surface-2:#3c3836;
+  --text:#ebdbb2; --text-muted:#afa18d; --text-faint:#7c6f64;
+  --border:#3c3836; --border-strong:#504945;
+  --accent:#83a598; --accent-hover:#a3c4b7; --accent-tint:#2c3a36; --accent-on:#1d2021;
+  --control-bg:#3c3836; --control-active-bg:#83a598; --control-active-fg:#1d2021;
+  --diff-add-fg:#b8bb26; --diff-remove-fg:#fb6a5d;
+  --diff-add-row:rgba(184,187,38,0.10); --diff-remove-row:rgba(251,106,93,0.10);
+  --diff-add-edge:rgba(184,187,38,0.85); --diff-remove-edge:rgba(251,106,93,0.85);
+  --diff-add-chip:#79740e; --diff-remove-chip:#cc241d;
+  --col-add-chip:#79740e; --col-add-on:#f6f3d0; --col-remove-chip:#b3231c; --col-remove-on:#ffe9e2;
+  --badge-amber:#fabd2f; --badge-amber-bg:#332b10; --badge-amber-edge:#fabd2f;
+  --syntax-keyword:#d3869b; --syntax-string:#8ec07c; --syntax-comment:#928374; --syntax-jinja:#fe8019;
+  --syntax-fold:#d3869b; --syntax-fold-bg:rgba(211,134,155,0.10);
+  --warn:#fabd2f; --error:#fb4934; --error-bg:#3a1a16;
+  --tooltip-bg:#1d2021; --tooltip-fg:#ebdbb2;
+  --code-bg:#282828; --code-header-bg:#32302f; --code-gutter-bg:#282828; --code-gutter-fg:#7c6f64;
+  --code-line-hover:rgba(255,255,255,0.04);
+  --code-add-bg:rgba(184,187,38,0.15); --code-add-gutter:rgba(184,187,38,0.28); --code-add-word:rgba(184,187,38,0.30);
+  --code-rem-bg:rgba(251,73,52,0.15); --code-rem-gutter:rgba(251,73,52,0.28); --code-rem-word:rgba(251,73,52,0.40);
+  --code-hunk-bg:rgba(131,165,152,0.18); --code-hunk-fg:#83a598;
+  --code-add-sigil:#b8bb26; --code-rem-sigil:#fb7164;
+}
+
+/* ==========================================================================
+   THEME · DRACULA  — vivid purple-on-charcoal (dark family)
+   ========================================================================== */
+:root[data-theme="dracula"] {
+  --bg:#282a36; --bg-alt:#21222c; --surface:#2b2e3b; --surface-2:#343746;
+  --text:#f8f8f2; --text-muted:#a9adc4; --text-faint:#6272a4;
+  --border:#44475a; --border-strong:#565a73;
+  --accent:#bd93f9; --accent-hover:#d6bbfb; --accent-tint:#352b50; --accent-on:#21222c;
+  --control-bg:#343746; --control-active-bg:#bd93f9; --control-active-fg:#21222c;
+  --diff-add-fg:#50fa7b; --diff-remove-fg:#ff6e6e;
+  --diff-add-row:rgba(80,250,123,0.10); --diff-remove-row:rgba(255,85,85,0.12);
+  --diff-add-edge:rgba(80,250,123,0.85); --diff-remove-edge:rgba(255,110,110,0.85);
+  --diff-add-chip:#25803f; --diff-remove-chip:#c93b3b;
+  --col-add-chip:#25803f; --col-add-on:#eafff1; --col-remove-chip:#b3344e; --col-remove-on:#ffe9f1;
+  --badge-amber:#ffb86c; --badge-amber-bg:#2e2616; --badge-amber-edge:#ffb86c;
+  --syntax-keyword:#ff79c6; --syntax-string:#f1fa8c; --syntax-comment:#6272a4; --syntax-jinja:#ffb86c;
+  --syntax-fold:#ff79c6; --syntax-fold-bg:rgba(255,121,198,0.10);
+  --warn:#ffb86c; --error:#ff5555; --error-bg:#3a1a1f;
+  --tooltip-bg:#21222c; --tooltip-fg:#f8f8f2;
+  --code-bg:#282a36; --code-header-bg:#21222c; --code-gutter-bg:#282a36; --code-gutter-fg:#6272a4;
+  --code-line-hover:rgba(255,255,255,0.04);
+  --code-add-bg:rgba(80,250,123,0.12); --code-add-gutter:rgba(80,250,123,0.26); --code-add-word:rgba(80,250,123,0.32);
+  --code-rem-bg:rgba(255,85,85,0.13); --code-rem-gutter:rgba(255,85,85,0.28); --code-rem-word:rgba(255,85,85,0.40);
+  --code-hunk-bg:rgba(189,147,249,0.16); --code-hunk-fg:#bd93f9;
+  --code-add-sigil:#50fa7b; --code-rem-sigil:#ff7171;
+}
+
+/* ==========================================================================
+   THEME · CATPPUCCIN LATTE  — soft pastel cream (light family)
+   ========================================================================== */
+:root[data-theme="latte"] {
+  --bg:#eff1f5; --bg-alt:#e6e9ef; --surface:#eff1f5; --surface-2:#e6e9ef;
+  --text:#4c4f69; --text-muted:#5c5f77; --text-faint:#9ca0b0;
+  --border:#ccd0da; --border-strong:#acb0be;
+  --accent:#1a5cdd; --accent-hover:#3553fc; --accent-tint:#dce4fb; --accent-on:#ffffff;
+  --control-bg:#e6e9ef; --control-active-bg:#1e66f5; --control-active-fg:#ffffff;
+  --diff-add-fg:#2a7016; --diff-remove-fg:#bd1331;
+  --diff-add-row:#e3f0db; --diff-remove-row:#f6dde0;
+  --diff-add-edge:#40a02b; --diff-remove-edge:#d20f39;
+  --diff-add-chip:#2a8026; --diff-remove-chip:#c4123a;
+  --col-add-chip:#cfe6c0; --col-add-on:#234d12; --col-remove-chip:#f4c4cb; --col-remove-on:#6e0a1f;
+  --badge-amber:#a36608; --badge-amber-bg:#faefd6; --badge-amber-edge:#df8e1d;
+  --syntax-keyword:#8839ef; --syntax-string:#40a02b; --syntax-comment:#8c8fa1; --syntax-jinja:#ea76cb;
+  --syntax-fold:#8839ef; --syntax-fold-bg:rgba(136,57,239,0.08);
+  --warn:#a36608; --error:#d20f39; --error-bg:#f7d8de;
+  --tooltip-bg:#4c4f69; --tooltip-fg:#eff1f5;
+  --code-bg:#eff1f5; --code-header-bg:#e6e9ef; --code-gutter-bg:#eff1f5; --code-gutter-fg:#9ca0b0;
+  --code-line-hover:rgba(76,79,105,0.04);
+  --code-add-bg:#e3f0db; --code-add-gutter:#cfe6c0; --code-add-word:#bfe0a8;
+  --code-rem-bg:#f6dde0; --code-rem-gutter:#efc3c8; --code-rem-word:#ecb6bc;
+  --code-hunk-bg:#dce4fb; --code-hunk-fg:#5c5f77;
+  --code-add-sigil:#2a7016; --code-rem-sigil:#bd1331;
+}
+
+/* ==========================================================================
+   THEME · ROSÉ PINE DAWN  — muted rose / warm low-contrast (light family)
+   ========================================================================== */
+:root[data-theme="rosepine"] {
+  --bg:#e6d9c9; --bg-alt:#f7f1ea; --surface:#fbf6ef; --surface-2:#ece0d3;
+  --text:#544f74; --text-muted:#5d5780; --text-faint:#938ea1;
+  --border:#cdbfb1; --border-strong:#b3a596;
+  --accent:#276680; --accent-hover:#3c666e; --accent-tint:#dde7ea; --accent-on:#ffffff;
+  --control-bg:#f2e9e1; --control-active-bg:#286983; --control-active-fg:#ffffff;
+  --diff-add-fg:#45700f; --diff-remove-fg:#b01b43;
+  --diff-add-row:#e6ebdd; --diff-remove-row:#f6dde1;
+  --diff-add-edge:#56743a; --diff-remove-edge:#b4254a;
+  --diff-add-chip:#56743a; --diff-remove-chip:#b4254a;
+  --col-add-chip:#d3ddbd; --col-add-on:#2c4410; --col-remove-chip:#f1c6cf; --col-remove-on:#67132f;
+  --badge-amber:#996515; --badge-amber-bg:#f7ebd5; --badge-amber-edge:#ea9d34;
+  --syntax-keyword:#907aa9; --syntax-string:#286983; --syntax-comment:#9893a5; --syntax-jinja:#d7827e;
+  --syntax-fold:#907aa9; --syntax-fold-bg:rgba(144,122,169,0.10);
+  --warn:#996515; --error:#b4254a; --error-bg:#f3dde2;
+  --tooltip-bg:#232136; --tooltip-fg:#e0def4;
+  --code-bg:#faf4ed; --code-header-bg:#f2e9e1; --code-gutter-bg:#faf4ed; --code-gutter-fg:#9893a5;
+  --code-line-hover:rgba(87,82,121,0.04);
+  --code-add-bg:#e6ebdd; --code-add-gutter:#d3ddbd; --code-add-word:#c2d3a0;
+  --code-rem-bg:#f6dde1; --code-rem-gutter:#eec3cb; --code-rem-word:#eabdc5;
+  --code-hunk-bg:#dde7ea; --code-hunk-fg:#575279;
+  --code-add-sigil:#45700f; --code-rem-sigil:#b01b43;
+}
+
+/* end of cute-dbt design tokens v1 (cute-dbt#242) */
+/* ============================================================================
+   cute-dbt base chassis v1 (cute-dbt#242)
+   ----------------------------------------------------------------------------
+   The minimal CROSS-FAMILY page chassis: the token-consuming page-level
+   rules both page families (report + explore) share, moved here from
+   templates/report.css section 4 — rules only, exact current values.
+   Included via askama immediately after partials/tokens.css in every
+   page's <style> block; the report-specific remainder (style packs,
+   chrome, components, reconciliation, coverage panel) stays in
+   templates/report.css and cascades after this file exactly as before.
+
+   ORDER DISCIPLINE (the zero-visual-diff contract of the extraction):
+   only cascade-order-safe rules moved. The report's body GEOMETRY
+   (font-size / max-width / padding-top / font-family) deliberately stays
+   in report.css — it must keep winning the equal-specificity contest
+   against the report's 720px responsive body rule, which sits earlier in
+   that file. This file carries only the body's token skin.
+
+   FIRST-PARTY asset — authored for cute-dbt; integrity gates are the
+   banner-pin + comment-balance tests in `src/adapters/asset_embed.rs`.
+
+   PORTING-BUG GUARD (handoff §2.1): never write a `*` immediately followed
+   by `/` inside a comment body — it closes the comment early and silently
+   eats the next rule. A comment-balance test pins this.
+   ========================================================================== */
+
+/* cute-dbt#121 — make the `hidden` attribute authoritative: Sakura's
+   `pre { display: block }` (author origin) otherwise overrides the UA
+   `[hidden] { display: none }`, so a toggled-off element would still
+   render. Both page families toggle visibility by setting `hidden`;
+   this rule wins. */
+[hidden] { display: none !important; }
+
+html { background: var(--bg); }
+body {
+  background: var(--bg);
+  color: var(--text);
+  transition: background 120ms ease, color 120ms ease;
+}
+
+a { color: var(--accent); }
+a:hover { color: var(--accent-hover); }
+
+select, input, textarea {
+  background: var(--surface); color: var(--text);
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+}
+select:hover, input:hover { border-color: var(--border-strong); }
+select:focus-visible, input:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
+
+/* end of cute-dbt base chassis v1 (cute-dbt#242) */
+/* ============================================================================
+   cute-dbt report chassis CSS v1 (cute-dbt#177)
+   ----------------------------------------------------------------------------
+   Claude Design Phase-1 integration, PR-1: semantic token layer + 8 AA-pinned
+   themes (5 from pass-1 + Dracula / Catppuccin Latte / Rosé Pine Dawn from
+   pass-2, cute-dbt#198) + 4 style packs + density layer + the tokenized
+   component rules.
+   FIRST-PARTY asset — authored for cute-dbt, embedded via `include_str!`
+   (`asset_embed::REPORT_CSS`) and interpolated into the report template's
+   custom <style> block. Deliberately NOT listed in `assets/MANIFEST.toml`:
+   that index is third-party vendoring provenance (Sakura, jQuery, DataTables,
+   Mermaid); this file has no upstream to pin. Its integrity gate is the
+   banner-pin + comment-balance tests in `src/adapters/asset_embed.rs`.
+
+   RE-LAYERED at cute-dbt#242: the design-system root — the semantic token
+   layer + all eight [data-theme] blocks — now lives in
+   templates/partials/tokens.css, and the minimal cross-family page chassis
+   ([hidden], the html/body token skin, links, form controls) in
+   templates/partials/base.css. Both partials are askama-included into the
+   report's <style> block IMMEDIATELY BEFORE this file (and identically into
+   the explore pages), so the emitted cascade order is:
+     1. partials/tokens.css — :root semantic tokens + eight [data-theme] blocks
+     2. partials/base.css   — shared page chassis (both page families)
+     3. styles.css layer    — the four html[data-style] direction packs (here)
+     4. chrome.css layer    — settings panel, pickers, Cytoscape host (here)
+     5. base.css layer      — report-specific tokenized components (here)
+   followed by the cute-dbt reconciliation + engine + coverage layers (this
+   repo's shipped affordances the handoff forked before: #157, #165, #166).
+
+   PORTING-BUG GUARD (handoff §2.1): never write a `*` immediately followed
+   by `/` inside a comment body — it closes the comment early and silently
+   eats the next rule (this once ate the `[hidden]` rule and broke every
+   toggle). A comment-balance test pins this.
+   ========================================================================== */
+
+/* ============================================================================
+   2. STYLE PACKS  (engine/styles.css — runtime "Style" setting on
+   html[data-style])
+   ----------------------------------------------------------------------------
+   Four distinct VISUAL LANGUAGES (not density clones). Orthogonal to THEME
+   (colour) and the density Tweak. Specificity: html[data-style] (0,1,1) beats
+   the base :root defaults; the density Tweak (:root[data-density], 0,2,0) still
+   wins over a style, as intended.
+     · paper    — airy editorial, generous whitespace, soft radius
+     · terminal — mono-forward, boxy, zero radius, $ prefix
+     · swiss    — international typographic grid: thick rules, bold tracked
+                  labels, square, structural
+     · soft     — modern product UI: rounded cards, subtle fills, pill controls
+   ========================================================================== */
+
+/* ========================== PAPER ========================================= */
+html[data-style="paper"] {
+  --fs-base: 14.5px;  --lh-base: 1.66;  --fs-label: 11px;
+  --pad-cell: 0.36rem 0.78rem;  --pad-panel: 1.45rem;  --gap-section: 1.7rem;
+  --radius-sm: 7px;  --radius-pan: 13px;  --rule-w: 1px;
+  --label-font: var(--font-ui);  --label-tracking: 0.1em;
+}
+html[data-style="paper"] .report-header h1 { font-size: 1.55em; letter-spacing: -0.015em; }
+html[data-style="paper"] .report-header { margin-bottom: 0.4rem; }
+html[data-style="paper"] .left-panel,
+html[data-style="paper"] .expected-panel {
+  border-radius: var(--radius-pan); padding: 1.1rem 1.35rem 1.35rem; background: var(--surface);
+}
+html[data-style="paper"] .left-panel { border: var(--rule-w) solid var(--border); }
+html[data-style="paper"] .panel-row { gap: 2rem; }
+html[data-style="paper"] .cte-dag-cyto { height: 26rem; }
+html[data-style="paper"] .dag-legend { gap: 0.5rem 2.4rem; }
+html[data-style="paper"] .node-role-badge,
+html[data-style="paper"] .incremental-badge,
+html[data-style="paper"] .mode-badge { padding: 0.16rem 0.62rem; }
+
+/* ========================= TERMINAL ======================================= */
+html[data-style="terminal"] {
+  --fs-base: 13px;  --lh-base: 1.46;  --fs-label: 10px;
+  --pad-cell: 0.16rem 0.6rem;  --pad-panel: 0.85rem;  --gap-section: 0.95rem;
+  --radius-sm: 0;  --radius-pan: 0;  --rule-w: 1px;
+  --font-ui: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, monospace;
+  --label-font: var(--font-ui);  --label-tracking: 0.09em;
+}
+html[data-style="terminal"] select,
+html[data-style="terminal"] input,
+html[data-style="terminal"] .theme-chip,
+html[data-style="terminal"] .seg-radio,
+html[data-style="terminal"] .test-mode-toggle,
+html[data-style="terminal"] .axis-filter,
+html[data-style="terminal"] .settings-cog,
+html[data-style="terminal"] .settings-panel,
+html[data-style="terminal"] .cte-dag-cyto,
+html[data-style="terminal"] .sql-block,
+html[data-style="terminal"] .td-defined-in,
+html[data-style="terminal"] .accent-swatch,
+html[data-style="terminal"] .node-role-badge,
+html[data-style="terminal"] .incremental-badge,
+html[data-style="terminal"] .mode-badge,
+html[data-style="terminal"] .this-badge,
+html[data-style="terminal"] .format-badge,
+html[data-style="terminal"] .row-count-badge,
+html[data-style="terminal"] .left-panel,
+html[data-style="terminal"] .expected-panel { border-radius: 0; }
+html[data-style="terminal"] table.given-table,
+html[data-style="terminal"] table.expected-table { border: 1px solid var(--border-strong); }
+html[data-style="terminal"] table.given-table td,
+html[data-style="terminal"] table.given-table th,
+html[data-style="terminal"] table.expected-table td,
+html[data-style="terminal"] table.expected-table th { border: 1px solid var(--border); }
+html[data-style="terminal"] table.given-table th,
+html[data-style="terminal"] table.expected-table th { background: var(--bg-alt); border-bottom: 2px solid var(--accent); }
+html[data-style="terminal"] .report-header h1 { letter-spacing: 0.02em; }
+html[data-style="terminal"] .report-header h1::before { content: "$ "; color: var(--accent); }
+html[data-style="terminal"] .left-panel { border: 1px solid var(--border); padding: 0.55rem 0.8rem 0.8rem; }
+
+/* =========================== SWISS ======================================== */
+html[data-style="swiss"] {
+  --fs-base: 13px;  --lh-base: 1.52;  --fs-label: 10.5px;
+  --pad-cell: 0.22rem 0.62rem;  --pad-panel: 1rem;  --gap-section: 1.3rem;
+  --radius-sm: 0;  --radius-pan: 0;  --rule-w: 1px;
+  --label-font: var(--font-ui);  --label-tracking: 0.16em;
+}
+/* thick rule + heavy black tracked labels = international typographic style */
+html[data-style="swiss"] .report-header { border-bottom: 3px solid var(--text); padding-bottom: 0.5rem; margin-bottom: 0.9rem; }
+html[data-style="swiss"] .report-header h1 { font-size: 1.7em; font-weight: 800; letter-spacing: -0.02em; }
+html[data-style="swiss"] .panel-header,
+html[data-style="swiss"] .cte-dag-header { border-top: 2px solid var(--text); padding-top: 0.4rem; }
+html[data-style="swiss"] .panel-header h2,
+html[data-style="swiss"] .cte-dag-header h2,
+html[data-style="swiss"] .settings-panel h2 { font-weight: 800; color: var(--text); }
+html[data-style="swiss"] select,
+html[data-style="swiss"] input,
+html[data-style="swiss"] .seg-radio,
+html[data-style="swiss"] .test-mode-toggle,
+html[data-style="swiss"] .axis-filter,
+html[data-style="swiss"] .theme-chip,
+html[data-style="swiss"] .settings-cog,
+html[data-style="swiss"] .settings-panel,
+html[data-style="swiss"] .cte-dag-cyto,
+html[data-style="swiss"] .sql-block,
+html[data-style="swiss"] .node-role-badge,
+html[data-style="swiss"] .incremental-badge,
+html[data-style="swiss"] .mode-badge,
+html[data-style="swiss"] .this-badge,
+html[data-style="swiss"] .format-badge,
+html[data-style="swiss"] .row-count-badge { border-radius: 0; }
+html[data-style="swiss"] .accent-swatch { border-radius: 0; }
+html[data-style="swiss"] .expected-panel { border-width: 0; border-top: 3px solid var(--accent); border-radius: 0; padding: 0.6rem 0 1rem; }
+html[data-style="swiss"] .left-panel { border: 0; border-top: 2px solid var(--border-strong); border-radius: 0; padding: 0.6rem 0 1rem; }
+html[data-style="swiss"] table.given-table th,
+html[data-style="swiss"] table.expected-table th { border-bottom: 2px solid var(--text); }
+
+/* ============================ SOFT ======================================== */
+html[data-style="soft"] {
+  --fs-base: 13.5px;  --lh-base: 1.55;  --fs-label: 10.5px;
+  --pad-cell: 0.3rem 0.72rem;  --pad-panel: 1.15rem;  --gap-section: 1.2rem;
+  --radius-sm: 8px;  --radius-pan: 14px;  --rule-w: 1px;
+  --label-font: var(--font-ui);  --label-tracking: 0.05em;
+}
+/* rounded cards, subtle surface fills, pill controls = modern product UI */
+html[data-style="soft"] .left-panel,
+html[data-style="soft"] .expected-panel {
+  border-radius: var(--radius-pan); padding: 1rem 1.2rem 1.2rem; background: var(--bg-alt);
+  border: var(--rule-w) solid var(--border);
+}
+html[data-style="soft"] .expected-panel { border-width: 2px; border-color: var(--accent); }
+html[data-style="soft"] .test-mode-toggle,
+html[data-style="soft"] .axis-filter,
+html[data-style="soft"] .seg-radio { border-radius: 999px; overflow: hidden; }
+html[data-style="soft"] .test-mode-toggle button,
+html[data-style="soft"] .axis-filter button,
+html[data-style="soft"] .seg-radio button { padding-left: 14px; padding-right: 14px; }
+html[data-style="soft"] select,
+html[data-style="soft"] input,
+html[data-style="soft"] .settings-panel { border-radius: var(--radius-sm); }
+html[data-style="soft"] .settings-panel { border-radius: var(--radius-pan); }
+html[data-style="soft"] .cte-dag-cyto,
+html[data-style="soft"] .sql-block { border-radius: var(--radius-pan); }
+html[data-style="soft"] .diff-fold-toggle,
+html[data-style="soft"] .yaml-view-btn,
+html[data-style="soft"] .theme-chip { border-radius: 999px; }
+html[data-style="soft"] .report-header h1 { letter-spacing: -0.01em; }
+
+/* ============================================================================
+   3. CHROME LAYER  (engine/chrome.css)
+   ----------------------------------------------------------------------------
+   New affordances added by the redesign: the expanded settings cog (theme +
+   accent + density + diff-style + engine pickers), the direction switcher,
+   the Cytoscape host + node-context card, and the inline-SVG icon sizing.
+   ========================================================================== */
+
+/* ---- inline-SVG icons (mixed iconography: SVG for functional affordances) */
+.icon { width: 1em; height: 1em; flex: 0 0 auto; display: inline-block; vertical-align: -0.125em; fill: none; stroke: currentColor; stroke-width: 1.7; stroke-linecap: round; stroke-linejoin: round; }
+.icon-lg { width: 1.15em; height: 1.15em; }
+
+/* ---- settings cog + panel (themed) ----------------------------------- */
+.settings-cog-wrap { position: fixed; top: 0.75rem; right: 0.75rem; z-index: 50; }
+.settings-cog {
+  font: inherit; line-height: 1; width: 2.2rem; height: 2.2rem;
+  display: inline-flex; align-items: center; justify-content: center;
+  border: var(--rule-w) solid var(--border); border-radius: 50%;
+  background: var(--surface); color: var(--text); cursor: pointer;
+  box-shadow: 0 1px 3px rgba(0,0,0,0.12);
+  transition: background 120ms ease, color 120ms ease, border-color 120ms ease;
+}
+.settings-cog .icon { width: 1.15rem; height: 1.15rem; }
+.settings-cog:hover { background: var(--bg-alt); border-color: var(--border-strong); }
+.settings-cog:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.settings-cog[aria-expanded="true"] { background: var(--control-active-bg); color: var(--control-active-fg); border-color: var(--control-active-bg); }
+
+.settings-panel {
+  position: absolute; top: 2.6rem; right: 0; width: 28rem; max-width: 92vw; box-sizing: border-box;
+  background: var(--surface); border: var(--rule-w) solid var(--border); border-radius: var(--radius-pan);
+  box-shadow: 0 6px 24px rgba(0,0,0,0.22); padding: 0.85rem 1rem 1rem;
+  font-size: 0.85em; color: var(--text);
+  max-height: 84vh; overflow-y: auto;
+}
+.settings-panel h2 {
+  font-size: var(--fs-label); text-transform: uppercase; letter-spacing: var(--label-tracking);
+  color: var(--text-muted); margin: 0 0 0.55rem; font-family: var(--label-font);
+}
+.settings-section + .settings-section { margin-top: 0.95rem; padding-top: 0.85rem; border-top: var(--rule-w) solid var(--border); }
+.settings-row { display: flex; align-items: center; justify-content: space-between; gap: 0.75rem; margin: 0.55rem 0; }
+.settings-row label { flex: 1 1 auto; color: var(--text); }
+.settings-row .settings-hint { display: block; color: var(--text-muted); font-size: 0.85em; margin-top: 0.1rem; }
+.settings-context-input { width: 3.5em; font: inherit; padding: 0.1rem 0.3rem; }
+
+/* theme picker — swatch grid */
+.theme-grid { display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.35rem; margin-top: 0.1rem; }
+.theme-chip {
+  display: flex; align-items: center; gap: 0.4rem; padding: 0.38rem 0.42rem; cursor: pointer;
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  background: var(--surface); color: var(--text); font: inherit; font-size: 0.9em; text-align: left;
+  transition: border-color 120ms ease;
+}
+.theme-chip:hover { border-color: var(--border-strong); }
+.theme-chip[aria-pressed="true"] { border-color: var(--accent); box-shadow: inset 0 0 0 1px var(--accent); }
+.theme-chip .swatch-dots { display: inline-flex; flex: 0 0 auto; }
+.theme-chip .swatch-dots i { width: 10px; height: 10px; border-radius: 50%; display: inline-block; border: 1px solid rgba(128,128,128,0.35); }
+.theme-chip .swatch-dots i + i { margin-left: -4px; }
+.theme-chip .theme-name { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+
+/* accent swatch row */
+.accent-row { display: flex; gap: 0.4rem; margin-top: 0.1rem; flex-wrap: wrap; }
+.accent-swatch { width: 1.5rem; height: 1.5rem; border-radius: 50%; border: 2px solid var(--border); cursor: pointer; padding: 0; position: relative; }
+.accent-swatch[aria-pressed="true"] { border-color: var(--text); box-shadow: 0 0 0 2px var(--surface), 0 0 0 3px var(--text); }
+.accent-swatch[data-accent="theme"] { background: linear-gradient(135deg, var(--accent) 50%, var(--text-muted) 50%); }
+
+/* segmented radio (density, diff-layout, DAG engine) */
+.seg-radio { display: inline-flex; border: var(--rule-w) solid var(--control-border); border-radius: var(--radius-sm); overflow: hidden; }
+.seg-radio button { font: inherit; font-size: 0.9em; padding: 0.25rem 0.6rem; border: none; background: var(--control-bg); color: var(--text-muted); cursor: pointer; }
+.seg-radio button + button { border-left: var(--rule-w) solid var(--control-border); }
+.seg-radio button[aria-pressed="true"] { background: var(--control-active-bg); color: var(--control-active-fg); }
+
+/* pure-CSS switch */
+.settings-switch { position: relative; display: inline-block; width: 2.4rem; height: 1.3rem; flex: 0 0 auto; }
+.settings-switch input { position: absolute; opacity: 0; width: 100%; height: 100%; margin: 0; cursor: pointer; }
+.settings-switch .switch-track { position: absolute; inset: 0; background: var(--border-strong); border-radius: 999px; transition: background 0.12s; pointer-events: none; }
+.settings-switch .switch-track::before { content: ""; position: absolute; top: 2px; left: 2px; width: 1.1rem; height: 1.1rem; background: #fff; border-radius: 50%; transition: transform 0.12s; box-shadow: 0 1px 2px rgba(0,0,0,0.3); }
+.settings-switch input:checked + .switch-track { background: var(--accent); }
+.settings-switch input:checked + .switch-track::before { transform: translateX(1.1rem); }
+.settings-switch input:focus-visible + .switch-track { outline: 2px solid var(--accent); outline-offset: 2px; }
+
+/* ---- Style picker (2×2 grid in settings) ----------------------------- */
+.style-grid { display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.4rem; }
+.style-opt {
+  font: inherit; font-size: 0.92em; padding: 0.4rem 0.5rem; cursor: pointer;
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  background: var(--surface); color: var(--text); text-align: center;
+  transition: border-color 120ms ease;
+}
+.style-opt:hover { border-color: var(--border-strong); }
+.style-opt[aria-pressed="true"] { border-color: var(--accent); box-shadow: inset 0 0 0 1px var(--accent); color: var(--text); font-weight: 600; }
+
+/* ---- Cytoscape host + node-context card ------------------------------- */
+.cte-dag-cyto { position: relative; width: 100%; height: 22rem; min-height: 16rem; border: var(--rule-w) solid var(--border); border-radius: var(--radius-pan); background: var(--surface); resize: vertical; overflow: hidden; }
+.cte-dag-cyto.is-hidden, .cte-dag-mermaid.is-hidden { display: none; }
+.cyto-canvas { position: absolute; inset: 0; }
+.cyto-hint { position: absolute; left: 0.6rem; bottom: 0.5rem; font-size: 0.75em; color: var(--text-faint); pointer-events: none; }
+
+.cyto-node-card {
+  position: absolute; z-index: 20; min-width: 12rem; max-width: 18rem; width: max-content;
+  background: var(--surface); border: var(--rule-w) solid var(--border-strong); border-radius: var(--radius-pan);
+  box-shadow: 0 6px 20px rgba(0,0,0,0.22); padding: 0.55rem 0.7rem; font-size: 0.8em; color: var(--text);
+  pointer-events: none; opacity: 0; transition: opacity 100ms ease;
+}
+.cyto-node-card.is-visible { opacity: 1; }
+/* wrap fixes (design pass-2, cute-dbt#198): long node ids and long
+   role/degree values break instead of pushing the card past its host */
+.cyto-node-card .nc-id { font-family: var(--font-mono); font-weight: 700; word-break: break-all; overflow-wrap: anywhere; display: block; margin-bottom: 0.25rem; }
+.cyto-node-card .nc-row { display: flex; gap: 0.5rem; color: var(--text-muted); flex-wrap: wrap; }
+.cyto-node-card .nc-row b { color: var(--text); font-weight: 600; flex: 0 0 auto; }
+.cyto-node-card .nc-row > span { min-width: 0; overflow-wrap: anywhere; word-break: break-word; }
+
+/* engine picker lives in settings; reuse .seg-radio */
+
+/* ---- responsive ------------------------------------------------------ */
+@media (max-width: 720px) {
+  body { max-width: 100%; padding: 0 0.9rem; }
+  /* cute-dbt#157 — minmax(0,1fr), never bare 1fr: the auto floor of 1fr
+     sizes the track to a wide DataTable's min-content and blows the page
+     past the viewport. Same fix as .panel-row.is-stacked below. */
+  .panel-row { grid-template-columns: minmax(0, 1fr); }
+  .expected-panel { position: static; }
+  /* max-width must widen with the fallback: the base rule's 92vw cap would
+     otherwise clip min(28rem, 94vw) and the 8-chip grid wraps early. */
+  .settings-panel { width: min(28rem, 94vw); max-width: 94vw; }
+  .report-header { flex-wrap: wrap; gap: 0.25rem; }
+  .selector-field { min-width: 0; width: 100%; }
+  .test-selector-row { gap: 0.4rem; }
+}
+@media (max-width: 420px) {
+  .theme-grid { grid-template-columns: 1fr; }
+}
+
+/* ============================================================================
+   4. BASE COMPONENT LAYER  (engine/base.css — tokenized)
+   ----------------------------------------------------------------------------
+   The report's component styles, rewritten from ~50 hardcoded hexes onto the
+   token layer above. Class names + DOM are the askama template contract —
+   unchanged. New chrome (theme picker, engine picker, Cytoscape host, node
+   card, direction switcher) lives in the chrome layer.
+   Density values reference the --fs / --pad / --radius tokens so the
+   DIRECTION stylesheet can retune the whole report by overriding a handful
+   of tokens.
+   ========================================================================== */
+
+/* The cute-dbt#121 [hidden] override, the html/body token skin (background /
+   color / 120ms transition), the link colors and the select/input/textarea
+   form-control skin moved to templates/partials/base.css at cute-dbt#242 —
+   the shared cross-family chassis both page families include before this
+   file. The body GEOMETRY below stays here on purpose: it must keep winning
+   the equal-specificity contest against the 720px responsive body rule in
+   the chrome layer above (cascade-order-sensitive — see base.css's banner). */
+body {
+  font-size: var(--fs-base);
+  line-height: var(--lh-base);
+  max-width: min(120rem, 100% - 4rem);
+  padding-top: 0.5rem;   /* small air under the fixed settings cog */
+  font-family: var(--font-ui);
+}
+
+h1, h2, h3, h4, h5, h6 { margin-top: 1.25rem; margin-bottom: 0.5rem; color: var(--text); }
+h1 { font-size: 1.5em; }
+h2 { font-size: 1em; }
+h3 { font-size: 0.95em; }
+h4 { font-size: 0.85em; }
+code, pre, kbd, samp { font-family: var(--font-mono); }
+
+.report-header { display: flex; align-items: baseline; gap: 0.75rem; margin-bottom: 0.5rem; }
+.report-header h1 { font-size: 1.25em; margin-top: 0.25rem; }
+.report-header .report-subtitle { margin: 0 0 0.25rem; color: var(--text-muted); font-size: 0.9em; }
+p { margin-bottom: 0.875rem; }
+
+.panel-header h2, .cte-dag-header h2 {
+  font-size: var(--fs-label); text-transform: uppercase;
+  letter-spacing: var(--label-tracking); color: var(--text-muted); margin: 0;
+  font-family: var(--label-font); white-space: nowrap;
+}
+.table-title { font-size: 0.8em; font-weight: 600; font-family: var(--font-mono); margin: 0; color: var(--text); }
+.table-header { margin: 0.4rem 0 0.2rem; }
+
+table.given-table th, table.expected-table th,
+table.given-table td, table.expected-table td {
+  padding: var(--pad-cell); font-size: 0.82em;
+  border-color: var(--border);
+}
+table.given-table th, table.expected-table th {
+  white-space: normal; max-width: 12rem; vertical-align: bottom; line-height: 1.2;
+  font-family: var(--font-mono); color: var(--text);
+}
+
+.diff-scope-banner, .test-selection, .cte-dag, .panel { margin-bottom: var(--gap-section); }
+.test-details-body {
+  padding: 0.6rem 0 0; margin-top: 0.35rem;
+  border-top: var(--rule-w) solid var(--border);
+  display: flex; flex-direction: column; gap: 0.3rem; font-size: 0.85em;
+}
+.td-line { line-height: 1.5; }
+.td-label, .td-meta-key { color: var(--text-muted); }
+.td-tag, .td-meta-key, .td-meta-val { font-family: var(--font-mono); }
+.td-defined-in {
+  font-family: var(--font-mono); background: var(--bg-alt);
+  padding: 0.05rem 0.35rem; border-radius: var(--radius-sm); color: var(--text);
+}
+.cte-dag-header { display: flex; align-items: center; gap: 0.4rem; margin-bottom: 0.55rem; }
+.cte-dag-header h2 { margin: 0; }
+.cte-dag-header .cte-dag-hint { font-size: 0.95em; line-height: 1; }
+
+.diff-scope-banner { padding: 0; margin: 0 0 var(--gap-section); color: var(--text); }
+.diff-scope-line code, .diff-scope-text + * code { font-family: var(--font-mono); }
+/* cute-dbt#240 (defects C+E) — Sakura's base `code { background:#f1f1f1 }`
+   is a light-theme constant: on the dark themes it pairs with the
+   near-white --text at ≈1.2:1 (illegible "near-white pills"). The two
+   founder-reported surfaces — the baseline-manifest path in the scope
+   banner (E) and the COVERED finding row's "Covered by" test ids (C) —
+   re-skin with theme tokens (the .td-defined-in idiom; --bg-alt is each
+   theme's sunken code-well pair for --text). AA-pinned on all 8 themes by
+   the headless guard family. */
+.diff-scope-line code, .finding-covered-by code {
+  background: var(--bg-alt); color: var(--text);
+}
+
+/* cute-dbt#201 — the left-border description BANNER (and its
+   test-description-section landmark) is retired: the description renders
+   as plain larger-font text inside the always-open test card (the
+   .test-card .test-description rule below). The #74 no-empty-landmark
+   intent carries over via the `hidden` attribute the JS toggles. */
+.test-description {
+  margin: 0; padding: 0; background: none; border: 0;
+  color: var(--text);
+}
+
+.test-selector-row { display: flex; flex-wrap: wrap; align-items: flex-end; gap: 0.5rem 1rem; }
+.selector-field { display: flex; flex-direction: column; min-width: 16rem; }
+.selector-field label { color: var(--text-muted); font-size: 0.85em; margin-bottom: 0.15rem; }
+
+/* select / input / textarea token skin -> partials/base.css (cute-dbt#242) */
+
+/* ---- DAG host (Mermaid) + Cytoscape host ----------------------------- */
+.cte-dag-mermaid { display: flex; justify-content: center; overflow-x: auto; padding: 0.5rem 0; }
+.cte-dag-mermaid svg { max-width: 100%; height: auto; }
+/* Keyboard-accessibility (cute-dbt#41): visible focus ring on focused DAG
+   nodes. :focus-visible isolates keyboard focus from mouse clicks so a click
+   does not leave a ring stuck on the clicked node. */
+g.dag-node { outline: none; }
+g.dag-node:focus-visible { outline: 2px dashed var(--accent); outline-offset: 3px; }
+
+.dag-legend {
+  display: flex; flex-wrap: wrap; gap: 0.5rem 2rem; align-items: center;
+  margin-top: 0.75rem; padding-top: 0.5rem;
+  border-top: var(--rule-w) dashed var(--border); font-size: 0.9em;
+}
+.dag-legend-group { display: flex; flex-wrap: wrap; gap: 0.4rem 1rem; align-items: center; }
+.dag-legend-title { font-weight: 600; color: var(--text-muted); white-space: nowrap; }
+.dag-legend-roles, .dag-legend-joins { display: inline-flex; flex-wrap: wrap; gap: 0.4rem 1.25rem; align-items: center; }
+.legend-item { display: inline-flex; align-items: center; gap: 0.4rem; color: var(--text); white-space: nowrap; }
+.legend-label { color: var(--text); white-space: nowrap; }
+.legend-swatch { display: inline-block; width: 22px; height: 14px; border: 1.5px solid currentColor; background: var(--surface); }
+/* FIXED DAG palette — same in every theme */
+.legend-item.role-import    { color: var(--dag-import-stroke); }
+.legend-item.role-import    .legend-swatch { background: var(--dag-import-fill); border-radius: 999px; }
+.legend-item.role-transform { color: var(--dag-transform-stroke); }
+.legend-item.role-transform .legend-swatch { background: var(--dag-transform-fill); }
+.legend-item.role-final     { color: var(--dag-final-stroke); }
+/* Two-layer hexagon (design pass-2, cute-dbt#198): a color-mix-darkened
+   edge layer with a ::before fill inset — matches the darker #b07400
+   role_final stroke both DAG engines now draw. */
+.legend-item.role-final     .legend-swatch {
+  position: relative;
+  background: color-mix(in oklab, var(--dag-final-stroke) 62%, black);
+  clip-path: polygon(15% 0, 85% 0, 100% 50%, 85% 100%, 15% 100%, 0 50%); border: 0; filter: none;
+}
+.legend-item.role-final     .legend-swatch::before {
+  content: ""; position: absolute; inset: 1.5px;
+  background: var(--dag-final-fill);
+  clip-path: polygon(15% 0, 85% 0, 100% 50%, 85% 100%, 15% 100%, 0 50%);
+}
+.legend-edge { display: inline-block; width: 28px; height: 3px; border-radius: 2px; }
+
+/* ============================================================================
+   cute-dbt#201 — design-2 layout restructure: DAG node-detail shelf +
+   the always-open test card (selector-as-title). Lifted from the pass-2
+   engine/base.css (design-return-2), deduplicated, minus the overrides
+   badge rules (those land with the badge itself in cute-dbt#202).
+   ========================================================================== */
+
+/* --- DAG node-detail shelf (beside the DAG; wraps under on narrow) ------ */
+.dag-stage { display: flex; flex-wrap: wrap; gap: 1rem; align-items: flex-start; }
+.dag-canvas-col { flex: 1 1 360px; min-width: 0; }
+.dag-shelf {
+  flex: 1 1 340px; min-width: 0; align-self: stretch;
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-pan);
+  background: var(--surface); overflow: hidden;
+  display: flex; flex-direction: column; max-height: 32rem;
+}
+.dag-shelf-head {
+  display: flex; align-items: center; justify-content: space-between; gap: 0.6rem;
+  padding: 0.45rem 0.7rem; background: var(--surface-2);
+  border-bottom: var(--rule-w) solid var(--border);
+}
+.dag-shelf-title { font-family: var(--font-mono); font-size: 0.82rem; font-weight: 700; color: var(--text); white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+.dag-shelf-close {
+  flex: 0 0 auto; width: 1.7rem; height: 1.7rem; padding: 0; line-height: 1; cursor: pointer;
+  display: inline-flex; align-items: center; justify-content: center;
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  background: var(--surface); color: var(--text-muted); font-size: 0.9rem;
+}
+.dag-shelf-close:hover { background: var(--bg-alt); color: var(--text); border-color: var(--border-strong); }
+.dag-shelf-body { padding: 0.7rem; overflow: auto; }
+
+/* model-detail card (in the shelf; also the shape the #202 ref tooltip
+   will mirror) */
+.model-detail-card { border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm); background: var(--bg-alt); padding: 0.55rem 0.65rem; margin-bottom: 0.7rem; }
+.model-detail-card code { background: none; padding: 0; border-radius: 0; }
+.mdc-head { display: flex; align-items: baseline; gap: 0.5rem; flex-wrap: wrap; }
+.mdc-name { font-family: var(--font-mono); font-weight: 700; color: var(--text); }
+.mdc-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: var(--badge-amber); background: var(--badge-amber-bg); border: var(--rule-w) solid var(--badge-amber-edge); border-radius: 999px; padding: 0.05rem 0.5rem; }
+.mdc-tags { display: flex; flex-wrap: wrap; gap: 0.3rem; margin-bottom: 0.45rem; }
+.mdc-tag { font-size: 0.72rem; color: var(--text-muted); background: var(--surface-2); border: var(--rule-w) solid var(--border); border-radius: 999px; padding: 0.03rem 0.5rem; }
+.mdc-desc { margin: 0.45rem 0 0; font-size: 0.95rem; line-height: 1.6; color: var(--text); text-wrap: pretty; }
+.mdc-section-label { margin: 0.7rem 0 0.3rem; font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.06em; color: var(--text-muted); }
+.mdc-mtests .mdc-mtest-list { list-style: none; margin: 0; padding: 0; display: flex; flex-direction: column; gap: 0.3rem; }
+/* structured-test chips (accent keyword + value chips) */
+.dt-row { display: flex; flex-wrap: wrap; align-items: baseline; gap: 0.3rem 0.85rem; font-size: 0.8rem; }
+.dt-key { font-family: var(--font-mono); font-weight: 700; }
+.dt-vals { display: inline-flex; flex-wrap: wrap; gap: 0.25rem; }
+.dt-val { font-family: var(--font-mono); border-radius: 4px; padding: 0.02rem 0.42rem; font-size: 0.85em; }
+.model-detail-card .dt-key, .mdc-col-tests .dt-key { color: var(--accent); }
+.model-detail-card .dt-val, .mdc-col-tests .dt-val { background: var(--surface-2); border: var(--rule-w) solid var(--border); color: var(--text); }
+.mdc-col-testcount { font: inherit; font-family: var(--font-mono); font-size: 0.7rem; line-height: 1.3; min-width: 1.2em; text-align: center; padding: 0 0.32rem; border-radius: 999px; cursor: pointer; color: var(--accent-hover); background: var(--accent-tint); border: var(--rule-w) solid color-mix(in oklab, var(--accent) 30%, transparent); }
+.mdc-col-testcount:hover { filter: brightness(0.97); }
+.mdc-col-testcount[aria-expanded="true"] { background: var(--accent); color: var(--accent-on); }
+.mdc-col-tests { list-style: none; margin: 0.5rem 0 0.2rem; padding: 0 0 0 0.6rem; display: flex; flex-direction: column; gap: 0.5rem; border-left: 2px solid var(--border); }
+.mdc-cols { list-style: none; margin: 0.55rem 0 0; padding: 0; }
+.mdc-cols > li { display: block; padding: 0.45rem 0; font-size: 0.84rem; border-top: var(--rule-w) solid var(--border); }
+.mdc-cols > li:first-child { border-top: 0; padding-top: 0; }
+.mdc-col-nrow { display: flex; align-items: baseline; gap: 0.45rem; flex-wrap: wrap; }
+.mdc-col-name { font-family: var(--font-mono); font-weight: 600; font-size: 0.88rem; color: var(--text); }
+.mdc-col-type { font-family: var(--font-mono); font-size: 0.82rem; color: var(--text-faint); }
+.mdc-col-desc { margin-top: 0.2rem; font-size: 0.82rem; line-height: 1.5; color: var(--text-muted); }
+
+/* role badge rides the Compiled SQL summary row in the shelf */
+.dag-shelf .compiled-sql > summary { display: flex; align-items: center; gap: 0.5rem; }
+.dag-shelf .compiled-sql > summary .cs-label { flex: 0 0 auto; }
+.dag-shelf .compiled-sql > summary .node-role-badge { margin-left: auto; }
+
+/* --- test card (selector = title; toggle right; badges far right) ------- */
+.test-section { margin: 1rem 0; }
+.test-card { border: 0; background: none; padding: 0; }
+.test-card-head { display: flex; align-items: flex-end; gap: 0.75rem; flex-wrap: wrap; }
+.test-card-head .test-select-field { flex: 0 1 auto; min-width: 0; }
+/* sizeSelectToWidest sets a static px width; this clamp keeps a long test
+   name from pushing a narrow viewport (the #157 lesson). */
+.test-card-head #test-select { max-width: 100%; }
+.test-card-head .test-mode-toggle { flex: 0 0 auto; }
+.test-card-head .test-badges { flex: 0 0 auto; margin-left: auto; align-self: center; }
+.test-badges { display: flex; flex-wrap: wrap; gap: 0.35rem; align-items: center; }
+.tb-badge { font-size: 0.72rem; line-height: 1.4; padding: 0.1rem 0.55rem; border-radius: 999px; border: var(--rule-w) solid transparent; white-space: nowrap; }
+.tb-tag { color: var(--accent-hover); background: var(--accent-tint); border-color: color-mix(in oklab, var(--accent) 35%, transparent); font-family: var(--font-mono); }
+/* cute-dbt#202 — the `overrides · N` badge (an #ov-tooltip trigger). */
+.tb-overrides { color: var(--accent-hover); background: var(--accent-tint); border-color: color-mix(in oklab, var(--accent) 35%, transparent); cursor: help; font-family: var(--font-mono); }
+.tb-overrides:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
+.tb-meta { color: var(--text-muted); background: var(--surface-2); border-color: var(--border); font-family: var(--font-mono); }
+.tb-muted { color: var(--text-faint); background: transparent; border-color: var(--border); }
+/* the always-open card's description — larger font (founder decision on
+   epic cute-dbt#197); `hidden` (the #74 carry-over) removes it entirely. */
+.test-card .test-description { margin: 0.7rem 0 0; font-size: 1.34rem; line-height: 1.55; color: var(--text); text-wrap: pretty; white-space: normal; }
+.test-card .test-details-body { margin-top: 0.85rem; }
+
+/* ---- two-panel region ------------------------------------------------ */
+.panel-row { display: grid; grid-template-columns: minmax(0,1fr) minmax(0,1fr); gap: 1.5rem; margin: 1.5rem 0; }
+/* cute-dbt#157 — the stacked track is minmax(0,1fr), NEVER bare 1fr
+   (= minmax(auto,1fr)): the auto floor sizes the single stacked track to a
+   wide DataTable's min-content and drags <body> past a phone viewport. The
+   handoff's base.css predates the #158 fix; a 375px headless regression
+   test pins this. */
+.panel-row.is-stacked { grid-template-columns: minmax(0, 1fr); }
+
+/* cute-dbt#201 — the .panel-toggle (Inspect/All-inputs) selectors are
+   retired with the segmented control; the #91 scope toggle now lives in
+   the test card's head (flex gap spaces it — no bottom margin). */
+.test-mode-toggle { display: inline-flex; gap: 0; border: var(--rule-w) solid var(--control-border); border-radius: var(--radius-sm); overflow: hidden; }
+.test-mode-toggle button {
+  font-size: 0.85em; padding: 4px 12px; border: none; border-radius: 0;
+  background: var(--control-bg); color: var(--control-fg); cursor: pointer; opacity: 1;
+  font-family: var(--font-ui);
+}
+.test-mode-toggle button + button { border-left: var(--rule-w) solid var(--control-border); }
+.test-mode-toggle button:not(.is-active) { opacity: 0.85; color: var(--text-muted); }
+.test-mode-toggle button.is-active { background: var(--control-active-bg); color: var(--control-active-fg); }
+
+/* cute-dbt#414 (Slice C) — the single-select 3-axis filter toggle, reusing
+   the #91 test-mode-toggle segmented recipe (one bordered group, zero-gap
+   buttons, the active segment inverts). JS-built + hidden until any model
+   carries `axes` (--pr-diff mode only), so baseline goldens stay
+   byte-identical. */
+.axis-filter { display: inline-flex; gap: 0; border: var(--rule-w) solid var(--control-border); border-radius: var(--radius-sm); overflow: hidden; margin-bottom: 0.75rem; }
+.axis-filter button {
+  font-size: 0.85em; padding: 4px 12px; border: none; border-radius: 0;
+  background: var(--control-bg); color: var(--control-fg); cursor: pointer; opacity: 1;
+  font-family: var(--font-ui);
+}
+.axis-filter button + button { border-left: var(--rule-w) solid var(--control-border); }
+.axis-filter button:not(.is-active) { opacity: 0.85; color: var(--text-muted); }
+.axis-filter button.is-active { background: var(--control-active-bg); color: var(--control-active-fg); }
+
+.zero-updated-hint, .zero-updated-affirm { margin: 0.5rem 0 0; color: var(--text-muted); font-style: italic; font-size: 0.9em; }
+.panel-header { display: flex; align-items: center; justify-content: space-between; gap: 0.5rem; margin-bottom: 0.75rem; flex-wrap: wrap; }
+
+.expected-panel {
+  border: 2px solid var(--accent); border-radius: var(--radius-pan);
+  padding: 0.5rem 1rem 1rem; position: sticky; top: 1rem; align-self: start;
+  background: var(--surface);
+}
+.panel-row.is-stacked .expected-panel { position: static; }
+.left-panel { background: var(--surface); }
+
+.empty-hint, .no-tests-empty { padding: 2rem; text-align: center; color: var(--text-muted); border: var(--rule-w) dashed var(--border); border-radius: var(--radius-pan); }
+.no-tests-empty { padding: 1.25rem 1rem; font-style: italic; }
+
+/* ---- badges ---------------------------------------------------------- */
+.node-role-badge, .incremental-badge, .mode-badge, .this-badge, .format-badge, .row-count-badge {
+  font-size: 0.7em; padding: 0.12rem 0.55rem; border-radius: 999px;
+  text-transform: uppercase; letter-spacing: 0.05em; border: var(--rule-w) solid currentColor;
+  font-family: var(--font-ui); white-space: nowrap;
+}
+.node-role-badge.role-import    { color: var(--dag-import-stroke); }
+.node-role-badge.role-transform { color: var(--text-muted); }
+.node-role-badge.role-final     { color: var(--badge-amber); }
+.incremental-badge { color: var(--badge-amber); border-color: var(--badge-amber-edge); background: var(--badge-amber-bg); }
+.mode-badge.mode-incremental { color: var(--badge-amber); border-color: var(--badge-amber-edge); }
+.mode-badge.mode-full-refresh { color: var(--text-muted); }
+.this-badge { color: var(--badge-amber); border-color: var(--badge-amber-edge); }
+.format-badge, .row-count-badge { color: var(--text-muted); border-color: var(--border); text-transform: none; letter-spacing: normal; }
+.fixture-provenance { font-size: 0.75em; color: var(--text-muted); }
+.fixture-provenance code { font-size: 0.95em; }
+
+.given-empty { padding: 0.75rem 1rem; color: var(--text-muted); font-style: italic; border: var(--rule-w) dashed var(--border); border-radius: var(--radius-sm); margin-bottom: 0.75rem; }
+
+/* ---- expect tooltip (cute-dbt#146 contract) --------------------------- */
+/* The load-bearing affordance paints on hover AND keyboard focus (a native
+   `title` does neither reliably): a focusable button with a CSS bubble. */
+.expect-tooltip { position: relative; display: inline-flex; align-items: center; border: none; background: transparent; padding: 0; margin-left: 0.25rem; font: inherit; font-size: 0.9em; color: var(--accent); cursor: help; border-bottom: 1px dotted currentColor; }
+.expect-tooltip-bubble {
+  position: absolute; left: 50%; top: calc(100% + 6px); transform: translateX(-50%); z-index: 60;
+  visibility: hidden; opacity: 0; transition: opacity 0.1s ease-in; width: 20rem; max-width: min(70vw, calc(100vw - 16px));
+  padding: 0.5rem 0.65rem; background: var(--tooltip-bg); color: var(--tooltip-fg); border-radius: var(--radius-pan);
+  /* cute-dbt#232 (audit D3) — tip text matches the column-tooltip
+     description: the 12px .col-tooltip shell base x 1.12em .ct-desc =
+     13.44px, in ABSOLUTE px because Sakura sets html{font-size:62.5%}
+     (1rem = 10px — the old 0.78rem computed to 7.8px, 58% of spec). */
+  box-shadow: 0 2px 10px rgba(0,0,0,0.35); font-size: 13.44px; font-weight: normal; line-height: 1.45;
+  text-align: left; text-transform: none; letter-spacing: normal; white-space: normal; pointer-events: none;
+}
+.expect-tooltip:hover .expect-tooltip-bubble, .expect-tooltip:focus .expect-tooltip-bubble, .expect-tooltip:focus-visible .expect-tooltip-bubble { visibility: visible; opacity: 1; }
+
+/* cute-dbt#202 — badge-borne bubbles (incremental branch · prior model
+   state): the #146 focusable-trigger CSS-bubble mechanism with the pass-2
+   badge-borne visual styling (founder decision, epic #197 — the expected
+   panel's separate ⓘ button is retired; the static DAG-hint ⓘ keeps the
+   .expect-tooltip dress above). The badge itself is the focusable trigger;
+   the nested bubble reveals on hover AND keyboard focus; no ancestor of
+   either badge clips (the given .table-header and the expected header/bar
+   have no overflow rule). */
+.mode-badge.has-mode-tip, .this-badge.has-mode-tip { position: relative; cursor: help; }
+.mode-badge.has-mode-tip:focus-visible, .this-badge.has-mode-tip:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
+.mode-badge.has-mode-tip .expect-tooltip-bubble,
+.this-badge.has-mode-tip .expect-tooltip-bubble { left: 0; transform: none; z-index: 60; box-shadow: 0 6px 22px rgba(0,0,0,0.3); border-radius: var(--radius-sm); /* cute-dbt#233 (audit D6) — pass-2 rounds the badge-borne bubble at --radius-sm; the shared .expect-tooltip-bubble keeps --radius-pan */ }
+.mode-badge.has-mode-tip:hover .expect-tooltip-bubble,
+.mode-badge.has-mode-tip:focus .expect-tooltip-bubble,
+.mode-badge.has-mode-tip:focus-visible .expect-tooltip-bubble,
+.this-badge.has-mode-tip:hover .expect-tooltip-bubble,
+.this-badge.has-mode-tip:focus .expect-tooltip-bubble,
+.this-badge.has-mode-tip:focus-visible .expect-tooltip-bubble { visibility: visible; opacity: 1; }
+
+/* cute-dbt#232 (audit D1) — edge-aware bubble anchoring. Belt-and-braces
+   first (the spec's own base.css right-anchor precedent): a mode badge
+   still riding the .panel-header (only the sql-format / external-fixture
+   early-return paths since #232) sits at the panel's right edge, so its
+   bubble opens leftward. */
+.expected-panel .panel-header .mode-badge.has-mode-tip .expect-tooltip-bubble { left: auto; right: 0; transform: none; }
+/* The JS edge tagger (tagTipEdge — geometry only; visibility stays pure
+   CSS per the #146 contract) annotates the trigger it measured against the
+   live viewport. The doubled class bumps specificity above the
+   .expected-panel header rule (0,4,0) so the measured truth always wins. */
+.has-mode-tip.has-mode-tip[data-tip-edge="right"] .expect-tooltip-bubble,
+.expect-tooltip.expect-tooltip[data-tip-edge="right"] .expect-tooltip-bubble { left: auto; right: 0; transform: none; }
+.has-mode-tip.has-mode-tip[data-tip-edge="left"] .expect-tooltip-bubble,
+.expect-tooltip.expect-tooltip[data-tip-edge="left"] .expect-tooltip-bubble { left: 0; right: auto; transform: none; }
+
+.fixture-sql-block-wrap .sql-block { padding-top: 0.75rem; }
+.given-section + .given-section { margin-top: 1rem; }
+.table-header { display: flex; align-items: baseline; gap: 0.5rem; margin: 0.5rem 0 0.25rem; }
+
+/* --- given header ref() pill + tip triggers (cute-dbt#202) ------------- */
+.given-section .table-title { display: inline-flex; align-items: center; gap: 0; flex-wrap: wrap; font-family: var(--font-mono); font-size: 0.8rem; }
+.gt-prefix { color: var(--text-muted); font-weight: 400; }
+.gt-model {
+  font-weight: 700; color: var(--accent-hover); white-space: nowrap;
+  background: var(--accent-tint); border: var(--rule-w) solid color-mix(in oklab, var(--accent) 40%, transparent);
+  border-radius: var(--radius-sm); padding: 0.18rem 0.5em; margin: 0 0.1em; font-size: 0.82rem; line-height: 1.4;
+}
+.gt-model.has-model-tip { cursor: help; }
+.gt-model.has-model-tip:hover { border-color: var(--accent); }
+.gt-model.has-model-tip:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
+.format-badge.has-fmt-tip { cursor: help; }
+.format-badge.has-fmt-tip:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
+
+/* expected model pill + header layout. cute-dbt#232 (audit D2): table
+   renders ALWAYS relocate the meta onto the .fixture-view-bar below the
+   title (mirroring Given), so the far-right header slot below applies ONLY
+   to the sql-format / external-fixture early-return paths, where the row
+   count / mode badge stay in the header (spec-consistent). */
+.expected-model-badge { cursor: default; }
+.expected-panel .panel-header { justify-content: flex-start; }
+.expected-panel .panel-header .expected-rowcount { margin-left: auto; }
+
+/* ---- tables ---------------------------------------------------------- */
+.table-fit { width: 100%; overflow-x: auto; }
+table.given-table, table.expected-table { width: max-content; min-width: 100%; }
+table.given-table th, table.expected-table th { font-weight: 700; border-bottom: 2px solid var(--accent); }
+table.given-table td, table.expected-table td { white-space: nowrap; color: var(--text); }
+.cell-null { color: var(--text-muted); font-style: italic; }
+.cell-absent { color: var(--text-faint); }
+.cell-num { text-align: right; font-variant-numeric: tabular-nums; }
+
+/* ---- cell-level diff ------------------------------------------------- */
+.cell-diff-toggle { display: flex; margin: 0.5rem 0 0; }
+/* the Diff/File toggle row (cute-dbt#202 order): meta reads left-to-right —
+   [model pill] [mode badge] [row count] — with the toggle pushed to the
+   far right (matches the Given bars). */
+.fixture-view-bar { display: flex; align-items: center; gap: 0.45rem; flex-wrap: wrap; margin: 0.5rem 0 0.5rem; }
+.fixture-view-bar .cell-diff-toggle { margin: 0 0 0 auto; }
+.fixture-view-bar .mode-badge, .fixture-view-bar .row-count-badge { margin: 0; }
+tr.row-added   td { background: var(--diff-add-row); }
+tr.row-removed td { background: var(--diff-remove-row); }
+tr.row-added   td:first-child { box-shadow: inset 3px 0 0 var(--diff-add-edge); }
+tr.row-removed td:first-child { box-shadow: inset 3px 0 0 var(--diff-remove-edge); }
+tr.row-removed td { text-decoration: line-through; text-decoration-color: var(--diff-remove-edge); }
+th.col-added .col-status, th.col-removed .col-status { font-size: 0.7em; font-weight: 700; margin-left: 0.3rem; border-radius: 2px; padding: 0 2px; }
+th.col-added .col-status   { background: var(--col-add-chip);    color: var(--col-add-on); }
+th.col-removed .col-status { background: var(--col-remove-chip); color: var(--col-remove-on); }
+th.col-removed { text-decoration: line-through; text-decoration-color: var(--diff-remove-edge); }
+.cell-old { color: var(--diff-remove-fg); font-weight: 700; }
+.cell-new { color: var(--diff-add-fg); font-weight: 700; }
+.cell-arrow { color: var(--diff-arrow); margin: 0 0.3ch; }
+
+/* The #188 [data-diffstyle] "marks" alt encoding was retired by design
+   pass-2 (cute-dbt#198): diff cells always render in colour. */
+
+/* ---- SQL / YAML drawers + syntax ------------------------------------- */
+.compiled-sql > summary, .authoring-yaml > summary, .model-sql > details > summary, .model-yaml > details > summary { cursor: pointer; }
+.authoring-yaml > summary, .model-sql > details > summary, .model-yaml > details > summary { font-weight: 600; color: var(--text); }
+.model-sql-summary-hint, .model-yaml-summary-hint, .yaml-diff-summary-hint { font-weight: 400; color: var(--text-muted); margin-left: 0.4rem; font-size: 0.9em; }
+.sql-block { margin: 0; padding: 0.4rem 0; background: var(--code-bg); border-radius: 0; color: var(--text); font-size: 12px; line-height: 1.6; font-variant-numeric: tabular-nums; overflow-x: auto; }
+.sql-block code { display: block; }
+.sql-block-wrap, .model-sql-wrap {
+  position: relative; margin-top: 0.6rem;
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-pan);
+  overflow: hidden; background: var(--code-bg);
+}
+.sql-copy {
+  position: absolute; top: 6px; right: 6px; z-index: 4; font-size: 0.72rem; padding: 0.12rem 0.55rem; line-height: 1.5;
+  background: var(--surface); border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  color: var(--text-muted); cursor: pointer; opacity: 0;
+  transition: opacity 120ms ease, background 120ms ease, color 120ms ease; font-family: var(--font-ui);
+}
+.sql-block-wrap:hover .sql-copy, .model-sql-wrap:hover .sql-copy, .sql-copy:focus-visible { opacity: 1; }
+.sql-copy:hover { background: var(--bg-alt); color: var(--text); border-color: var(--border-strong); }
+.sql-copy.copied { background: var(--accent-tint); border-color: var(--accent); color: var(--accent-hover); opacity: 1; }
+.sql-copy.copy-failed { background: var(--error-bg); border-color: var(--error); color: var(--error); opacity: 1; }
+
+.error-card { display: none; border: 2px solid var(--error); background: var(--error-bg); padding: 1rem 1.25rem; margin: 0 0 1.5rem; border-radius: var(--radius-pan); }
+.error-card.is-visible { display: block; }
+.error-card h2 { margin-top: 0; color: var(--error); }
+.error-card .error-node-id { font-family: var(--font-mono); color: var(--error); }
+.mermaid-error { color: var(--error); }
+.muted { color: var(--text-muted); }
+.recursive-banner { background: var(--badge-amber-bg); border-left: 4px solid var(--badge-amber-edge); padding: 0.5rem 0.75rem; margin: 0.5rem 0; font-size: 0.85em; color: var(--badge-amber); border-radius: 0 var(--radius-sm) var(--radius-sm) 0; }
+
+/* Syntax palette (cute-dbt#132) — cool/neutral hues chosen to AVOID red +
+   green so syntax colors never clash with the red/green diff highlight. */
+.sql-keyword, .yaml-key   { color: var(--syntax-keyword); font-weight: 700; }
+.sql-string,  .yaml-string { color: var(--syntax-string); }
+.sql-comment, .yaml-comment { color: var(--syntax-comment); font-style: italic; }
+.sql-jinja   { color: var(--syntax-jinja); }
+.authoring-yaml { margin-top: 0.75rem; }
+
+/* ---- unified diff + numbered code lines (GitHub-grade) --------------- */
+.yaml-diff-toggle { display: flex; margin: 0.5rem 0 0; }
+/* framed code-card header: file path (left) + Diff/File toggle (right) */
+.code-header {
+  display: flex; align-items: center; gap: 0.6rem; flex-wrap: wrap;
+  padding: 0.35rem 0.7rem;
+  background: var(--code-header-bg); border-bottom: var(--rule-w) solid var(--border); font-size: 0.82em;
+}
+.code-filename {
+  flex: 0 1 auto; min-width: 0; font-family: var(--font-mono); font-size: 0.96em;
+  color: var(--text-muted); white-space: nowrap; overflow: hidden; text-overflow: ellipsis;
+}
+.code-header-spacer { flex: 1 1 auto; min-width: 0.5rem; }
+.code-header .yaml-diff-toggle { margin: 0 0 0 auto; flex: 0 0 auto; }
+/* copy-to-clipboard icon button (cute-dbt#199) — inline SVG, class flash */
+.code-copy-btn { font: inherit; font-size: 0.92em; padding: 0.12rem 0.6rem; border: var(--rule-w) solid var(--control-border); background: var(--control-bg); color: var(--control-fg); cursor: pointer; border-radius: var(--radius-sm); }
+.code-copy-btn.icon-btn { display: inline-flex; align-items: center; justify-content: center; padding: 0.22rem 0.4rem; }
+.code-copy-btn .icon { width: 0.95rem; height: 0.95rem; }
+.code-copy-btn:hover { filter: brightness(0.97); }
+.code-copy-btn.copied { background: var(--accent-tint); border-color: var(--accent); color: var(--accent-hover); }
+.code-copy-btn.copy-failed { background: var(--error-bg); border-color: var(--error); color: var(--error); }
+/* relocated incremental badge — hugs its text with ~15% side padding */
+.code-header .incremental-badge {
+  flex: 0 0 auto; font-size: 0.86em; line-height: 1.35;
+  padding: 0.16em 0.62em; letter-spacing: 0.04em;
+}
+.yaml-view-btn { font: inherit; font-size: 0.92em; padding: 0.12rem 0.6rem; border: var(--rule-w) solid var(--control-border); background: var(--control-bg); color: var(--control-fg); cursor: pointer; }
+.yaml-view-btn:first-child { border-radius: var(--radius-sm) 0 0 var(--radius-sm); }
+.yaml-view-btn:last-child { border-radius: 0 var(--radius-sm) var(--radius-sm) 0; border-left: none; }
+.yaml-view-btn.active { background: var(--control-active-bg); color: var(--control-active-fg); border-color: var(--control-active-bg); }
+
+/* Each line is a flex row: [gutter][sigil][code]. The code column soft-wraps
+   (flex:1; min-width:0) so long lines never produce a horizontal scrollbar;
+   wrapped continuation lines hang-indent under the code start, not the gutter.
+   The gutter stays sticky-left and stretches to the (possibly multi-line) row
+   height. (The engine emits this line DOM since cute-dbt#178.) */
+.diff-line, .code-line { display: flex; align-items: stretch; white-space: normal; }
+.code-line:hover { background: var(--code-line-hover); }
+
+.diff-gutter, .code-gutter {
+  position: sticky; left: 0; z-index: 2; flex: 0 0 auto; user-select: none;
+  text-align: right; color: var(--code-gutter-fg);
+  background: var(--code-gutter-bg); border-right: var(--rule-w) solid var(--border);
+}
+.diff-gutter { display: flex; }
+.code-gutter { box-sizing: border-box; width: 3.4em; padding: 0 0.7em 0 0.5em; }
+.dln { box-sizing: border-box; flex: 0 0 auto; display: inline-block; width: 2.8em; padding: 0 0.5em; text-align: right; }
+.diff-gutter-hunk { box-sizing: border-box; width: 5.6em; }
+
+/* cute-dbt#251 — no opacity dimming on the sigil: composited opacity is
+   not used for text (the #227 rule); the per-theme --code-add/rem-sigil
+   tokens carry the full AA ratio and the headless AA-matrix guard fails
+   loudly if a self-or-ancestor opacity returns. */
+.diff-sigil { flex: 0 0 auto; width: 2ch; padding-left: 0.6ch; text-align: left; font-weight: 700; user-select: none; }
+.diff-code { flex: 1 1 auto; min-width: 0; white-space: pre-wrap; overflow-wrap: anywhere; }
+.diff-fold-label { flex: 1 1 auto; }
+
+.diff-added   { background: var(--code-add-bg); }
+.diff-removed { background: var(--code-rem-bg); }
+.diff-added   .diff-gutter { box-shadow: inset 0 0 0 999px var(--code-add-gutter); border-right-color: transparent; color: var(--text-muted); }
+.diff-removed .diff-gutter { box-shadow: inset 0 0 0 999px var(--code-rem-gutter); border-right-color: transparent; color: var(--text-muted); }
+.diff-added   .diff-sigil { color: var(--code-add-sigil); }
+.diff-removed .diff-sigil { color: var(--code-rem-sigil); }
+
+/* hunk fold (cute-dbt#199) — the line-number gutter carries the +/− steppers
+   (GitHub-style); the control re-parks adjacent to the remaining hidden run,
+   or below the run once fully revealed. */
+.diff-fold { cursor: pointer; background: var(--code-hunk-bg); color: var(--code-hunk-fg); font-style: normal; }
+.diff-fold:hover { filter: brightness(0.98); }
+.diff-fold .diff-gutter-hunk {
+  box-shadow: inset 0 0 0 999px var(--code-hunk-bg); border-right-color: transparent;
+  display: inline-flex; align-items: center; justify-content: center; padding: 0;
+}
+/* gutter +/− steppers: + expands by the user's step, − contracts */
+.fold-steppers { display: inline-flex; align-items: center; gap: 1px; }
+.fold-steppers button {
+  display: inline-flex; align-items: center; justify-content: center;
+  width: 1.25em; height: 1.25em; padding: 0; line-height: 1; font-weight: 700; font-size: 0.95em;
+  border: var(--rule-w) solid color-mix(in oklab, var(--code-hunk-fg) 45%, transparent);
+  border-radius: 3px; background: transparent; color: var(--code-hunk-fg); cursor: pointer;
+}
+.fold-steppers button:hover:not(:disabled) { background: color-mix(in oklab, var(--code-hunk-fg) 18%, transparent); }
+.fold-steppers button:disabled { opacity: 0.32; cursor: default; }
+.diff-fold .diff-sigil { width: 0; padding: 0; }
+.diff-fold-label { color: var(--code-hunk-fg); font-style: normal; flex: 0 0 auto; }
+.fold-collapse-all { flex: 0 0 auto; margin: 0 auto; font: inherit; font-size: 0.92em; padding: 0 0.55em; border: var(--rule-w) solid var(--code-hunk-fg); border-radius: var(--radius-sm); background: transparent; color: var(--code-hunk-fg); cursor: pointer; }
+.fold-collapse-all:hover { background: color-mix(in oklab, var(--code-hunk-fg) 16%, transparent); }
+
+/* per-diff Expand/Collapse-all toggle (cute-dbt#199) — lives in each diff's
+   code header; the #132 top-of-report .diff-view-controls strip is retired. */
+.diff-fold-toggle { font: inherit; font-size: 0.92em; padding: 0.12rem 0.6rem; border: var(--rule-w) solid var(--control-border); background: var(--control-bg); color: var(--control-fg); cursor: pointer; border-radius: var(--radius-sm); }
+.diff-fold-toggle:hover { filter: brightness(0.97); }
+.diff-fold-toggle[aria-pressed="true"] { background: var(--control-active-bg); color: var(--control-active-fg); border-color: var(--control-active-bg); }
+.diff-added strong   { font-weight: 700; background: var(--code-add-word); color: inherit; border-radius: 2px; padding: 0 1px; }
+.diff-removed strong { font-weight: 700; background: var(--code-rem-word); color: inherit; border-radius: 2px; padding: 0 1px; }
+
+/* ---- split (side-by-side) diff --------------------------------------- */
+/* both layouts are emitted; show exactly one per html[data-difflayout].
+   (Live since cute-dbt#178: diffViewsHtml emits the unified <code> AND the
+   split <table> side by side.) */
+.diff-unified { display: block; }
+.diff-split { display: none; width: 100%; border-collapse: collapse; table-layout: fixed; font-variant-numeric: tabular-nums; }
+/* explicit column widths so the two code halves are a true 50/50 that grows
+   and shrinks with the viewport (the fold row's colspan cell must NOT define
+   widths). */
+.diff-split col.ds-c-num { width: 3.8em; }
+.diff-split col.ds-c-code { width: auto; }
+.diff-split td { vertical-align: top; padding: 0; border: 0; }
+.diff-split .ds-num {
+  text-align: right; padding: 0 0.55em; white-space: nowrap; user-select: none;
+  color: var(--code-gutter-fg); background: var(--code-gutter-bg); border-right: var(--rule-w) solid var(--border);
+}
+/* separator between the two equal halves: a clear divider + small gap */
+.diff-split tr > td.ds-num:nth-child(3) { border-left: var(--rule-w) solid var(--border); padding-left: 0.7rem; }
+.diff-split tr > td.ds-fold-gutter:nth-child(3) { border-left: var(--rule-w) solid var(--border); }
+.diff-split .ds-code { overflow: hidden; }
+.diff-split .ds-codewrap { display: flex; align-items: flex-start; padding-right: 0.5rem; }
+.diff-split .ds-code .diff-sigil { flex: 0 0 auto; width: 1.6ch; padding-left: 0.4ch; }
+/* wrap long lines inside the fixed-width cell — never a per-line scrollbar —
+   and hang-indent continuation lines under the code start (flex column), not
+   the far-left edge under the line numbers. */
+.diff-split .ds-code .diff-code { flex: 1 1 auto; min-width: 0; white-space: pre-wrap; overflow-wrap: anywhere; }
+.diff-split .ds-added   { background: var(--code-add-bg); }
+.diff-split .ds-removed { background: var(--code-rem-bg); }
+.diff-split .ds-num.ds-added   { box-shadow: inset 0 0 0 999px var(--code-add-gutter); border-right-color: transparent; color: var(--text-muted); }
+.diff-split .ds-num.ds-removed { box-shadow: inset 0 0 0 999px var(--code-rem-gutter); border-right-color: transparent; color: var(--text-muted); }
+.diff-split .ds-added   .diff-sigil { color: var(--code-add-sigil); }
+.diff-split .ds-removed .diff-sigil { color: var(--code-rem-sigil); }
+.diff-split .ds-empty { background: var(--code-header-bg); }
+
+/* split-view fold row (cute-dbt#199 — mirrors the unified hunk fold; one
+   solid full-width band with the gutter steppers + a colspan label cell) */
+.diff-split tr.diff-fold { cursor: pointer; background: var(--code-hunk-bg); color: var(--code-hunk-fg); }
+.diff-split tr.diff-fold:hover { filter: brightness(0.98); }
+.diff-split .ds-fold-gutter { box-sizing: border-box; border: 0; padding: 0.1rem 0.2rem; text-align: center; color: var(--code-hunk-fg); }
+.diff-split .ds-fold-gutter .fold-steppers { justify-content: center; }
+.diff-split .ds-fold-cell { padding: 0.12rem 0.6rem; border: 0; }
+.diff-split .ds-fold-inner { display: flex; align-items: center; width: 100%; }
+.diff-split .ds-fold-cell .diff-fold-label { color: var(--code-hunk-fg); flex: 0 0 auto; }
+
+/* layout selection: Unified is the default (above). AUTO => split when wide;
+   explicit Split => split down to a smaller floor, then fall back to unified. */
+@media (min-width: 1000px) {
+  html[data-difflayout="auto"] .diff-unified { display: none; }
+  html[data-difflayout="auto"] .diff-split   { display: table; }
+}
+@media (min-width: 760px) {
+  html[data-difflayout="split"] .diff-unified { display: none; }
+  html[data-difflayout="split"] .diff-split   { display: table; }
+}
+html[data-difflayout="unified"] .diff-split { display: none; }
+html[data-difflayout="unified"] .diff-unified { display: block; }
+
+/* ---- Model SQL ------------------------------------------------------- */
+.model-sql-block { height: 24rem; min-height: 8rem; overflow: auto; resize: vertical; }
+
+/* ---- Model YAML (cute-dbt#247) ---------------------------------------- */
+/* The authored schema-entry block: capped like the Model SQL card but
+   shorter by default (schema entries are usually narrower than model SQL),
+   same resize affordance. max-height (not height) so a short entry does
+   not float in empty chrome. */
+.model-yaml-block { max-height: 24rem; min-height: 4rem; overflow: auto; resize: vertical; }
+/* The truthful degrade placeholder — muted prose inside the code chrome,
+   never an empty card. */
+.model-yaml-missing { margin: 0; padding: 0.6rem 0.75rem; color: var(--text-muted); font-size: 0.85em; }
+
+/* ---- column-header info tooltip --------------------------------------- */
+/* DELIBERATE divergence from the handoff's base.css (decided at PR-1, kept
+   at the engine PR cute-dbt#178): the redesign's `.col-tooltip` was a
+   body-appended position:fixed bubble keyed off `th.has-col-meta` and fed by
+   a ModelPayload.column_meta contract that does not exist yet. The SHIPPED
+   cute-dbt#165 / cute-dbt#166 contract — a focusable <button> trigger inside
+   the th with a CSS bubble on :hover AND :focus, headless-guarded — wins;
+   its tokenized rules live in the reconciliation layer below. Any swap to
+   the handoff's renderer belongs to the column_meta PR (handoff PR-3), with
+   the headless guards carried forward. */
+
+/* ---- density override (Tweak; wins over the direction's :root values) - */
+:root[data-density="compact"] {
+  --fs-base: 12px; --lh-base: 1.42; --pad-cell: 0.12rem 0.4rem;
+  --pad-panel: 0.7rem; --gap-section: 0.7rem;
+}
+:root[data-density="cozy"] {
+  --fs-base: 13px; --lh-base: 1.5; --pad-cell: 0.2rem 0.5rem;
+  --pad-panel: 1rem; --gap-section: 1rem;
+}
+:root[data-density="comfortable"] {
+  --fs-base: 14.5px; --lh-base: 1.62; --pad-cell: 0.34rem 0.7rem;
+  --pad-panel: 1.25rem; --gap-section: 1.4rem;
+}
+
+/* ============================================================================
+   5. cute-dbt RECONCILIATION LAYER (permanent)
+   ----------------------------------------------------------------------------
+   Shipped affordances the handoff's engine forked before. These are not
+   bridge shims — they carry post-fork contracts (#165/#166) forward and stay
+   until their owning feature PR replaces them explicitly.
+   ========================================================================== */
+
+/* cute-dbt#165 + cute-dbt#166 + cute-dbt#178 — column-header tooltip, now
+   the handoff spec (rules ported from its base.css): the WHOLE header cell
+   is the hover/focus trigger — no per-column icon, no native title. The
+   bubble is the single body-appended #col-tooltip, position:fixed with
+   JS-set coordinates (the th lives inside the .table-fit overflow-x
+   scroller, which would clip an absolutely-positioned bubble; fixed +
+   horizontal clamp + the flip-above fallback never clips). The #166 a11y
+   contract carries forward on the th itself: tabindex makes it
+   keyboard-focusable, aria-label is the AT surface, the bubble stays
+   aria-hidden, hover AND focus both reveal. */
+th.has-col-meta { cursor: help; }
+th.has-col-meta:focus-visible { outline: 2px solid var(--accent); outline-offset: -2px; }
+.col-tooltip {
+  position: fixed; z-index: 90; min-width: 12rem;
+  /* cute-dbt#240 (defects A+D) — the bubble must CONTAIN its content:
+     (1) the width cap can never exceed the viewport (the calc term mirrors
+     the #232/#234 CSS-bubble cap, so positionTipNear's clamp always has a
+     box that fits), and (2) long unbreakable mono tokens (test names,
+     override values) wrap inside the painted background instead of
+     painting past its right edge. */
+  max-width: min(24rem, calc(100vw - 16px));
+  overflow-wrap: anywhere;
+  background: var(--tooltip-bg); color: var(--tooltip-fg);
+  border-radius: var(--radius-pan); box-shadow: 0 6px 22px rgba(0,0,0,0.32);
+  padding: 0.7rem 0.8rem; font-size: 12px; line-height: 1.45;
+  font-weight: normal; text-transform: none; letter-spacing: normal; pointer-events: none;
+}
+.col-tooltip .ct-desc { font-size: 1.12em; line-height: 1.5; opacity: 0.97; }
+/* cute-dbt#240 (defect B) — the truthful no-metadata fallback line. */
+.col-tooltip .ct-empty { margin-top: 0.2rem; font-size: 0.95em; opacity: 0.75; }
+.col-tooltip .ct-tests-label { margin-top: 0.6rem; font-size: 0.82em; text-transform: uppercase; letter-spacing: 0.07em; opacity: 0.6; }
+.col-tooltip .ct-tests { display: flex; flex-direction: column; gap: 0.4rem; margin-top: 0.3rem; }
+.col-tooltip .ct-test { display: flex; flex-wrap: wrap; align-items: baseline; gap: 0.25rem 0.7rem; }
+/* cute-dbt#233 (audit D4) — the pass-2 readable-on-dark accent: raw
+   var(--accent) lands ≈3:1 against the always-dark tooltip fill on the
+   4 light themes (AA fail); the oklab mix toward white clears 4.5:1 on
+   all 8 themes (headless-pinned). */
+.col-tooltip .ct-key { font-family: var(--font-mono); font-weight: 700; color: color-mix(in oklab, var(--accent) 60%, white); }
+/* cute-dbt#233 — Latte ct-key AA: the spec's own 60% mix lands 3.37:1 on
+   latte's tooltip fill (#4c4f69 — the lightest of the 8; the spec tokens
+   carry the same value, so this is empirically the spec's gap, not ours).
+   The #198/#206/#227 pattern: a scoped stand-in only — the mix shifts
+   toward white for latte alone (≈5:1 measured); the verbatim latte theme
+   block and the other 7 themes' spec-literal mix stay pinned. Covers the
+   model tip too (it rides the .col-tooltip shell). */
+:root[data-theme="latte"] .col-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 35%, white); }
+.col-tooltip .ct-vals { display: inline-flex; flex-wrap: wrap; gap: 0.25rem; }
+/* cute-dbt#240 (defect A) — chips wrap mid-token when (and only when) a
+   single value exceeds the bubble width: `anywhere` breaks ONLY on
+   overflow, so the short shipped args stay one-line chips. */
+.col-tooltip .ct-val { background: rgba(255,255,255,0.14); border-radius: 4px; padding: 0.05rem 0.42rem; font-family: var(--font-mono); font-size: 0.9em; overflow-wrap: anywhere; }
+/* (cute-dbt#202 — the .ct-detail run is retired: detail strings now split
+   into the same ct-vals/ct-val chips the values branch uses.) */
+
+/* cute-dbt#202 — model-ref + overrides tips reuse the .col-tooltip shell
+   (body-appended #model-tooltip / #ov-tooltip singletons, position:fixed
+   via the shared class above; hardcoded whites follow the ct-val
+   precedent — the tooltip interior is dark in every theme). */
+/* cute-dbt#240 (defect D) — the wider tips keep the same can-never-exceed-
+   the-viewport cap so the positionTipNear clamp always fully contains them. */
+.col-tooltip.ov-tooltip, .col-tooltip.model-tooltip { max-width: min(32rem, calc(100vw - 16px)); }
+.model-tooltip .mt-chips { display: flex; flex-wrap: wrap; gap: 0.3rem; margin-bottom: 0.45rem; }
+.model-tooltip .ct-desc { font-size: 1.3em; line-height: 1.5; }
+.model-tooltip .mt-mat { font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.05em; color: #f0c674; border: 1px solid currentColor; border-radius: 999px; padding: 0.02rem 0.5rem; }
+.model-tooltip .mt-tag { font-size: 0.72rem; color: rgba(255,255,255,0.82); background: rgba(255,255,255,0.12); border-radius: 999px; padding: 0.02rem 0.5rem; }
+/* cute-dbt#235 — model-level tests inside the model-ref tip ride the
+   COLUMN tooltip's own .ct-test/.ct-key/.ct-vals anatomy (the shared
+   ctTestHtml builder — never a parallel chip system; the .mt-mtests/
+   .dt-* dress is retired here, the shelf card keeps its light-surface
+   .dt-* chips). Test names keep the readable-on-dark accent treatment
+   the retired .model-tooltip .dt-key carried; the value chips inherit
+   .col-tooltip .ct-val verbatim. The base .col-tooltip .ct-key color
+   is cute-dbt#233's lane — only this model-scoped override lives here
+   (same specificity, later in file, so it wins inside the model tip). */
+.model-tooltip .ct-key { color: color-mix(in oklab, var(--accent) 60%, white); }
+.ov-tooltip .ov-grp + .ov-grp { margin-top: 0.55rem; }
+.ov-tooltip .ov-grp-name { font-family: var(--font-mono); font-size: 0.78em; text-transform: uppercase; letter-spacing: 0.06em; opacity: 0.6; margin-bottom: 0.25rem; }
+/* cute-dbt#240 (defect D) — the nowrap run made any key/value longer than
+   the bubble's cap paint past the painted background and clip at the
+   viewport's right edge near right-edge triggers. Rows now flex-wrap and
+   long tokens break inside the bubble; colors stay verbatim (cute-dbt#238
+   owns the ov-tooltip palette). */
+.ov-tooltip .ov-row { display: flex; flex-wrap: wrap; gap: 0.15rem 0.5rem; align-items: baseline; padding-left: 0.45rem; }
+.ov-tooltip .ov-key, .ov-tooltip .ov-row code { background: none; padding: 0; font-family: var(--font-mono); font-weight: 700; color: color-mix(in oklab, var(--accent) 50%, white); overflow-wrap: anywhere; }
+/* cute-dbt#238 — Latte ov-key AA: the all-theme 50% accent mix lands
+   3.69:1 on latte's tooltip fill (#4c4f69 — the lightest of the 8; #273's
+   light-page accent deepening made this dark-fill surface marginally
+   worse). The symmetric twin of #233 D4's .ct-key latte stand-in at the
+   same 35% mix (measured 4.76 PASS; 40% still fails at 4.38); the other
+   7 themes' 50% mix above stays pinned. */
+:root[data-theme="latte"] .ov-tooltip .ov-key,
+:root[data-theme="latte"] .ov-tooltip .ov-row code {
+  color: color-mix(in oklab, var(--accent) 35%, white);
+}
+.ov-tooltip .ov-eq { opacity: 0.45; }
+.ov-tooltip .ov-val { font-family: var(--font-mono); color: rgba(255,255,255,0.9); overflow-wrap: anywhere; min-width: 0; }
+
+/* cute-dbt#202 — the format-block tip (#fmt-tooltip): a mini framed code
+   surface (NOT the dark .col-tooltip shell — it reuses the code-card
+   chrome so the reconstruction reads like the drawers). */
+.fmt-tooltip {
+  position: fixed; z-index: 90; max-width: min(34rem, 92vw);
+  /* cute-dbt#246 — never a reachability fold: the bubble is
+     pointer-events:none and hides on mouseleave, so anything laid past
+     the old fixed 22rem max-height could never be scrolled to or read.
+     The JS side now bounds the content by REAL MEASUREMENT (trim lines
+     until the laid-out content fits + a truthful elision row), and the
+     height cap is viewport-relative — the #240 defect-D width-cap
+     pattern — so positionTipNear's post-flip top clamp always fully
+     contains the box on the height axis, at any viewport height.
+     overflow:auto stays as the safety net (PR #286 review): if some
+     exotic rendering still defeats the measured trim, the mismatch
+     degrades to a visible scrollbar — a signal — never a silent clip. */
+  max-height: calc(100vh - 16px); overflow: auto;
+  border: var(--rule-w) solid var(--border-strong); border-radius: var(--radius-pan);
+  box-shadow: 0 8px 26px rgba(0,0,0,0.34); background: var(--code-bg);
+  pointer-events: none;
+}
+.fmt-tooltip .code-header { font-size: 0.74rem; }
+.fmt-tooltip .sql-block { margin: 0; padding: 0.4rem 0; }
+.fmt-tooltip .code-line { padding-right: 1rem; }
+/* cute-dbt#246 — the truthful elision row under the trimmed block. Plain
+   var(--text) on the bubble's var(--code-bg) (the code-line pairing the
+   8-theme AA matrix already covers); fixed px so the Sakura 62.5% root
+   (1rem = 10px) cannot shrink it unreadable. */
+.fmt-tooltip .fmt-more { padding: 0.15rem 0.8rem 0.4rem; font-size: 11px; color: var(--text); }
+
+/* ============================================================================
+   6. ENGINE-PR ADDITIONS (cute-dbt#178)
+   ----------------------------------------------------------------------------
+   The PR-1 bridge layer that lived here (block-layout .diff-line shims, the
+   legacy .sql-block copy-button clearance, the text-glyph cog sizing) is
+   DELETED per its own contract: the engine now emits the redesign's line DOM
+   ([gutter][sigil][code] flex rows, code-card headers, the SVG cog), so the
+   chassis rules above apply directly. What remains below is the small chrome
+   the static settings-panel markup needs.
+   ========================================================================== */
+
+/* cute-dbt#136 / cute-dbt#199 — a fully-expanded fold control stays visible
+   (re-parked below its run, "All N lines shown" + collapse-all); mark it
+   among many hunks. */
+.diff-fold[aria-expanded="true"] { filter: brightness(0.96); }
+
+/* The Model SQL section keeps its vertical rhythm (it sits outside the
+   .panel spacing list). The Model YAML section (cute-dbt#247) is its
+   peer and keeps the same rhythm. */
+.model-sql, .model-yaml { margin-bottom: var(--gap-section); }
+
+/* Stacked settings rows (the appearance section): the control grid sits
+   UNDER its label, full-width — the prototype expressed this with inline
+   styles; the static template markup uses this class instead. */
+.settings-row.settings-row-stack { display: block; }
+.settings-row.settings-row-stack > label { display: block; margin-bottom: 0.35rem; }
+
+/* ============================================================================
+   7. COVERAGE-CHECKS PANEL (cute-dbt#170)
+   ----------------------------------------------------------------------------
+   The per-model findings surface. Token-native: every colour rides the
+   semantic layer so all eight themes + four style packs apply. Trust-model
+   rules made visual: the three verdicts are a CHECKLIST (mark + word, never
+   colour-only); the three tiers are LABELED chips with distinct emphasis
+   (filled / outlined / muted — never blended, no score, no percentage);
+   suppressed findings are visible-but-quiet behind a collapsed count.
+   ========================================================================== */
+.model-findings { margin-bottom: var(--gap-section); }
+/* cute-dbt#219 — the viewer-side coverage-intelligence display toggle: the
+   settings switch sets html[data-coverage=off] (theme.js, persisted as the
+   `coverage` field of cute-dbt.appearance.v1) and this ONE rule hides every
+   check-engine-derived surface. The panel's rendered content stays in the
+   DOM (display-only — payload untouched), so ON restores without re-render. */
+html[data-coverage="off"] .model-findings { display: none; }
+/* cute-dbt#292 — the viewer-side project-state display toggle: the settings
+   switch sets html[data-project=off] (theme.js, persisted as the `project`
+   field of cute-dbt.appearance.v1) and these rules hide every project-state
+   surface: the project-definition panel (its categorized rows — vars /
+   config tree / hooks / dispatch / paths / identity — and the raw-diff
+   fallback live inside it), the per-model config-provenance chips, and the
+   per-model var-reference chips. Display-only, the data-coverage twin:
+   content stays in the DOM, ON restores without re-render. The toggle row
+   itself only renders when the report emitted project-state content
+   (cute-dbt#291's render-time experimental gate). */
+html[data-project="off"] .project-def-panel,
+html[data-project="off"] .model-attribution-row,
+html[data-project="off"] .var-reference-chip { display: none; }
+.findings-header { display: flex; align-items: baseline; gap: 0.6rem; margin-bottom: 0.45rem; flex-wrap: wrap; }
+.findings-header h2 {
+  font-size: var(--fs-label); text-transform: uppercase;
+  letter-spacing: var(--label-tracking); color: var(--text-muted); margin: 0;
+  font-family: var(--label-font); white-space: nowrap;
+}
+.findings-subtitle { margin: 0; color: var(--text-muted); font-size: 0.85em; }
+.findings-empty { margin: 0; padding: 0.55rem 0.8rem; color: var(--text-muted); font-style: italic; font-size: 0.9em; border: var(--rule-w) dashed var(--border); border-radius: var(--radius-sm); }
+.findings-tally { margin: 0 0 0.4rem; color: var(--text-muted); font-size: 0.85em; }
+
+.findings-checklist { list-style: none; margin: 0; padding: 0; display: flex; flex-direction: column; gap: 0.35rem; }
+.finding-row { margin: 0; border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm); background: var(--surface); }
+.finding-details > summary {
+  display: flex; align-items: center; gap: 0.5rem; flex-wrap: wrap;
+  padding: 0.35rem 0.6rem; cursor: pointer; list-style: none;
+}
+.finding-details > summary::-webkit-details-marker { display: none; }
+.finding-details > summary::after { content: "▸"; margin-left: auto; color: var(--text-faint); font-size: 0.8em; }
+.finding-details[open] > summary::after { content: "▾"; }
+.finding-details > summary:focus-visible { outline: 2px solid var(--accent); outline-offset: -2px; }
+
+/* three-valued checklist mark + word — never colour-only */
+.f-mark { font-weight: 700; width: 1.1em; text-align: center; flex: 0 0 auto; }
+.verdict-covered   .f-mark { color: var(--diff-add-fg); }
+.verdict-uncovered .f-mark { color: var(--warn); }
+.verdict-unknown   .f-mark { color: var(--text-faint); }
+.f-verdict {
+  font-size: 0.72em; text-transform: uppercase; letter-spacing: 0.05em;
+  font-family: var(--font-ui); white-space: nowrap; flex: 0 0 auto;
+}
+.verdict-covered   .f-verdict { color: var(--diff-add-fg); }
+.verdict-uncovered .f-verdict { color: var(--warn); font-weight: 700; }
+.verdict-unknown   .f-verdict { color: var(--text-muted); }
+.verdict-uncovered { border-color: var(--badge-amber-edge); }
+.f-name { color: var(--text); font-size: 0.9em; }
+.f-construct {
+  font-family: var(--font-mono); font-size: 0.78em; color: var(--text-muted);
+  background: var(--bg-alt); padding: 0.05rem 0.35rem; border-radius: var(--radius-sm);
+}
+
+/* tier chips — labeled, three DISTINCT emphasis levels, never blended:
+   TOTAL = solid fill, HIGH = outlined accent, advisory = muted outline. */
+.tier-chip {
+  font-size: 0.68em; padding: 0.12rem 0.55rem; border-radius: 999px;
+  text-transform: uppercase; letter-spacing: 0.05em;
+  font-family: var(--font-ui); white-space: nowrap; flex: 0 0 auto;
+  border: var(--rule-w) solid transparent; cursor: help;
+}
+.tier-chip.tier-total    { background: var(--control-active-bg); color: var(--control-active-fg); border-color: var(--control-active-bg); }
+.tier-chip.tier-high     { background: transparent; color: var(--accent); border-color: var(--accent); }
+.tier-chip.tier-advisory { background: transparent; color: var(--text-muted); border-color: var(--border-strong); text-transform: none; }
+/* cute-dbt#269 — the honest-UNKNOWN tier (the project panel's dispatch
+   banner). Same muted-outline token recipe as tier-advisory (the pair
+   the #206/#227 AA sweeps gate on every theme — including the latte
+   .tier-chip --text-muted override above), dashed to read as
+   "unattributable", uppercase from the base rule. */
+.tier-chip.tier-unknown  { background: transparent; color: var(--text-muted); border-color: var(--border-strong); border-style: dashed; }
+/* cute-dbt#268 — the vars-attribution evidence tiers (the project
+   panel's per-var lines). Three confidence levels mapped onto the SAME
+   AA-gated token recipes as the finding tiers (the #206 sweep measures
+   every chip class on every theme): DIRECT = the solid tier-total
+   recipe (strongest evidence: the model's own SQL), CONFIG = the
+   outlined-accent tier-high recipe (unrendered_config carries the
+   authored Jinja verbatim), MACRO = the muted-outline tier-advisory
+   recipe (medium: recovered through the depends_on.macros closure),
+   uppercase from the base rule. */
+.tier-chip.tier-direct   { background: var(--control-active-bg); color: var(--control-active-fg); border-color: var(--control-active-bg); }
+.tier-chip.tier-config   { background: transparent; color: var(--accent); border-color: var(--accent); }
+.tier-chip.tier-macro    { background: transparent; color: var(--text-muted); border-color: var(--border-strong); }
+.tier-chip:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+/* cute-dbt#198 discovery — Latte chip AA: the verbatim latte accent
+   (#1e66f5, 4.34:1) and text-muted (#6c6f85, 4.37:1) sit a hair under AA
+   on the latte surface at the chips' 0.68em size. Deepened stand-ins are
+   scoped to the chips per theme (accent -> one-step-darker latte blue,
+   5.13:1; muted -> Catppuccin subtext1, 5.53:1) — a token override only;
+   the chip rules/markup and the verbatim latte theme block stay pinned. */
+:root[data-theme="latte"] .tier-chip { --accent: #1a5cdd; --text-muted: #5c5f77; }
+/* cute-dbt#206 — Solarized chip AA: the verbatim solarized accent
+   (#268bd2) sits at 3.41:1 on the chip surface (#fdf6e3) — under AA at
+   the chips' 0.68em size. The deepened stand-in is the theme's OWN
+   accent-hover (#1d6fa8, one-step-darker solarized blue, 5.00:1) —
+   a token override only, scoped to the chips; tier-total (4.99:1) and
+   tier-advisory (4.92:1) already pass, so accent is the only token
+   overridden and the verbatim solarized theme block stays pinned. */
+:root[data-theme="solarized"] .tier-chip { --accent: #1d6fa8; }
+/* cute-dbt#227 — suppressed-surface AA: the latte override above scopes
+   to .tier-chip only, but the suppress-chip and the suppressed row's
+   informational text ride the SAME verbatim latte muted on the same
+   surface (#6c6f85 on #eff1f5 = 4.37:1, sub-AA) — the same deepened
+   stand-in (Catppuccin subtext1 #5c5f77; 5.53:1 on surface, 5.14:1 on
+   the construct chip's --bg-alt) extends to every suppressed surface.
+   Two narrower spot fixes ride along: the rosepine collapsed count sits
+   on the page --bg (#726d8d on #e6d9c9 = 3.53:1) and its suppressed
+   construct chip on --bg-alt (4.37:1) — deepened to #5d5780 (4.82:1 /
+   5.96:1); the solarized suppressed construct chip misses the floor by
+   a hair (#5e6e73 on #f3ecd6 = 4.497:1) — deepened to the theme's own
+   base01 #586e75 (4.56:1). Token overrides only; the verbatim theme
+   blocks stay pinned. */
+:root[data-theme="latte"] .suppress-chip,
+:root[data-theme="latte"] .finding-row.is-suppressed,
+:root[data-theme="latte"] .findings-suppressed > summary { --text-muted: #5c5f77; }
+:root[data-theme="rosepine"] .findings-suppressed > summary,
+:root[data-theme="rosepine"] .finding-row.is-suppressed .f-construct { --text-muted: #5d5780; }
+:root[data-theme="solarized"] .finding-row.is-suppressed .f-construct { --text-muted: #586e75; }
+/* cute-dbt#231 — normal-row construct chip AA: the #227 overrides above
+   scope to SUPPRESSED surfaces only, but the NORMAL finding row's
+   construct chip rides the same verbatim muted on its own --bg-alt fill
+   and sits sub-AA on three light themes (measured empirically on the
+   resolved chip fill): latte #6c6f85 on #e6e9ef = 4.06:1, rosepine
+   #726d8d on #f7f1ea = 4.37:1, and solarized #5e6e73 on #f3ecd6 =
+   4.497:1 — a hair under, the SAME pairing #227 deepened on suppressed
+   rows (the issue named only latte+rosepine; hex resolves to exact
+   integer RGB, so the painted ratio equals the token math and solarized
+   fails the floor too). The established deepened stand-ins extend to
+   the chip page-wide (latte Catppuccin subtext1 #5c5f77 = 5.14:1;
+   rosepine #5d5780 = 5.96:1; solarized base01 #586e75 = 4.56:1) —
+   suppressed chips already paint these exact values, so the #227 rules
+   above keep their meaning unchanged. Token overrides only; the
+   verbatim theme blocks stay pinned. */
+:root[data-theme="latte"] .f-construct { --text-muted: #5c5f77; }
+:root[data-theme="rosepine"] .f-construct { --text-muted: #5d5780; }
+:root[data-theme="solarized"] .f-construct { --text-muted: #586e75; }
+html[data-style="terminal"] .tier-chip,
+html[data-style="terminal"] .suppress-chip,
+html[data-style="swiss"] .tier-chip,
+html[data-style="swiss"] .suppress-chip { border-radius: 0; }
+
+/* finding body */
+.finding-body { padding: 0.45rem 0.7rem 0.6rem; border-top: var(--rule-w) solid var(--border); font-size: 0.88em; }
+.finding-body p { margin: 0.3rem 0; }
+.f-label { color: var(--text-muted); }
+/* cute-dbt#240 (defect C residual, PR #244 verification) — the
+   "Covered by " prefix label rides the verbatim muted on the finding
+   row's --surface fill and sits sub-AA on latte alone: #6c6f85 on
+   #eff1f5 = 4.37:1 (11.88px weight-400, so the 4.5 floor applies;
+   rosepine measures 4.56 and stays verbatim). The established #227/#231
+   deepened stand-in extends to this label (latte Catppuccin subtext1
+   #5c5f77 ≈ 5.5:1 on the row fill, re-measured empirically) — token
+   override only; the verbatim latte theme block stays pinned. */
+:root[data-theme="latte"] .finding-covered-by .f-label,
+:root[data-theme="latte"] .finding-degraded-label .f-label { --text-muted: #5c5f77; }
+.finding-covered-by code, .finding-suppress-reason code { font-size: 0.92em; }
+/* cute-dbt#259 — degraded backing: the summary chip (shown only when
+   EVERY attributing test is weakened — no full-strength backing at all)
+   and the per-test cause list beside the attribution. The chip pairs
+   the always-AA --text with the amber edge token (non-text), so no
+   per-theme stand-in is needed; the cause copy is body --text. */
+.degraded-chip {
+  font-size: 0.68em; padding: 0.12rem 0.55rem; border-radius: 999px;
+  font-family: var(--font-ui); white-space: nowrap; flex: 0 0 auto;
+  color: var(--text); border: var(--rule-w) solid var(--badge-amber-edge);
+  cursor: help;
+}
+.degraded-chip:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+html[data-style="terminal"] .degraded-chip,
+html[data-style="swiss"] .degraded-chip { border-radius: 0; }
+.finding-degraded-label { margin-bottom: 0.1rem; }
+.finding-degraded { list-style: none; margin: 0.2rem 0 0.3rem; padding: 0; display: flex; flex-direction: column; gap: 0.3rem; }
+.finding-degraded-test { line-height: 1.45; }
+.finding-degraded-test code { font-size: 0.92em; }
+.f-degraded-cause { color: var(--text); font-size: 0.92em; margin-top: 0.1rem; }
+.finding-evidence { list-style: none; margin: 0.3rem 0; padding: 0; display: flex; flex-direction: column; gap: 0.15rem; }
+.finding-evidence li { line-height: 1.45; }
+.f-ev-label { color: var(--text-muted); font-size: 0.92em; }
+.f-ev-value { font-family: var(--font-mono); font-size: 0.92em; overflow-wrap: anywhere; }
+.finding-pin {
+  font: inherit; font-size: 0.92em; padding: 0.15rem 0.6rem; margin: 0.25rem 0; cursor: pointer;
+  border: var(--rule-w) solid var(--control-border); border-radius: var(--radius-sm);
+  background: var(--control-bg); color: var(--control-fg);
+}
+.finding-pin:hover { border-color: var(--accent); color: var(--accent); }
+.finding-pin:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.finding-recommendation { color: var(--text); }
+.finding-sketch { margin-top: 0.45rem; }
+.finding-sketch .sql-block { padding: 0.5rem 0.7rem; }
+
+/* the inline rationale drawer */
+.finding-rationale { margin-top: 0.5rem; }
+.finding-rationale > summary { cursor: pointer; color: var(--accent); font-size: 0.95em; }
+.finding-rationale > summary:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.finding-rationale-body { padding: 0.35rem 0 0.1rem 0.9rem; border-left: 2px solid var(--border); margin-top: 0.3rem; }
+.f-rationale { color: var(--text); }
+.f-spec-label {
+  margin: 0.45rem 0 0.1rem; font-size: var(--fs-label); text-transform: uppercase;
+  letter-spacing: var(--label-tracking); color: var(--text-muted); font-family: var(--label-font);
+}
+.f-spec-list { margin: 0.1rem 0 0.3rem; padding-left: 1.1rem; color: var(--text-muted); }
+.f-spec-list li { margin: 0.12rem 0; line-height: 1.45; }
+.f-book-link { margin-top: 0.45rem; }
+
+/* suppressed findings — visible-but-quiet, never vanished */
+.findings-suppressed { margin-top: 0.5rem; }
+.findings-suppressed > summary { cursor: pointer; color: var(--text-muted); font-size: 0.85em; font-style: italic; }
+.findings-suppressed > summary:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.findings-checklist-suppressed { margin-top: 0.35rem; }
+/* cute-dbt#227 — "visible-but-quiet" rides TOKEN dimming, never opacity.
+   The retired `opacity: 0.72` composited every descendant toward the
+   page bg, dropping EFFECTIVE text contrast sub-AA in all eight themes
+   (2.7–3.9:1) and making it uncomputable from resolved tokens — the
+   exact failure mode the #206 contrast guard exists to catch. Quietness
+   now comes from the muted-but-AA text family at normal weight plus the
+   subtle default border (chrome may dim freely — non-text); the
+   decorative aria-hidden mark is the only --text-faint survivor
+   (WCAG-exempt). The headless suppressed-text sweep pins both the
+   ratios and the no-ancestor-opacity mechanism. */
+.finding-row.is-suppressed { border-color: var(--border); }
+.finding-row.is-suppressed .f-mark { color: var(--text-faint); }
+.finding-row.is-suppressed .f-verdict { color: var(--text-muted); font-weight: 400; }
+.finding-row.is-suppressed .f-name { color: var(--text-muted); }
+.suppress-chip {
+  font-size: 0.68em; padding: 0.12rem 0.55rem; border-radius: 999px;
+  font-family: var(--font-ui); white-space: nowrap; flex: 0 0 auto;
+  color: var(--text-muted); border: var(--rule-w) dashed var(--border-strong);
+}
+.finding-suppress-reason { color: var(--text-muted); font-style: italic; }
+
+/* evidence-pin flash on the DAG section (cute-dbt#170) */
+@keyframes cute-pin-flash {
+  0%   { box-shadow: 0 0 0 3px var(--accent); }
+  100% { box-shadow: 0 0 0 0 rgba(0,0,0,0); }
+}
+.cte-dag.pin-flash { animation: cute-pin-flash 1.4s ease-out; border-radius: var(--radius-sm); }
+
+/* source-map pane: bidirectional compiled-CTE↔code sync (cute-dbt#446, S3) */
+.code-map { margin-top: var(--space-3); }
+.code-map > summary { cursor: pointer; display: flex; align-items: baseline; gap: var(--space-2); }
+.code-map-hint { color: var(--text-muted); font-size: 0.85em; font-style: italic; }
+.code-map-pre .code-line { cursor: pointer; border-radius: var(--radius-sm); }
+/* the line currently owned by the selected node (static highlight) */
+.code-map-pre .code-line.code-line-owned { background: color-mix(in srgb, var(--accent) 12%, transparent); }
+/* the flash run on (re)selection — fades the accent wash out */
+@keyframes cute-code-flash {
+  0%   { background: color-mix(in srgb, var(--accent) 38%, transparent); }
+  100% { background: color-mix(in srgb, var(--accent) 12%, transparent); }
+}
+.code-map-pre .code-line.code-flash { animation: cute-code-flash 1.4s ease-out; }
+
+/* ============================================================================
+   §9 PROJECT-DEFINITION PANEL (cute-dbt#266)
+   The server-rendered "Project definition changed" callout: categorized
+   rows for an in-diff dbt_project.yml (vars / config tree / dispatch /
+   hooks / paths / identity / other), the formatting-only empty note, and
+   the Shape-A raw-diff fallback row. Theme-token-only (AA pairs come from
+   tokens.css); the raw lines reuse the diff row/fg token family.
+   ========================================================================= */
+.project-def-panel {
+  border: var(--rule-w) solid var(--border); border-left: 3px solid var(--accent);
+  border-radius: var(--radius-pan); background: var(--surface);
+  padding: 0.75rem 1rem; margin: 0 0 var(--gap-section);
+}
+.project-def-panel h2 { margin: 0 0 0.15rem; font-size: 1.05em; }
+.project-def-subtitle { margin: 0 0 0.5rem; color: var(--text-muted); font-size: 0.9em; }
+.project-def-subtitle code, .project-def-label {
+  font-family: var(--font-mono); background: var(--bg-alt); color: var(--text);
+  padding: 0.05rem 0.35rem; border-radius: var(--radius-sm);
+}
+.project-def-rows { list-style: none; margin: 0; padding: 0; display: flex; flex-direction: column; gap: 0.3rem; }
+.project-def-row { display: flex; align-items: baseline; gap: 0.5rem; flex-wrap: wrap; font-size: 0.9em; }
+.project-def-chip {
+  font-family: var(--font-ui); font-size: 0.78em; text-transform: uppercase;
+  letter-spacing: 0.04em; background: var(--bg-alt); color: var(--text-muted);
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  padding: 0.05rem 0.4rem; white-space: nowrap;
+}
+.project-def-detail { font-family: var(--font-mono); color: var(--text); overflow-wrap: anywhere; }
+.project-def-note { color: var(--text-muted); font-style: italic; font-size: 0.9em; }
+.project-def-empty, .project-def-fallback-copy {
+  margin: 0; color: var(--text-muted); font-style: italic; font-size: 0.9em;
+}
+.project-def-fallback-copy { margin-bottom: 0.4rem; }
+.project-def-raw-diff {
+  margin: 0; padding: 0.5rem 0.75rem; background: var(--bg-alt);
+  border: var(--rule-w) solid var(--border); border-radius: var(--radius-sm);
+  font-family: var(--font-mono); font-size: 0.82em; overflow-x: auto;
+}
+.project-def-raw-line { display: block; }
+.project-def-raw-line.is-removed { color: var(--diff-remove-fg); background: var(--diff-remove-row); }
+.project-def-raw-line.is-added { color: var(--diff-add-fg); background: var(--diff-add-row); }
+.project-def-raw-line.is-removed::before { content: "- "; }
+.project-def-raw-line.is-added::before { content: "+ "; }
+/* cute-dbt#269 — purpose-built hooks + dispatch rows. The dispatch
+   banner is a full-width bordered row on the panel's own --surface
+   (no fill: the note's --text-muted/--surface pairing is the
+   #227-gated AA pair; a --bg-alt fill would re-open the #231 gap).
+   The hook slot hosts the JS-rendered #111 SQL diff (.sql-block +
+   .diff-line family, already themed). */
+.project-def-row.is-banner {
+  flex-basis: 100%;
+  border: var(--rule-w) solid var(--border-strong);
+  border-radius: var(--radius-sm);
+  padding: 0.4rem 0.6rem;
+}
+.project-def-hook-slot { flex-basis: 100%; min-width: 0; }
+.project-def-hook-slot .project-hook-sql { margin: 0.25rem 0 0; }
+
+/* cute-dbt#267 — config-tree attribution: the panel rows' affected-models
+   listing (R1b: names inline up to the cap, past it the collapsed
+   <details> below) + the per-model provenance chip row beside the model
+   selector. */
+.project-def-affected { color: var(--text-muted); font-size: 0.9em; overflow-wrap: anywhere; }
+.project-def-affected-list { flex-basis: 100%; margin: 0; font-size: 0.9em; }
+.project-def-affected-list summary { cursor: pointer; color: var(--text-muted); }
+.project-def-affected-names code {
+  font-family: var(--font-mono); background: var(--bg-alt); color: var(--text);
+  padding: 0.05rem 0.35rem; border-radius: var(--radius-sm);
+}
+.model-attribution-row {
+  margin: 0.4rem 0 0; display: flex; flex-wrap: wrap; gap: 0.35rem;
+}
+.config-attribution-chip {
+  font-family: var(--font-mono); font-size: 0.8em;
+  background: var(--bg-alt); color: var(--text);
+  border: var(--rule-w) solid var(--border); border-left: 3px solid var(--accent);
+  border-radius: var(--radius-sm); padding: 0.1rem 0.45rem;
+  overflow-wrap: anywhere;
+}
+
+/* cute-dbt#268 — vars attribution: the per-var entry blocks inside a
+   vars panel row (name + scope + resolved old→new + tiered "at least N"
+   lines + the masked/insulated/dynamic statements) and the model-row
+   var-reference chip (same recipe family as the config-attribution
+   chip; the dashed left edge reads "contextual, not scope"). All pairs
+   are existing AA-gated tokens on the panel's --surface. */
+.project-def-var-entry {
+  flex-basis: 100%; min-width: 0;
+  border-top: var(--rule-w) solid var(--border);
+  padding: 0.35rem 0 0.1rem; margin: 0.15rem 0 0;
+}
+.project-def-var-name {
+  font-family: var(--font-mono); background: var(--bg-alt); color: var(--text);
+  padding: 0.05rem 0.35rem; border-radius: var(--radius-sm); font-weight: 600;
+}
+.project-def-var-scope { color: var(--text-muted); font-size: 0.9em; margin-left: 0.4rem; }
+.project-def-var-entry > .project-def-detail { margin-left: 0.5rem; }
+.project-def-var-tier {
+  margin: 0.3rem 0 0; display: flex; align-items: baseline; gap: 0.5rem;
+  flex-wrap: wrap;
+}
+.project-def-var-tier-text { overflow-wrap: anywhere; }
+.project-def-var-note {
+  margin: 0.25rem 0 0; color: var(--text-muted); font-size: 0.9em;
+  overflow-wrap: anywhere;
+}
+.var-reference-chip {
+  font-family: var(--font-mono); font-size: 0.8em;
+  background: var(--bg-alt); color: var(--text);
+  border: var(--rule-w) solid var(--border); border-left: 3px dashed var(--accent);
+  border-radius: var(--radius-sm); padding: 0.1rem 0.45rem;
+  overflow-wrap: anywhere;
+}
+
+/* cute-dbt#413 — the per-model change-axis chips (Body / Config / Unit
+   test) + the non-interactive config-file chip. A SEPARATE row from the
+   project-state attribution row above, so the axis chips (a core PR-review
+   affordance) are NEVER hidden by the html[data-project=off] display
+   toggle. The axis chips are LABELED, pill-shaped solid chips (the
+   coverage/findings tier-chip family) — one per fired axis; the
+   config-file chip reuses the config-attribution mono recipe with a solid
+   left edge marking it as the grouping key (contextual, never a button —
+   the filter toggle is the separate #414). All tokens are existing
+   AA-gated semantic tokens, so the chips theme with the chassis. */
+.model-axes-row {
+  margin: 0.4rem 0 0; display: flex; flex-wrap: wrap; gap: 0.35rem;
+  align-items: center;
+}
+.axis-chip {
+  font: inherit; font-size: 0.78em; font-weight: 600; line-height: 1.4;
+  background: var(--bg-alt); color: var(--text);
+  border: var(--rule-w) solid var(--border);
+  border-radius: 999px; padding: 0.05rem 0.55rem;
+  white-space: nowrap;
+}
+.axis-chip-body { border-left: 3px solid var(--accent); }
+.axis-chip-config { border-left: 3px solid var(--text-muted); }
+.axis-chip-unit_test { border-left: 3px dashed var(--accent); }
+.config-file-chip {
+  font-family: var(--font-mono); font-size: 0.78em;
+  background: var(--bg-alt); color: var(--text-muted);
+  border: var(--rule-w) solid var(--border); border-left: 3px solid var(--border);
+  border-radius: var(--radius-sm); padding: 0.1rem 0.45rem;
+  overflow-wrap: anywhere;
+}
+
+/* cute-dbt#416 — the top-level NEW/REMOVED state chips, reusing the
+   .axis-chip pill base so they compose coherently with the 3-axis MODIFIED
+   chips. NEW leads the model-axes row (accent left-border, the
+   "added" affordance); REMOVED leads the report-level removed-models
+   summary (muted left-border + line-through path list, the "gone"
+   affordance). Existing AA-gated semantic tokens only, so the chips theme
+   with the chassis. The visual polish is a #360 design pass. */
+.state-chip { font-weight: 700; }
+.state-chip-new { border-left: 3px solid var(--accent); }
+.state-chip-removed { border-left: 3px solid var(--text-muted); }
+.removed-models-row {
+  margin: 0.4rem 0 0; display: flex; flex-wrap: wrap; gap: 0.35rem;
+  align-items: center;
+}
+.removed-path-chip {
+  font-family: var(--font-mono); font-size: 0.78em;
+  background: var(--bg-alt); color: var(--text-muted);
+  border: var(--rule-w) solid var(--border);
+  border-radius: var(--radius-sm); padding: 0.1rem 0.45rem;
+  text-decoration: line-through; overflow-wrap: anywhere;
+}
+
+/* ============================================================================
+   cute-dbt#402 (epic #360) — the subject-lens tab shell. Tabs are subject
+   JOURNEYS (Models / Macros / Project), not change-state sections. First
+   pass: minimal chassis only — the tab strip, the active-tab affordance, and
+   panel show/hide. Uses the design-system semantic tokens so it themes with
+   the rest of the chassis. Claude Design owns the visual polish.
+   ============================================================================ */
+.lens-tabs {
+  display: flex; gap: 0.25rem; flex-wrap: wrap;
+  margin: 1rem 0 0.75rem; padding: 0;
+  border-bottom: var(--rule-w) solid var(--border);
+}
+.lens-tab {
+  appearance: none; cursor: pointer;
+  background: transparent; color: var(--text-muted);
+  border: var(--rule-w) solid transparent; border-bottom: none;
+  border-top-left-radius: var(--radius-sm); border-top-right-radius: var(--radius-sm);
+  padding: 0.4rem 0.85rem; margin-bottom: calc(-1 * var(--rule-w));
+  font: inherit; font-weight: 600; line-height: 1.2;
+}
+.lens-tab:hover { color: var(--text); background: var(--bg-alt); }
+.lens-tab:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.lens-tab.is-active {
+  color: var(--text); background: var(--bg);
+  border-color: var(--border);
+  border-bottom: var(--rule-w) solid var(--bg);
+}
+.lens-panel[hidden] { display: none; }
+.lens-scaffold { padding: 0.5rem 0; }
+.lens-scaffold-empty {
+  color: var(--text-muted); overflow-wrap: anywhere; margin: 0.5rem 0;
+}
+
+/* PR review comments (cute-dbt#419 / #420 / #421 / #422, epic #353).
+   The whole surface is JS-rendered from DATA.pr_comments; these rules
+   dress the inline threads, the top count button, and the per-model count
+   chip. No `overflow:hidden` ancestor clips the focusable-bubble tooltip
+   (the .model-comment-count-chip reuses the shared .expect-tooltip family,
+   whose bubble is position:absolute over a position:relative trigger). */
+.pr-comments-count {
+  display: inline-flex; align-items: center; gap: 0.4rem;
+  margin: 0.6rem 0 0; padding: 0.35rem 0.7rem;
+  border: 1px solid var(--border, #ccc); border-radius: var(--radius-sm, 6px);
+  background: var(--surface-2, rgba(0,0,0,0.03)); color: var(--text, inherit);
+  font: inherit; font-size: 0.92em; cursor: pointer;
+}
+.pr-comments-count:hover { background: var(--surface-3, rgba(0,0,0,0.06)); }
+.pr-comments-count:focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
+.pr-comments-count-label { font-weight: 600; }
+.pr-comments-count[hidden] { display: none; }
+
+.model-comments-row { margin: 0.3rem 0; }
+.model-comments-row[hidden] { display: none; }
+.model-comment-count-chip {
+  border-bottom: none; padding: 0.15rem 0.5rem;
+  border: 1px solid var(--border, #ccc); border-radius: 999px;
+  background: var(--surface-2, rgba(0,0,0,0.03)); cursor: help;
+}
+.model-comment-bubble { text-align: left; }
+.model-comment-bubble-row { display: block; white-space: nowrap; padding: 0.05rem 0; }
+
+/* Inline thread cards, injected after the anchored Model-SQL diff line. */
+.pr-comment-thread {
+  display: block; margin: 0.3rem 0 0.5rem; padding: 0.5rem 0.7rem;
+  border: 1px solid var(--border, #ccc);
+  border-left: 3px solid var(--accent);
+  border-radius: var(--radius-sm, 6px);
+  background: var(--surface-1, rgba(0,0,0,0.02));
+  font-size: 0.9em; white-space: normal;
+}
+.pr-comment-resolved { border-left-color: var(--ok, #2e7d32); opacity: 0.85; }
+.pr-comment-outdated { border-left-color: var(--warn, #b45c0c); }
+.pr-comment-thread-head {
+  display: flex; align-items: center; gap: 0.4rem;
+  margin-bottom: 0.35rem; color: var(--text-muted);
+}
+.pr-comment-anchor { font-weight: 600; }
+.pr-comment-badge {
+  font-size: 0.78em; padding: 0.05rem 0.4rem; border-radius: 999px;
+  border: 1px solid currentColor;
+}
+.pr-comment-badge-resolved { color: var(--ok, #2e7d32); }
+.pr-comment-badge-outdated { color: var(--warn, #b45c0c); }
+.pr-comment-collapse > summary { cursor: pointer; color: var(--text-muted); }
+.pr-comment { margin: 0.25rem 0; }
+.pr-comment-author { font-weight: 600; color: var(--accent); }
+.pr-comment-text { white-space: pre-wrap; overflow-wrap: anywhere; margin-top: 0.1rem; }
+.pr-comment-tail {
+  margin: 0.6rem 0; padding: 0.5rem 0.7rem;
+  border: 1px dashed var(--border, #ccc); border-radius: var(--radius-sm, 6px);
+}
+.pr-comment-tail-label { margin: 0 0 0.4rem; color: var(--text-muted); font-style: italic; }
+
+/* end of cute-dbt report chassis v1 (cute-dbt#177) */
+
+</style>
+</head>
+<body>
+
+<header class="report-header">
+  <h1>cute-dbt report</h1>
+</header>
+
+<section class="diff-scope-banner" aria-label="Diff scope">
+  <p class="diff-scope-line">
+    <span class="diff-scope-text">Showing 1 unit test in scope</span> — from PR file diff.
+  </p>
+  
+  <p class="zero-updated-affirm" data-testid="zero-updated-affirm" hidden>no unit-test definitions changed in this diff</p>
+  
+
+  
+  <button type="button" class="pr-comments-count" data-testid="pr-comments-count" hidden></button>
+</section>
+
+
+<div class="settings-cog-wrap">
+  <button type="button" class="settings-cog" aria-label="Report settings"
+          aria-haspopup="true" aria-expanded="false" aria-controls="settings-panel">
+    <svg class="icon" viewBox="0 0 24 24" aria-hidden="true"><circle cx="12" cy="12" r="3.2"/><path d="M19.4 15a1.65 1.65 0 0 0 .33 1.82l.06.06a2 2 0 1 1-2.83 2.83l-.06-.06a1.65 1.65 0 0 0-1.82-.33 1.65 1.65 0 0 0-1 1.51V21a2 2 0 0 1-4 0v-.09A1.65 1.65 0 0 0 9 19.4a1.65 1.65 0 0 0-1.82.33l-.06.06a2 2 0 1 1-2.83-2.83l.06-.06a1.65 1.65 0 0 0 .33-1.82 1.65 1.65 0 0 0-1.51-1H3a2 2 0 0 1 0-4h.09A1.65 1.65 0 0 0 4.6 9a1.65 1.65 0 0 0-.33-1.82l-.06-.06a2 2 0 1 1 2.83-2.83l.06.06a1.65 1.65 0 0 0 1.82.33h.09A1.65 1.65 0 0 0 9 4.6V3a2 2 0 0 1 4 0v.09a1.65 1.65 0 0 0 1 1.51 1.65 1.65 0 0 0 1.82-.33l.06-.06a2 2 0 1 1 2.83 2.83l-.06.06a1.65 1.65 0 0 0-.33 1.82v.09a1.65 1.65 0 0 0 1.51 1H21a2 2 0 0 1 0 4h-.09a1.65 1.65 0 0 0-1.51 1z"/></svg>
+  </button>
+  <div class="settings-panel" id="settings-panel" role="dialog"
+       aria-label="Report settings" hidden>
+    <h2>Settings</h2>
+    <div class="settings-section appearance-section">
+      <h2>Appearance</h2>
+      <div class="settings-row settings-row-stack">
+        <label>Style</label>
+        <div class="style-grid">
+          <button type="button" class="style-opt" data-style-id="soft">Soft</button>
+          <button type="button" class="style-opt" data-style-id="paper">Paper</button>
+          <button type="button" class="style-opt" data-style-id="swiss">Swiss</button>
+          <button type="button" class="style-opt" data-style-id="terminal">Terminal</button>
+        </div>
+      </div>
+      <div class="settings-row settings-row-stack">
+        <label>Theme</label>
+        <div class="theme-grid">
+          <button type="button" class="theme-chip" data-theme-id="light"><span class="swatch-dots"><i style="background:#ffffff"></i><i style="background:#1d7484"></i><i style="background:#33363d"></i></span><span class="theme-name">Light</span></button>
+          <button type="button" class="theme-chip" data-theme-id="solarized"><span class="swatch-dots"><i style="background:#fdf6e3"></i><i style="background:#268bd2"></i><i style="background:#b58900"></i></span><span class="theme-name">Solarized</span></button>
+          <button type="button" class="theme-chip" data-theme-id="latte"><span class="swatch-dots"><i style="background:#eff1f5"></i><i style="background:#1e66f5"></i><i style="background:#8839ef"></i></span><span class="theme-name">Catppuccin Latte</span></button>
+          <button type="button" class="theme-chip" data-theme-id="rosepine"><span class="swatch-dots"><i style="background:#faf4ed"></i><i style="background:#286983"></i><i style="background:#907aa9"></i></span><span class="theme-name">Ros&eacute; Pine Dawn</span></button>
+          <button type="button" class="theme-chip" data-theme-id="dark"><span class="swatch-dots"><i style="background:#15171c"></i><i style="background:#5fb3c4"></i><i style="background:#d7dae1"></i></span><span class="theme-name">Dark</span></button>
+          <button type="button" class="theme-chip" data-theme-id="tokyo"><span class="swatch-dots"><i style="background:#1a1b26"></i><i style="background:#7aa2f7"></i><i style="background:#bb9af7"></i></span><span class="theme-name">Tokyo Night</span></button>
+          <button type="button" class="theme-chip" data-theme-id="gruvbox"><span class="swatch-dots"><i style="background:#282828"></i><i style="background:#83a598"></i><i style="background:#fabd2f"></i></span><span class="theme-name">Gruvbox</span></button>
+          <button type="button" class="theme-chip" data-theme-id="dracula"><span class="swatch-dots"><i style="background:#282a36"></i><i style="background:#bd93f9"></i><i style="background:#ff79c6"></i></span><span class="theme-name">Dracula</span></button>
+        </div>
+      </div>
+      <div class="settings-row settings-row-stack">
+        <label>Accent</label>
+        <div class="accent-row">
+          <button type="button" class="accent-swatch" data-accent="theme" aria-label="Theme default accent" title="Theme default accent"></button>
+          <button type="button" class="accent-swatch" data-accent="teal" style="background:#1d7484" aria-label="teal accent" title="teal accent"></button>
+          <button type="button" class="accent-swatch" data-accent="blue" style="background:#2a6fdb" aria-label="blue accent" title="blue accent"></button>
+          <button type="button" class="accent-swatch" data-accent="violet" style="background:#7c3aed" aria-label="violet accent" title="violet accent"></button>
+          <button type="button" class="accent-swatch" data-accent="green" style="background:#1f8a5b" aria-label="green accent" title="green accent"></button>
+          <button type="button" class="accent-swatch" data-accent="amber" style="background:#b45c0c" aria-label="amber accent" title="amber accent"></button>
+        </div>
+      </div>
+      <div class="settings-row settings-row-stack">
+        <label>Density</label>
+        <div class="seg-radio density-seg">
+          <button type="button" data-density="compact">Compact</button>
+          <button type="button" data-density="cozy">Cozy</button>
+          <button type="button" data-density="comfortable">Roomy</button>
+        </div>
+      </div>
+      <div class="settings-row settings-row-stack">
+        <label>Diff layout<span class="settings-hint">side-by-side when there&rsquo;s room</span></label>
+        <div class="seg-radio difflayout-seg">
+          <button type="button" data-difflayout="auto">Auto</button>
+          <button type="button" data-difflayout="unified">Unified</button>
+          <button type="button" data-difflayout="split">Split</button>
+        </div>
+      </div>
+      
+      <div class="settings-row settings-row-stack">
+        <label>DAG engine<span class="settings-hint">Mermaid (static) or Cytoscape (interactive)</span></label>
+        <div class="seg-radio engine-seg">
+          <button type="button" data-engine="mermaid">Mermaid</button>
+          <button type="button" data-engine="cytoscape">Cytoscape</button>
+        </div>
+      </div>
+    </div>
+    
+    <div class="settings-section experimental-section">
+      <div class="settings-row settings-row-stack">
+        <label>Experimental
+          <span class="settings-hint">unstable surfaces &mdash; defaults may change between versions</span>
+        </label>
+      </div>
+      <div class="settings-row">
+        <label for="settings-coverage-input">Coverage intelligence
+          <span class="settings-hint">show or hide the coverage-checks surface</span>
+        </label>
+        <span class="settings-switch">
+          <input type="checkbox" id="settings-coverage-input"
+                 aria-label="Show coverage-intelligence checks">
+          <span class="switch-track" aria-hidden="true"></span>
+        </span>
+      </div>
+    </div>
+    <div class="settings-section">
+      <div class="settings-row">
+        <label for="settings-context-input">Diff context lines
+          <span class="settings-hint">unchanged lines kept around each change</span>
+        </label>
+        <input type="number" class="settings-context-input" id="settings-context-input"
+               min="0" max="20" step="1" value="3"
+               aria-label="Unchanged context lines shown around each change">
+      </div>
+      
+      <div class="settings-row">
+        <label for="settings-expand-step">Expand step
+          <span class="settings-hint">lines revealed per gutter click &mdash; 0 = all</span>
+        </label>
+        <input type="number" class="settings-context-input" id="settings-expand-step"
+               min="0" max="500" step="1" value="20"
+               aria-label="Lines revealed each time you expand a fold; 0 reveals all">
+      </div>
+      <div class="settings-row">
+        <label for="settings-normalize-input">Hide format-only changes
+          <span class="settings-hint">treat reformatted-but-equal cells as unchanged</span>
+        </label>
+        <span class="settings-switch">
+          <input type="checkbox" id="settings-normalize-input"
+                 aria-label="Hide format-only (normalized-equal) cell changes" checked>
+          <span class="switch-track" aria-hidden="true"></span>
+        </span>
+      </div>
+    </div>
+  </div>
+</div><nav class="lens-tabs" role="tablist" aria-label="Report lenses" data-testid="lens-tabs">
+  <button type="button" class="lens-tab is-active" role="tab" id="lens-tab-models" aria-selected="true" aria-controls="lens-panel-models" tabindex="0" data-lens="models" data-testid="lens-tab-models">Models</button>
+  <button type="button" class="lens-tab" role="tab" id="lens-tab-macros" aria-selected="false" aria-controls="lens-panel-macros" tabindex="-1" data-lens="macros" data-testid="lens-tab-macros">Macros</button>
+  <button type="button" class="lens-tab" role="tab" id="lens-tab-project" aria-selected="false" aria-controls="lens-panel-project" tabindex="-1" data-lens="project" data-testid="lens-tab-project">Project</button>
+</nav>
+<div class="lens-panel is-active" role="tabpanel" id="lens-panel-models" aria-labelledby="lens-tab-models" data-lens="models" data-testid="lens-panel-models"><section class="error-card" role="alert" aria-label="Manifest error">
+  <h2>This manifest can't be visualized</h2>
+  <div class="error-node-id">node: <code>—</code></div>
+  <p class="error-remediation">
+    Re-run with <code>dbt compile</code> or <code>dbt run</code> —
+    <code>dbt parse</code> does not produce compiled SQL.
+  </p>
+</section>
+
+<section class="test-selection" aria-label="Test selection">
+  
+  <div class="axis-filter" role="radiogroup" aria-label="Filter models by change axis" data-testid="axis-filter" hidden></div>
+  <p class="removed-models-row" aria-label="Models removed in this PR" data-testid="removed-models" hidden></p>
+  <div class="test-selector-row">
+    <div class="selector-field">
+      <label for="model-select">Model</label>
+      <select id="model-select"></select>
+      
+    </div>
+  </div>
+  <p class="model-axes-row" data-testid="model-axes" hidden></p>
+  
+  <p class="model-comments-row" data-testid="model-comments" hidden></p>
+  <p class="model-attribution-row" data-testid="model-attribution" hidden></p>
+  <p class="zero-updated-hint" data-testid="zero-updated-hint" hidden></p>
+</section>
+
+
+<section class="model-findings" aria-label="Coverage checks" data-testid="model-findings">
+  <div class="findings-header">
+    <h2>Coverage checks</h2>
+    <p class="findings-subtitle">Static checks over the manifest and compiled SQL — tiers are labeled, never averaged.</p>
+  </div>
+  <div class="findings-body"></div>
+</section>
+
+<section class="model-sql" aria-label="Model SQL">
+  <details class="model-sql-details">
+    <summary>Model SQL<span class="model-sql-summary-hint">raw, with Jinja</span></summary>
+    
+    <div class="sql-block-wrap model-sql-wrap">
+      <pre class="sql-block model-sql-block"><code class="model-sql-code"></code></pre>
+    </div>
+  </details>
+</section>
+
+
+<section class="model-yaml" aria-label="Model YAML">
+  <details class="model-yaml-details">
+    <summary>Model YAML<span class="model-yaml-summary-hint">authored schema entry</span></summary>
+    <div class="sql-block-wrap model-yaml-wrap">
+      <pre class="sql-block model-yaml-block"><code class="model-yaml-code"></code></pre>
+    </div>
+  </details>
+</section>
+
+<section class="cte-dag" aria-label="CTE dependency DAG">
+  <div class="cte-dag-header">
+    <h2>CTE dependency DAG</h2>
+    
+    <button type="button" class="expect-tooltip cte-dag-hint" aria-label="Click a node (or Tab to it and press Enter) to inspect.">ⓘ<span class="expect-tooltip-bubble" aria-hidden="true">Click a node (or Tab to it and press Enter) to inspect.</span></button>
+  </div>
+  <div class="recursive-banner" hidden>
+    Recursive CTE present; recursive arm omitted from DAG (v0.1 limit).
+  </div>
+  
+  <div class="dag-stage">
+    <div class="dag-canvas-col">
+      <div class="cte-dag-mermaid"></div>
+      
+      <div class="cte-dag-cyto is-hidden">
+        <div class="cyto-canvas" aria-label="Interactive CTE DAG (Cytoscape)"></div>
+        <div class="cyto-node-card" role="status" aria-live="polite"></div>
+      </div>
+      <div class="dag-legend">
+        <div class="dag-legend-group">
+          <span class="dag-legend-title">node role</span>
+          <span class="dag-legend-roles"></span>
+        </div>
+        <div class="dag-legend-group">
+          <span class="dag-legend-title">edge type</span>
+          <span class="dag-legend-joins"></span>
+        </div>
+      </div>
+    </div>
+    <aside class="dag-shelf" aria-label="Node detail" hidden>
+      <div class="dag-shelf-head">
+        <span class="dag-shelf-title">Node detail</span>
+        <button type="button" class="dag-shelf-close" aria-label="Close node detail">✕</button>
+      </div>
+      <div class="dag-shelf-body"></div>
+    </aside>
+  </div>
+</section>
+
+
+<section class="test-section" aria-label="Unit test">
+  <div class="test-card">
+    <div class="test-card-head">
+      <div class="selector-field test-select-field">
+        <label for="test-select">Unit test</label>
+        <select id="test-select"></select>
+      </div>
+      <div class="test-mode-toggle" role="group" aria-label="Test scope" data-testid="updated-toggle">
+        <button type="button" data-test-mode="updated" class="is-active" aria-pressed="true">Updated only</button>
+        <button type="button" data-test-mode="all" aria-pressed="false">All tests</button>
+      </div>
+      <div class="test-badges"></div>
+    </div>
+    <p class="test-description" hidden></p>
+    <div class="test-details-body"></div>
+  </div>
+</section>
+
+<div class="panel-row">
+
+  <section class="panel left-panel" aria-label="Given inputs">
+    
+    <div class="panel-header">
+      <h2>Given</h2>
+    </div>
+    <div class="left-panel-body" data-mode="inputs"></div>
+  </section>
+
+  <section class="panel expected-panel" aria-label="Expected output">
+    <div class="panel-header">
+      <h2>Expected</h2>
+      <span class="row-count-badge expected-rowcount">0 rows</span>
+    </div>
+    <div class="expected-body"></div>
+  </section>
+
+</div></div>
+<div class="lens-panel" role="tabpanel" id="lens-panel-macros" aria-labelledby="lens-tab-macros" data-lens="macros" data-testid="lens-panel-macros" hidden></div>
+<div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
+</div>
+
+<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"fct_events","path":"models/marts/fct_events.sql","dag":{"nodes":[{"id":"base","role":"import"},{"id":"(final select)","label":"fct_events.sql","role":"final"}],"edges":[{"from":"base","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select event_id, occurred_at, amount from base\n","base":"base as (\n    select event_id, occurred_at, amount from \"memory\".\"main\".\"stg_events\"\n)"},"code_map":{"compiled":"with base as (\n    select event_id, occurred_at, amount from \"memory\".\"main\".\"stg_events\"\n)\nselect event_id, occurred_at, amount from base\n","node_spans":{"(final select)":{"start":{"line":4,"col":1,"byte":92},"end":{"line":5,"col":1,"byte":139}},"base":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":91}}},"raw_zones":[{"kind":"incremental_guard","start":{"line":7,"col":1,"byte":153},"end":{"line":13,"col":12,"byte":327},"presence":"compiled_out"}],"column_spans":{"(final select)\u001famount":{"start":{"line":4,"col":31,"byte":122},"end":{"line":4,"col":37,"byte":128}},"(final select)\u001fevent_id":{"start":{"line":4,"col":8,"byte":99},"end":{"line":4,"col":16,"byte":107}},"(final select)\u001foccurred_at":{"start":{"line":4,"col":18,"byte":109},"end":{"line":4,"col":29,"byte":120}},"base\u001famount":{"start":{"line":2,"col":35,"byte":49},"end":{"line":2,"col":41,"byte":55}},"base\u001fevent_id":{"start":{"line":2,"col":12,"byte":26},"end":{"line":2,"col":20,"byte":34}},"base\u001foccurred_at":{"start":{"line":2,"col":22,"byte":36},"end":{"line":2,"col":33,"byte":47}}},"raw_node_spans":{"base":{"start":{"line":3,"col":6,"byte":70},"end":{"line":5,"col":2,"byte":151}}},"raw_dag":{"nodes":[{"id":"base","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"zone:0","role":"zone","is_zone":true,"presence":"compiled_out"}]},"node_map":{"compiled":{"base":"base"}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_events"}},"column":"event_id"},"to_col":{"scope":{"intra":{"node_id":"base"}},"column":"event_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_events"}},"column":"occurred_at"},"to_col":{"scope":{"intra":{"node_id":"base"}},"column":"occurred_at"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_events"}},"column":"amount"},"to_col":{"scope":{"intra":{"node_id":"base"}},"column":"amount"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"base"}},"column":"event_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"event_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"base"}},"column":"occurred_at"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"occurred_at"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"base"}},"column":"amount"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"amount"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{ config(materialized='incremental', unique_key='event_id') }}\n\nwith base as (\n    select event_id, occurred_at, amount from {{ ref('stg_events') }}\n)\n\n{% if is_incremental() %}\n, recent as (\n    select event_id, occurred_at, amount\n    from base\n    where occurred_at > (select max(occurred_at) from {{ this }})\n)\n{% endif %}\n\nselect event_id, occurred_at, amount from base\n","sql_diff":{"lines":[{"kind":"context","text":"{{ config(materialized='incremental', unique_key='event_id') }}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"removed","text":"with base as (   -- old","emphasis":[14,23]},{"kind":"added","text":"with base as (","emphasis":null},{"kind":"context","text":"    select event_id, occurred_at, amount from {{ ref('stg_events') }}","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"{% if is_incremental() %}","emphasis":null},{"kind":"context","text":", recent as (","emphasis":null},{"kind":"context","text":"    select event_id, occurred_at, amount","emphasis":null},{"kind":"context","text":"    from base","emphasis":null},{"kind":"context","text":"    where occurred_at > (select max(occurred_at) from {{ this }})","emphasis":null},{"kind":"context","text":")","emphasis":null},{"kind":"context","text":"{% endif %}","emphasis":null},{"kind":"context","text":"","emphasis":null},{"kind":"context","text":"select event_id, occurred_at, amount from base","emphasis":null}]},"model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[{"id":"unit_test.analytics.fct_events.test_fct_events_passthrough","name":"test_fct_events_passthrough","target_model":"fct_events","changed":false,"defined_in":"models/marts/unit_tests.yml","given":[{"input":"ref('stg_events')","bound_to_node":"base","table":{"columns":["amount","event_id","occurred_at"],"rows":[{"cells":[{"display":"10","key":{"t":"str","v":"10"}},{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"2024-01-01","key":{"t":"str","v":"2024-01-01"}}]}]},"rows":[{"amount":"10","event_id":"e1","occurred_at":"2024-01-01"}],"format":"dict"}],"expected":{"table":{"columns":["amount","event_id","occurred_at"],"rows":[{"cells":[{"display":"10","key":{"t":"str","v":"10"}},{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"2024-01-01","key":{"t":"str","v":"2024-01-01"}}]}]},"rows":[{"amount":"10","event_id":"e1","occurred_at":"2024-01-01"}],"format":"dict"}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.analytics.fct_events","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"event_id"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.analytics.fct_events","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental"},{"label":"branch coverage","value":"false-only — 1 test(s) exercise the initial full-build branch (no override compiles is_incremental() as false); none override is_incremental to true"},{"label":"missing branch","value":"the incremental branch (is_incremental() = true) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). 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rgba(185,185,185,1))"}updateColors(){if(this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#333"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#333",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor),this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||je(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.vertLineColor=this.vertLineColor||"navy",this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.noteFontWeight=this.noteFontWeight||"normal",this.fontWeight=this.fontWeight||"normal",this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.darkMode?(this.rowOdd=this.rowOdd||Xe(this.mainBkg,5)||"#ffffff",this.rowEven=this.rowEven||Xe(this.mainBkg,10)):(this.rowOdd=this.rowOdd||je(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||je(this.mainBkg,5)),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||this.tertiaryColor,this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],75);else for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],25);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleInv"+r]=this["cScaleInv"+r]||Ge(this["cScale"+r]);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this.darkMode?this["cScalePeer"+r]=this["cScalePeer"+r]||je(this["cScale"+r],10):this["cScalePeer"+r]=this["cScalePeer"+r]||Xe(this["cScale"+r],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleLabel"+r]=this["cScaleLabel"+r]||this.scaleLabelColor;let t=this.darkMode?-4:-1;for(let r=0;r<5;r++)this["surface"+r]=this["surface"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(5+r*3)}),this["surfacePeer"+r]=this["surfacePeer"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(8+r*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||ne(this.primaryColor,{h:64}),this.fillType3=this.fillType3||ne(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||ne(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||ne(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||ne(this.primaryColor,{h:128}),this.fillType7=this.fillType7||ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||ne(this.primaryColor,{l:-10}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-10}),this.pie6=this.pie6||ne(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||ne(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.venn1=this.venn1??ne(this.primaryColor,{l:-30}),this.venn2=this.venn2??ne(this.secondaryColor,{l:-30}),this.venn3=this.venn3??ne(this.tertiaryColor,{l:-30}),this.venn4=this.venn4??ne(this.primaryColor,{h:60,l:-30}),this.venn5=this.venn5??ne(this.primaryColor,{h:-60,l:-30}),this.venn6=this.venn6??ne(this.secondaryColor,{h:60,l:-30}),this.venn7=this.venn7??ne(this.primaryColor,{h:120,l:-30}),this.venn8=this.venn8??ne(this.secondaryColor,{h:120,l:-30}),this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.wardleyEvolutionColor=this.wardleyEvolutionColor||"#dc3545",this.wardley={backgroundColor:this.wardley?.backgroundColor||this.background,axisColor:this.wardley?.axisColor||this.lineColor,axisTextColor:this.wardley?.axisTextColor||this.primaryTextColor,gridColor:this.wardley?.gridColor||this.gridColor,componentFill:this.wardley?.componentFill||this.background,componentStroke:this.wardley?.componentStroke||this.lineColor,componentLabelColor:this.wardley?.componentLabelColor||this.primaryTextColor,linkStroke:this.wardley?.linkStroke||this.lineColor,evolutionStroke:this.wardley?.evolutionStroke||this.wardleyEvolutionColor,annotationStroke:this.wardley?.annotationStroke||this.lineColor,annotationTextColor:this.wardley?.annotationTextColor||this.primaryTextColor,annotationFill:this.wardley?.annotationFill||this.background},this.archEdgeColor=this.archEdgeColor||"#777",this.archEdgeArrowColor=this.archEdgeArrowColor||"#777",this.archEdgeWidth=this.archEdgeWidth||"3",this.archGroupBorderColor=this.archGroupBorderColor||"#000",this.archGroupBorderWidth=this.archGroupBorderWidth||"2px",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,dataLabelColor:this.xyChart?.dataLabelColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||ne(this.primaryColor,{h:-30}),this.git4=this.git4||ne(this.primaryColor,{h:-60}),this.git5=this.git5||ne(this.primaryColor,{h:-90}),this.git6=this.git6||ne(this.primaryColor,{h:60}),this.git7=this.git7||ne(this.primaryColor,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.emUiFill=this.emUiFill||"white",this.emUiStroke=this.emUiStroke||"#dbdada",this.emProcessorFill=this.emProcessorFill||"#edb3f6",this.emProcessorStroke=this.emProcessorStroke||"#b88cbf",this.emReadModelFill=this.emReadModelFill||"#d3f1a2",this.emReadModelStroke=this.emReadModelStroke||"#a3b732",this.emCommandFill=this.emCommandFill||"#bcd6fe",this.emCommandStroke=this.emCommandStroke||"#679ac3",this.emEventFill=this.emEventFill||"#ffb778",this.emEventStroke=this.emEventStroke||"#c19a0f",this.emSwimlaneBackgroundOdd=this.emSwimlaneBackgroundOdd||"rgb(250,250,250)",this.emSwimlaneBackgroundStroke=this.emSwimlaneBackgroundStroke||"rgb(240,240,240)",this.emArrowhead=this.emArrowhead||this.lineColor,this.emRelationStroke=this.emRelationStroke||this.lineColor,this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn,this.gradientStart=this.primaryBorderColor,this.gradientStop=this.secondaryBorderColor}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},CH=o(e=>{let t=new g7;return t.calculate(e),t},"getThemeVariables")});var y7,wH,SH=z(()=>{"use strict";Cn();Eo();y7=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=je(this.primaryColor,16),this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Ge(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.lineColor=Ge(this.background),this.textColor=Ge(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=je(Ge("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=mn(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.radius=5,this.strokeWidth=1,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.clusterBkg="#302F3D",this.sectionBkgColor=Xe("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=Xe(this.sectionBkgColor,10),this.taskBorderColor=mn(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=mn(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.vertLineColor="#00BFFF",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd=this.rowOdd||je(this.mainBkg,5)||"#ffffff",this.rowEven=this.rowEven||Xe(this.mainBkg,10),this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd",this.useGradient=!0,this.gradientStart=this.primaryBorderColor,this.gradientStop=this.secondaryBorderColor,this.dropShadow="drop-shadow( 1px 2px 2px rgba(185,185,185,1))",this.noteFontWeight=this.noteFontWeight||"normal",this.fontWeight=this.fontWeight||"normal"}updateColors(){this.secondBkg=je(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=je(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=je(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=Ge(this.doneTaskBkgColor),this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=ne(this.primaryColor,{h:64}),this.fillType3=ne(this.secondaryColor,{h:64}),this.fillType4=ne(this.primaryColor,{h:-64}),this.fillType5=ne(this.secondaryColor,{h:-64}),this.fillType6=ne(this.primaryColor,{h:128}),this.fillType7=ne(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330});for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleInv"+t]=this["cScaleInv"+t]||Ge(this["cScale"+t]);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScalePeer"+t]=this["cScalePeer"+t]||je(this["cScale"+t],10);for(let t=0;t<5;t++)this["surface"+t]=this["surface"+t]||ne(this.mainBkg,{h:30,s:-30,l:-(-10+t*4)}),this["surfacePeer"+t]=this["surfacePeer"+t]||ne(this.mainBkg,{h:30,s:-30,l:-(-7+t*4)});this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleLabel"+t]=this["cScaleLabel"+t]||this.scaleLabelColor;for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["pie"+t]=this["cScale"+t];this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.mainContrastColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.mainContrastColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7";for(let t=0;t<8;t++)this["venn"+(t+1)]=this["venn"+(t+1)]??je(this["cScale"+t],30);this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,dataLabelColor:this.xyChart?.dataLabelColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#3498db,#2ecc71,#e74c3c,#f1c40f,#bdc3c7,#ffffff,#34495e,#9b59b6,#1abc9c,#e67e22"},this.packet={startByteColor:this.primaryTextColor,endByteColor:this.primaryTextColor,labelColor:this.primaryTextColor,titleColor:this.primaryTextColor,blockStrokeColor:this.primaryTextColor,blockFillColor:this.background},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.wardleyEvolutionColor=this.wardleyEvolutionColor||"#ff6b6b",this.wardley={backgroundColor:this.wardley?.backgroundColor||this.background,axisColor:this.wardley?.axisColor||this.lineColor,axisTextColor:this.wardley?.axisTextColor||this.primaryTextColor,gridColor:this.wardley?.gridColor||this.gridColor,componentFill:this.wardley?.componentFill||this.mainBkg,componentStroke:this.wardley?.componentStroke||this.lineColor,componentLabelColor:this.wardley?.componentLabelColor||this.primaryTextColor,linkStroke:this.wardley?.linkStroke||this.lineColor,evolutionStroke:this.wardley?.evolutionStroke||this.wardleyEvolutionColor,annotationStroke:this.wardley?.annotationStroke||this.lineColor,annotationTextColor:this.wardley?.annotationTextColor||this.primaryTextColor,annotationFill:this.wardley?.annotationFill||this.mainBkg},this.classText=this.primaryTextColor,this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=je(this.secondaryColor,20),this.git1=je(this.pie2||this.secondaryColor,20),this.git2=je(this.pie3||this.tertiaryColor,20),this.git3=je(this.pie4||ne(this.primaryColor,{h:-30}),20),this.git4=je(this.pie5||ne(this.primaryColor,{h:-60}),20),this.git5=je(this.pie6||ne(this.primaryColor,{h:-90}),10),this.git6=je(this.pie7||ne(this.primaryColor,{h:60}),10),this.git7=je(this.pie8||ne(this.primaryColor,{h:120}),20),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Ge(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Ge(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.emUiFill=this.emUiFill||"#2d2d2d",this.emUiStroke=this.emUiStroke||"#555",this.emProcessorFill=this.emProcessorFill||je("#5a3d5c",10),this.emProcessorStroke=this.emProcessorStroke||"#8a6d8c",this.emReadModelFill=this.emReadModelFill||je("#3d5a2d",10),this.emReadModelStroke=this.emReadModelStroke||"#6d8c5c",this.emCommandFill=this.emCommandFill||je("#2d3d5a",10),this.emCommandStroke=this.emCommandStroke||"#5c6d8c",this.emEventFill=this.emEventFill||je("#5a452d",10),this.emEventStroke=this.emEventStroke||"#8c755c",this.emSwimlaneBackgroundOdd=this.emSwimlaneBackgroundOdd||je(this.background,5),this.emSwimlaneBackgroundStroke=this.emSwimlaneBackgroundStroke||je(this.background,12),this.emArrowhead=this.emArrowhead||this.lineColor,this.emRelationStroke=this.emRelationStroke||this.lineColor,this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||je(this.background,12),this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||je(this.background,2),this.nodeBorder=this.nodeBorder||"#999"}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},wH=o(e=>{let t=new y7;return t.calculate(e),t},"getThemeVariables")});var v7,Ao,$f=z(()=>{"use strict";Cn();Eo();dl();v7=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=ne(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.lineColor=Ge(this.background),this.textColor=Ge(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.radius=5,this.strokeWidth=1,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.clusterBkg="#FBFBFF",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.vertLineColor="calculated",this.sectionBkgColor=mn(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.vertLineColor="navy",this.noteFontWeight=this.noteFontWeight||"normal",this.fontWeight=this.fontWeight||"normal",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd="calculated",this.rowEven="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.useGradient=!1,this.gradientStart=this.primaryBorderColor,this.gradientStop=this.secondaryBorderColor,this.dropShadow="drop-shadow(1px 2px 2px rgba(185, 185, 185, 1))",this.updateColors()}updateColors(){this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Xe(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Xe(this.tertiaryColor,40);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScale"+t]=Xe(this["cScale"+t],10),this["cScalePeer"+t]=this["cScalePeer"+t]||Xe(this["cScale"+t],25);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleInv"+t]=this["cScaleInv"+t]||ne(this["cScale"+t],{h:180});for(let t=0;t<5;t++)this["surface"+t]=this["surface"+t]||ne(this.mainBkg,{h:30,l:-(5+t*5)}),this["surfacePeer"+t]=this["surfacePeer"+t]||ne(this.mainBkg,{h:30,l:-(7+t*5)});if(this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor,this.labelTextColor!=="calculated"){this.cScaleLabel0=this.cScaleLabel0||Ge(this.labelTextColor),this.cScaleLabel3=this.cScaleLabel3||Ge(this.labelTextColor);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleLabel"+t]=this["cScaleLabel"+t]||this.labelTextColor}this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.textColor,this.edgeLabelBackground=this.labelBackground,this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.signalColor=this.textColor,this.signalTextColor=this.textColor,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.rowOdd=this.rowOdd||je(this.primaryColor,75)||"#ffffff",this.rowEven=this.rowEven||je(this.primaryColor,1),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=ne(this.primaryColor,{h:64}),this.fillType3=ne(this.secondaryColor,{h:64}),this.fillType4=ne(this.primaryColor,{h:-64}),this.fillType5=ne(this.secondaryColor,{h:-64}),this.fillType6=ne(this.primaryColor,{h:128}),this.fillType7=ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||ne(this.tertiaryColor,{l:-40}),this.pie4=this.pie4||ne(this.primaryColor,{l:-10}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-30}),this.pie6=this.pie6||ne(this.tertiaryColor,{l:-20}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-20}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-40}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:-40}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-40}),this.pie11=this.pie11||ne(this.primaryColor,{h:-90,l:-40}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-30}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.venn1=this.venn1??ne(this.primaryColor,{l:-30}),this.venn2=this.venn2??ne(this.secondaryColor,{l:-30}),this.venn3=this.venn3??ne(this.tertiaryColor,{l:-40}),this.venn4=this.venn4??ne(this.primaryColor,{h:60,l:-30}),this.venn5=this.venn5??ne(this.primaryColor,{h:-60,l:-30}),this.venn6=this.venn6??ne(this.secondaryColor,{h:60,l:-30}),this.venn7=this.venn7??ne(this.primaryColor,{h:120,l:-30}),this.venn8=this.venn8??ne(this.secondaryColor,{h:120,l:-30}),this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.wardleyEvolutionColor=this.wardleyEvolutionColor||"#dc3545",this.wardley={backgroundColor:this.wardley?.backgroundColor||this.background,axisColor:this.wardley?.axisColor||this.lineColor,axisTextColor:this.wardley?.axisTextColor||this.primaryTextColor,gridColor:this.wardley?.gridColor||this.gridColor,componentFill:this.wardley?.componentFill||this.background,componentStroke:this.wardley?.componentStroke||this.lineColor,componentLabelColor:this.wardley?.componentLabelColor||this.primaryTextColor,linkStroke:this.wardley?.linkStroke||this.lineColor,evolutionStroke:this.wardley?.evolutionStroke||this.wardleyEvolutionColor,annotationStroke:this.wardley?.annotationStroke||this.lineColor,annotationTextColor:this.wardley?.annotationTextColor||this.primaryTextColor,annotationFill:this.wardley?.annotationFill||this.background},this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,dataLabelColor:this.xyChart?.dataLabelColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#ECECFF,#8493A6,#FFC3A0,#DCDDE1,#B8E994,#D1A36F,#C3CDE6,#FFB6C1,#496078,#F8F3E3"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.labelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||ne(this.primaryColor,{h:-30}),this.git4=this.git4||ne(this.primaryColor,{h:-60}),this.git5=this.git5||ne(this.primaryColor,{h:-90}),this.git6=this.git6||ne(this.primaryColor,{h:60}),this.git7=this.git7||ne(this.primaryColor,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Xe(Ge(this.git0),25),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Ge(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Ge(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.emUiFill=this.emUiFill||"white",this.emUiStroke=this.emUiStroke||"#dbdada",this.emProcessorFill=this.emProcessorFill||"#edb3f6",this.emProcessorStroke=this.emProcessorStroke||"#b88cbf",this.emReadModelFill=this.emReadModelFill||"#d3f1a2",this.emReadModelStroke=this.emReadModelStroke||"#a3b732",this.emCommandFill=this.emCommandFill||"#bcd6fe",this.emCommandStroke=this.emCommandStroke||"#679ac3",this.emEventFill=this.emEventFill||"#ffb778",this.emEventStroke=this.emEventStroke||"#c19a0f",this.emSwimlaneBackgroundOdd=this.emSwimlaneBackgroundOdd||"rgb(250,250,250)",this.emSwimlaneBackgroundStroke=this.emSwimlaneBackgroundStroke||"rgb(240,240,240)",this.emArrowhead=this.emArrowhead||this.lineColor,this.emRelationStroke=this.emRelationStroke||this.lineColor,this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(Object.keys(this).forEach(i=>{this[i]==="calculated"&&(this[i]=void 0)}),typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},Ao=o(e=>{let t=new v7;return t.calculate(e),t},"getThemeVariables")});var x7,EH,AH=z(()=>{"use strict";Cn();dl();Eo();x7=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=je("#cde498",10),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.primaryColor),this.lineColor=Ge(this.background),this.textColor=Ge(this.background),this.THEME_COLOR_LIMIT=12,this.radius=5,this.strokeWidth=1,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.vertLineColor="#00BFFF",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.noteFontWeight="normal",this.fontWeight="normal",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.useGradient=!0,this.gradientStart=this.primaryBorderColor,this.gradientStop=this.secondaryBorderColor,this.dropShadow="drop-shadow( 1px 2px 2px rgba(185,185,185,0.5))"}updateColors(){this.actorBorder=Xe(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Xe(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Xe(this.tertiaryColor,40);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScale"+t]=Xe(this["cScale"+t],10),this["cScalePeer"+t]=this["cScalePeer"+t]||Xe(this["cScale"+t],25);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleInv"+t]=this["cScaleInv"+t]||ne(this["cScale"+t],{h:180});this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor;for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleLabel"+t]=this["cScaleLabel"+t]||this.scaleLabelColor;for(let t=0;t<5;t++)this["surface"+t]=this["surface"+t]||ne(this.mainBkg,{h:30,s:-30,l:-(5+t*5)}),this["surfacePeer"+t]=this["surfacePeer"+t]||ne(this.mainBkg,{h:30,s:-30,l:-(8+t*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.taskBorderColor=this.border1,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.rowOdd=this.rowOdd||je(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||je(this.mainBkg,20),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=ne(this.primaryColor,{h:64}),this.fillType3=ne(this.secondaryColor,{h:64}),this.fillType4=ne(this.primaryColor,{h:-64}),this.fillType5=ne(this.secondaryColor,{h:-64}),this.fillType6=ne(this.primaryColor,{h:128}),this.fillType7=ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||ne(this.primaryColor,{l:-30}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-30}),this.pie6=this.pie6||ne(this.tertiaryColor,{h:40,l:-40}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-50}),this.pie11=this.pie11||ne(this.primaryColor,{h:-60,l:-50}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-50}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.venn1=this.venn1??ne(this.primaryColor,{l:-30}),this.venn2=this.venn2??ne(this.secondaryColor,{l:-30}),this.venn3=this.venn3??ne(this.tertiaryColor,{l:-30}),this.venn4=this.venn4??ne(this.primaryColor,{h:60,l:-30}),this.venn5=this.venn5??ne(this.primaryColor,{h:-60,l:-30}),this.venn6=this.venn6??ne(this.secondaryColor,{h:60,l:-30}),this.venn7=this.venn7??ne(this.primaryColor,{h:120,l:-30}),this.venn8=this.venn8??ne(this.secondaryColor,{h:120,l:-30}),this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.packet={startByteColor:this.primaryTextColor,endByteColor:this.primaryTextColor,labelColor:this.primaryTextColor,titleColor:this.primaryTextColor,blockStrokeColor:this.primaryTextColor,blockFillColor:this.mainBkg},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.wardleyEvolutionColor=this.wardleyEvolutionColor||"#dc3545",this.wardley={backgroundColor:this.wardley?.backgroundColor||this.background,axisColor:this.wardley?.axisColor||this.lineColor,axisTextColor:this.wardley?.axisTextColor||this.primaryTextColor,gridColor:this.wardley?.gridColor||this.gridColor,componentFill:this.wardley?.componentFill||this.background,componentStroke:this.wardley?.componentStroke||this.lineColor,componentLabelColor:this.wardley?.componentLabelColor||this.primaryTextColor,linkStroke:this.wardley?.linkStroke||this.lineColor,evolutionStroke:this.wardley?.evolutionStroke||this.wardleyEvolutionColor,annotationStroke:this.wardley?.annotationStroke||this.lineColor,annotationTextColor:this.wardley?.annotationTextColor||this.primaryTextColor,annotationFill:this.wardley?.annotationFill||this.background},this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,dataLabelColor:this.xyChart?.dataLabelColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#CDE498,#FF6B6B,#A0D2DB,#D7BDE2,#F0F0F0,#FFC3A0,#7FD8BE,#FF9A8B,#FAF3E0,#FFF176"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||ne(this.primaryColor,{h:-30}),this.git4=this.git4||ne(this.primaryColor,{h:-60}),this.git5=this.git5||ne(this.primaryColor,{h:-90}),this.git6=this.git6||ne(this.primaryColor,{h:60}),this.git7=this.git7||ne(this.primaryColor,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Ge(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Ge(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.emUiFill=this.emUiFill||"white",this.emUiStroke=this.emUiStroke||"#dbdada",this.emProcessorFill=this.emProcessorFill||"#edb3f6",this.emProcessorStroke=this.emProcessorStroke||"#b88cbf",this.emReadModelFill=this.emReadModelFill||"#d3f1a2",this.emReadModelStroke=this.emReadModelStroke||"#a3b732",this.emCommandFill=this.emCommandFill||"#bcd6fe",this.emCommandStroke=this.emCommandStroke||"#679ac3",this.emEventFill=this.emEventFill||"#ffb778",this.emEventStroke=this.emEventStroke||"#c19a0f",this.emSwimlaneBackgroundOdd=this.emSwimlaneBackgroundOdd||"rgb(250,250,250)",this.emSwimlaneBackgroundStroke=this.emSwimlaneBackgroundStroke||"rgb(240,240,240)",this.emArrowhead=this.emArrowhead||this.lineColor,this.emRelationStroke=this.emRelationStroke||this.lineColor,this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},EH=o(e=>{let t=new x7;return t.calculate(e),t},"getThemeVariables")});var b7,_H,DH=z(()=>{"use strict";Cn();Eo();dl();b7=class{static{o(this,"Theme")}constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=je(this.contrast,55),this.background="#ffffff",this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.lineColor=Ge(this.background),this.textColor=Ge(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.radius=5,this.strokeWidth=1,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor=this.actorBorder,this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.vertLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.noteFontWeight="normal",this.fontWeight="normal",this.rowOdd=this.rowOdd||je(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||"#f4f4f4",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.useGradient=!0,this.gradientStart=this.primaryBorderColor,this.gradientStop=this.secondaryBorderColor,this.dropShadow="drop-shadow( 1px 2px 2px rgba(185,185,185,1))"}updateColors(){this.secondBkg=je(this.contrast,55),this.border2=this.contrast,this.actorBorder=je(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.actorBorder,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleInv"+t]=this["cScaleInv"+t]||Ge(this["cScale"+t]);for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this.darkMode?this["cScalePeer"+t]=this["cScalePeer"+t]||je(this["cScale"+t],10):this["cScalePeer"+t]=this["cScalePeer"+t]||Xe(this["cScale"+t],10);this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor),this.cScaleLabel0=this.cScaleLabel0||this.cScale1,this.cScaleLabel2=this.cScaleLabel2||this.cScale1;for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["cScaleLabel"+t]=this["cScaleLabel"+t]||this.scaleLabelColor;for(let t=0;t<5;t++)this["surface"+t]=this["surface"+t]||ne(this.mainBkg,{l:-(5+t*5)}),this["surfacePeer"+t]=this["surfacePeer"+t]||ne(this.mainBkg,{l:-(8+t*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.text,this.sectionBkgColor=je(this.contrast,30),this.sectionBkgColor2=je(this.contrast,30),this.taskBorderColor=Xe(this.contrast,10),this.taskBkgColor=this.contrast,this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor=this.text,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.gridColor=je(this.border1,30),this.doneTaskBkgColor=this.done,this.doneTaskBorderColor=this.lineColor,this.critBkgColor=this.critical,this.critBorderColor=Xe(this.critBkgColor,10),this.todayLineColor=this.critBkgColor,this.vertLineColor=this.critBkgColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||"#000",this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f4f4f4",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.stateBorder=this.stateBorder||"#000",this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#222",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=ne(this.primaryColor,{h:64}),this.fillType3=ne(this.secondaryColor,{h:64}),this.fillType4=ne(this.primaryColor,{h:-64}),this.fillType5=ne(this.secondaryColor,{h:-64}),this.fillType6=ne(this.primaryColor,{h:128}),this.fillType7=ne(this.secondaryColor,{h:128});for(let t=0;t<this.THEME_COLOR_LIMIT;t++)this["pie"+t]=this["cScale"+t];this.pie12=this.pie0,this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7";for(let t=0;t<8;t++)this["venn"+(t+1)]=this["venn"+(t+1)]??this["cScale"+t];this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,dataLabelColor:this.xyChart?.dataLabelColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#EEE,#6BB8E4,#8ACB88,#C7ACD6,#E8DCC2,#FFB2A8,#FFF380,#7E8D91,#FFD8B1,#FAF3E0"},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.wardleyEvolutionColor=this.wardleyEvolutionColor||"#dc3545",this.wardley={backgroundColor:this.wardley?.backgroundColor||this.background,axisColor:this.wardley?.axisColor||this.lineColor,axisTextColor:this.wardley?.axisTextColor||this.primaryTextColor,gridColor:this.wardley?.gridColor||this.gridColor,componentFill:this.wardley?.componentFill||this.background,componentStroke:this.wardley?.componentStroke||this.lineColor,componentLabelColor:this.wardley?.componentLabelColor||this.primaryTextColor,linkStroke:this.wardley?.linkStroke||this.lineColor,evolutionStroke:this.wardley?.evolutionStroke||this.wardleyEvolutionColor,annotationStroke:this.wardley?.annotationStroke||this.lineColor,annotationTextColor:this.wardley?.annotationTextColor||this.primaryTextColor,annotationFill:this.wardley?.annotationFill||this.background},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=Xe(this.pie1,25)||this.primaryColor,this.git1=this.pie2||this.secondaryColor,this.git2=this.pie3||this.tertiaryColor,this.git3=this.pie4||ne(this.primaryColor,{h:-30}),this.git4=this.pie5||ne(this.primaryColor,{h:-60}),this.git5=this.pie6||ne(this.primaryColor,{h:-90}),this.git6=this.pie7||ne(this.primaryColor,{h:60}),this.git7=this.pie8||ne(this.primaryColor,{h:120}),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||this.labelTextColor,this.gitBranchLabel0=this.branchLabelColor,this.gitBranchLabel1="white",this.gitBranchLabel2=this.branchLabelColor,this.gitBranchLabel3="white",this.gitBranchLabel4=this.branchLabelColor,this.gitBranchLabel5=this.branchLabelColor,this.gitBranchLabel6=this.branchLabelColor,this.gitBranchLabel7=this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.emUiFill=this.emUiFill||"white",this.emUiStroke=this.emUiStroke||"#dbdada",this.emProcessorFill=this.emProcessorFill||"#edb3f6",this.emProcessorStroke=this.emProcessorStroke||"#b88cbf",this.emReadModelFill=this.emReadModelFill||"#d3f1a2",this.emReadModelStroke=this.emReadModelStroke||"#a3b732",this.emCommandFill=this.emCommandFill||"#bcd6fe",this.emCommandStroke=this.emCommandStroke||"#679ac3",this.emEventFill=this.emEventFill||"#ffb778",this.emEventStroke=this.emEventStroke||"#c19a0f",this.emSwimlaneBackgroundOdd=this.emSwimlaneBackgroundOdd||"rgb(250,250,250)",this.emSwimlaneBackgroundStroke=this.emSwimlaneBackgroundStroke||"rgb(240,240,240)",this.emArrowhead=this.emArrowhead||this.lineColor,this.emRelationStroke=this.emRelationStroke||this.lineColor,this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},_H=o(e=>{let t=new b7;return t.calculate(e),t},"getThemeVariables")});var T7,RH,LH=z(()=>{"use strict";Cn();Eo();dl();T7=class{static{o(this,"Theme")}constructor(){this.background="#ffffff",this.primaryColor="#cccccc",this.mainBkg="#ffffff",this.noteBkgColor="#fff5ad",this.noteTextColor="#333",this.THEME_COLOR_LIMIT=12,this.radius=3,this.strokeWidth=2,this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.fontFamily="arial, sans-serif",this.fontSize="14px",this.nodeBorder="#000000",this.stateBorder="#000000",this.useGradient=!0,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="drop-shadow( 0px 1px 2px rgba(0, 0, 0, 0.25));",this.tertiaryColor="#ffffff",this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.noteFontWeight="normal",this.fontWeight="normal"}updateColors(){this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#333"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#333",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor);let t="#ECECFE",r="#E9E9F1",i=ne(t,{h:180,l:5});if(this.sectionBkgColor=this.sectionBkgColor||i,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||r,this.sectionBkgColor2=this.sectionBkgColor2||t,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||t,this.activeTaskBorderColor=this.activeTaskBorderColor||t,this.activeTaskBkgColor=this.activeTaskBkgColor||je(t,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||t,this.cScale1=this.cScale1||r,this.cScale2=this.cScale2||i,this.cScale3=this.cScale3||ne(t,{h:30}),this.cScale4=this.cScale4||ne(t,{h:60}),this.cScale5=this.cScale5||ne(t,{h:90}),this.cScale6=this.cScale6||ne(t,{h:120}),this.cScale7=this.cScale7||ne(t,{h:150}),this.cScale8=this.cScale8||ne(t,{h:210,l:150}),this.cScale9=this.cScale9||ne(t,{h:270}),this.cScale10=this.cScale10||ne(t,{h:300}),this.cScale11=this.cScale11||ne(t,{h:330}),this.darkMode)for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=Xe(this["cScale"+a],75);else for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=Xe(this["cScale"+a],25);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Ge(this["cScale"+a]);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this.darkMode?this["cScalePeer"+a]=this["cScalePeer"+a]||je(this["cScale"+a],10):this["cScalePeer"+a]=this["cScalePeer"+a]||Xe(this["cScale"+a],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;let n=this.darkMode?-4:-1;for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(5+a*3)}),this["surfacePeer"+a]=this["surfacePeer"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(8+a*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||t,this.fillType1=this.fillType1||r,this.fillType2=this.fillType2||ne(t,{h:64}),this.fillType3=this.fillType3||ne(r,{h:64}),this.fillType4=this.fillType4||ne(t,{h:-64}),this.fillType5=this.fillType5||ne(r,{h:-64}),this.fillType6=this.fillType6||ne(t,{h:128}),this.fillType7=this.fillType7||ne(r,{h:128}),this.pie1=this.pie1||t,this.pie2=this.pie2||r,this.pie3=this.pie3||i,this.pie4=this.pie4||ne(t,{l:-10}),this.pie5=this.pie5||ne(r,{l:-10}),this.pie6=this.pie6||ne(i,{l:-10}),this.pie7=this.pie7||ne(t,{h:60,l:-10}),this.pie8=this.pie8||ne(t,{h:-60,l:-10}),this.pie9=this.pie9||ne(t,{h:120,l:0}),this.pie10=this.pie10||ne(t,{h:60,l:-20}),this.pie11=this.pie11||ne(t,{h:-60,l:-20}),this.pie12=this.pie12||ne(t,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||t,this.quadrant2Fill=this.quadrant2Fill||ne(t,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(t,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(t,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||t,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||t,this.git1=this.git1||r,this.git2=this.git2||i,this.git3=this.git3||ne(t,{h:-30}),this.git4=this.git4||ne(t,{h:-60}),this.git5=this.git5||ne(t,{h:-90}),this.git6=this.git6||ne(t,{h:60}),this.git7=this.git7||ne(t,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},RH=o(e=>{let t=new T7;return t.calculate(e),t},"getThemeVariables")});var C7,MH,NH=z(()=>{"use strict";Cn();Eo();dl();C7=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=je(this.primaryColor,16),this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Ge(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.mainBkg="#2a2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=je(Ge("#323D47"),10),this.border1="#ccc",this.border2=mn(255,255,255,.25),this.arrowheadColor=Ge(this.background),this.fontFamily="arial, sans-serif",this.fontSize="14px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.radius=3,this.strokeWidth=1,this.noteBkgColor="#fff5ad",this.noteTextColor="#333",this.THEME_COLOR_LIMIT=12,this.fontFamily="arial, sans-serif",this.fontSize="14px",this.useGradient=!0,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="drop-shadow( 1px 2px 2px rgba(185,185,185,0.2))",this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.noteFontWeight="normal",this.fontWeight="normal"}updateColors(){if(this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#333"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#333",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.border1,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor),this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||je(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],75);else for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],25);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleInv"+r]=this["cScaleInv"+r]||Ge(this["cScale"+r]);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this.darkMode?this["cScalePeer"+r]=this["cScalePeer"+r]||je(this["cScale"+r],10):this["cScalePeer"+r]=this["cScalePeer"+r]||Xe(this["cScale"+r],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleLabel"+r]=this["cScaleLabel"+r]||this.scaleLabelColor;let t=this.darkMode?-4:-1;for(let r=0;r<5;r++)this["surface"+r]=this["surface"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(5+r*3)}),this["surfacePeer"+r]=this["surfacePeer"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(8+r*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||ne(this.primaryColor,{h:64}),this.fillType3=this.fillType3||ne(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||ne(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||ne(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||ne(this.primaryColor,{h:128}),this.fillType7=this.fillType7||ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||ne(this.primaryColor,{l:-10}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-10}),this.pie6=this.pie6||ne(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||ne(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||"#0b0000",this.git1=this.git1||"#4d1037",this.git2=this.git2||"#3f5258",this.git3=this.git3||"#4f2f1b",this.git4=this.git4||"#6e0a0a",this.git5=this.git5||"#3b0048",this.git6=this.git6||"#995a01",this.git7=this.git7||"#154706",this.gitDarkMode=!0,this.gitDarkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},MH=o(e=>{let t=new C7;return t.calculate(e),t},"getThemeVariables")});var k7,IH,OH=z(()=>{"use strict";Cn();Eo();dl();k7=class{static{o(this,"Theme")}constructor(){this.background="#ffffff",this.primaryColor="#cccccc",this.mainBkg="#ffffff",this.noteBkgColor="#fff5ad",this.noteTextColor="#28253D",this.THEME_COLOR_LIMIT=12,this.radius=12,this.strokeWidth=2,this.primaryBorderColor=Jt("#28253D",this.darkMode),this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.nodeBorder="#28253D",this.stateBorder="#28253D",this.useGradient=!1,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="url(#drop-shadow)",this.nodeShadow=!0,this.tertiaryColor="#ffffff",this.clusterBkg="#F9F9FB",this.clusterBorder="#BDBCCC",this.noteBorderColor="#FACC15",this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.actorBorder="#28253D",this.filterColor="#000000"}updateColors(){this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#28253D"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#FEF9C3",this.noteTextColor=this.noteTextColor||"#28253D",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.noteFontWeight=600,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor);let t="#ECECFE",r="#E9E9F1",i=ne(t,{h:180,l:5});this.sectionBkgColor=this.sectionBkgColor||i,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||r,this.sectionBkgColor2=this.sectionBkgColor2||t,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||t,this.activeTaskBorderColor=this.activeTaskBorderColor||t,this.activeTaskBkgColor=this.activeTaskBkgColor||je(t,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.compositeTitleBackground="#F9F9FB",this.altBackground="#F9F9FB",this.stateEdgeLabelBackground="#FFFFFF",this.fontWeight=600,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=this.mainBkg;if(this.darkMode)for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=Xe(this["cScale"+a],75);else for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=Xe(this["cScale"+a],25);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Ge(this["cScale"+a]);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this.darkMode?this["cScalePeer"+a]=this["cScalePeer"+a]||je(this["cScale"+a],10):this["cScalePeer"+a]=this["cScalePeer"+a]||Xe(this["cScale"+a],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;let n=this.darkMode?-4:-1;for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(5+a*3)}),this["surfacePeer"+a]=this["surfacePeer"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(8+a*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||t,this.fillType1=this.fillType1||r,this.fillType2=this.fillType2||ne(t,{h:64}),this.fillType3=this.fillType3||ne(r,{h:64}),this.fillType4=this.fillType4||ne(t,{h:-64}),this.fillType5=this.fillType5||ne(r,{h:-64}),this.fillType6=this.fillType6||ne(t,{h:128}),this.fillType7=this.fillType7||ne(r,{h:128}),this.pie1=this.pie1||t,this.pie2=this.pie2||r,this.pie3=this.pie3||i,this.pie4=this.pie4||ne(t,{l:-10}),this.pie5=this.pie5||ne(r,{l:-10}),this.pie6=this.pie6||ne(i,{l:-10}),this.pie7=this.pie7||ne(t,{h:60,l:-10}),this.pie8=this.pie8||ne(t,{h:-60,l:-10}),this.pie9=this.pie9||ne(t,{h:120,l:0}),this.pie10=this.pie10||ne(t,{h:60,l:-20}),this.pie11=this.pie11||ne(t,{h:-60,l:-20}),this.pie12=this.pie12||ne(t,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||t,this.quadrant2Fill=this.quadrant2Fill||ne(t,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(t,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(t,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||t,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.requirementEdgeLabelBackground="#FFFFFF",this.git0=this.git0||t,this.git1=this.git1||r,this.git2=this.git2||i,this.git3=this.git3||ne(t,{h:-30}),this.git4=this.git4||ne(t,{h:-60}),this.git5=this.git5||ne(t,{h:-90}),this.git6=this.git6||ne(t,{h:60}),this.git7=this.git7||ne(t,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.commitLineColor=this.commitLineColor??"#BDBCCC",this.erEdgeLabelBackground="#FFFFFF",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},IH=o(e=>{let t=new k7;return t.calculate(e),t},"getThemeVariables")});var w7,PH,BH=z(()=>{"use strict";Cn();Eo();dl();w7=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=je(this.primaryColor,16),this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Ge(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.mainBkg="#111113",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=je(Ge("#323D47"),10),this.border1="#ccc",this.border2=mn(255,255,255,.25),this.arrowheadColor=Ge(this.background),this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.labelBackground="#111113",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.radius=12,this.strokeWidth=2,this.noteBkgColor=this.noteBkgColor??"#FEF9C3",this.noteTextColor=this.noteTextColor??"#28253D",this.THEME_COLOR_LIMIT=12,this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.nodeBorder="#FFFFFF",this.stateBorder="#FFFFFF",this.useGradient=!1,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="url(#drop-shadow)",this.nodeShadow=!0,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.clusterBkg="#1E1A2E",this.clusterBorder="#BDBCCC",this.noteBorderColor="#FACC15",this.noteFontWeight=600,this.filterColor="#FFFFFF"}updateColors(){if(this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#FFFFFF"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#FFFFFF",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.border1,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder="#FFFFFF",this.signalColor="#FFFFFF",this.labelBoxBorderColor="#BDBCCC",this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor),this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||je(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.compositeBackground="#16141F",this.altBackground="#16141F",this.compositeTitleBackground="#16141F",this.stateEdgeLabelBackground="#16141F",this.fontWeight=600,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||ne(this.primaryColor,{h:30}),this.cScale4=this.cScale4||ne(this.primaryColor,{h:60}),this.cScale5=this.cScale5||ne(this.primaryColor,{h:90}),this.cScale6=this.cScale6||ne(this.primaryColor,{h:120}),this.cScale7=this.cScale7||ne(this.primaryColor,{h:150}),this.cScale8=this.cScale8||ne(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||ne(this.primaryColor,{h:270}),this.cScale10=this.cScale10||ne(this.primaryColor,{h:300}),this.cScale11=this.cScale11||ne(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],75);else for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Xe(this["cScale"+r],25);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleInv"+r]=this["cScaleInv"+r]||Ge(this["cScale"+r]);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this.darkMode?this["cScalePeer"+r]=this["cScalePeer"+r]||je(this["cScale"+r],10):this["cScalePeer"+r]=this["cScalePeer"+r]||Xe(this["cScale"+r],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleLabel"+r]=this["cScaleLabel"+r]||this.scaleLabelColor;let t=this.darkMode?-4:-1;for(let r=0;r<5;r++)this["surface"+r]=this["surface"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(5+r*3)}),this["surfacePeer"+r]=this["surfacePeer"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(8+r*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||ne(this.primaryColor,{h:64}),this.fillType3=this.fillType3||ne(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||ne(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||ne(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||ne(this.primaryColor,{h:128}),this.fillType7=this.fillType7||ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||ne(this.primaryColor,{l:-10}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-10}),this.pie6=this.pie6||ne(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||ne(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.requirementEdgeLabelBackground="#16141F",this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||ne(this.primaryColor,{h:-30}),this.git4=this.git4||ne(this.primaryColor,{h:-60}),this.git5=this.git5||ne(this.primaryColor,{h:-90}),this.git6=this.git6||ne(this.primaryColor,{h:60}),this.git7=this.git7||ne(this.primaryColor,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.commitLineColor=this.commitLineColor??"#BDBCCC",this.erEdgeLabelBackground="#16141F",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},PH=o(e=>{let t=new w7;return t.calculate(e),t},"getThemeVariables")});var S7,FH,$H=z(()=>{"use strict";Cn();Eo();dl();S7=class{static{o(this,"Theme")}constructor(){this.background="#ffffff",this.primaryColor="#cccccc",this.mainBkg="#ffffff",this.noteBkgColor="#fff5ad",this.noteTextColor="#28253D",this.THEME_COLOR_LIMIT=12,this.radius=12,this.strokeWidth=2,this.primaryBorderColor=Jt(this.primaryColor,this.darkMode),this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.nodeBorder="#28253D",this.stateBorder="#28253D",this.useGradient=!1,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="url(#drop-shadow)",this.nodeShadow=!0,this.tertiaryColor="#ffffff",this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.actorBorder="#28253D",this.noteBorderColor="#FACC15",this.noteFontWeight=600,this.borderColorArray=["#E879F9","#2DD4BF","#FB923C","#22D3EE","#4ADE80","#A78BFA","#F87171","#FACC15","#818CF8","#A3E635 ","#38BDF8","#FB7185"],this.bkgColorArray=["#FDF4FF","#F0FDFA","#FFF7ED","#ECFEFF","#F0FDF4","#F5F3FF","#FEF2F2","#FEFCE8","#EEF2FF","#F7FEE7","#F0F9FF","#FFF1F2"],this.filterColor="#000000"}updateColors(){this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#28253D"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#28253D",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor);let t="#ECECFE",r="#E9E9F1",i=ne(t,{h:180,l:5});this.sectionBkgColor=this.sectionBkgColor||i,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||r,this.sectionBkgColor2=this.sectionBkgColor2||t,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||t,this.activeTaskBorderColor=this.activeTaskBorderColor||t,this.activeTaskBkgColor=this.activeTaskBkgColor||je(t,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||"#f4a8ff",this.cScale1=this.cScale1||"#46ecd5",this.cScale2=this.cScale2||"#ffb86a",this.cScale3=this.cScale3||"#dab2ff",this.cScale4=this.cScale4||"#7bf1a8",this.cScale5=this.cScale5||"#c4b4ff",this.cScale6=this.cScale6||"#ffa2a2",this.cScale7=this.cScale7||"#ffdf20",this.cScale8=this.cScale8||"#a3b3ff",this.cScale9=this.cScale9||"#bbf451",this.cScale10=this.cScale10||"#74d4ff",this.cScale11=this.cScale11||"#ffa1ad";for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Ge(this["cScale"+a]);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this.darkMode?this["cScalePeer"+a]=this["cScalePeer"+a]||je(this["cScale"+a],10):this["cScalePeer"+a]=this["cScalePeer"+a]||Xe(this["cScale"+a],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;let n=this.darkMode?-4:-1;for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(5+a*3)}),this["surfacePeer"+a]=this["surfacePeer"+a]||ne(this.mainBkg,{h:180,s:-15,l:n*(8+a*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||t,this.fillType1=this.fillType1||r,this.fillType2=this.fillType2||ne(t,{h:64}),this.fillType3=this.fillType3||ne(r,{h:64}),this.fillType4=this.fillType4||ne(t,{h:-64}),this.fillType5=this.fillType5||ne(r,{h:-64}),this.fillType6=this.fillType6||ne(t,{h:128}),this.fillType7=this.fillType7||ne(r,{h:128}),this.pie1=this.pie1||t,this.pie2=this.pie2||r,this.pie3=this.pie3||i,this.pie4=this.pie4||ne(t,{l:-10}),this.pie5=this.pie5||ne(r,{l:-10}),this.pie6=this.pie6||ne(i,{l:-10}),this.pie7=this.pie7||ne(t,{h:60,l:-10}),this.pie8=this.pie8||ne(t,{h:-60,l:-10}),this.pie9=this.pie9||ne(t,{h:120,l:0}),this.pie10=this.pie10||ne(t,{h:60,l:-20}),this.pie11=this.pie11||ne(t,{h:-60,l:-20}),this.pie12=this.pie12||ne(t,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||t,this.quadrant2Fill=this.quadrant2Fill||ne(t,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(t,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(t,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||t,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||t,this.git1=this.git1||r,this.git2=this.git2||i,this.git3=this.git3||ne(t,{h:-30}),this.git4=this.git4||ne(t,{h:-60}),this.git5=this.git5||ne(t,{h:-90}),this.git6=this.git6||ne(t,{h:60}),this.git7=this.git7||ne(t,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLineColor=this.commitLineColor??"#BDBCCC",this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.fontWeight=600,this.erEdgeLabelBackground="#FFFFFF",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},FH=o(e=>{let t=new S7;return t.calculate(e),t},"getThemeVariables")});var E7,zH,GH=z(()=>{"use strict";Cn();Eo();dl();E7=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=je(this.primaryColor,16),this.tertiaryColor=ne(this.primaryColor,{h:-160}),this.primaryBorderColor=Ge(this.background),this.secondaryBorderColor=Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Jt(this.tertiaryColor,this.darkMode),this.primaryTextColor=Ge(this.primaryColor),this.secondaryTextColor=Ge(this.secondaryColor),this.tertiaryTextColor=Ge(this.tertiaryColor),this.mainBkg="#111113",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=je(Ge("#323D47"),10),this.border1="#ccc",this.border2=mn(255,255,255,.25),this.arrowheadColor=Ge(this.background),this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.labelBackground="#111113",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.radius=12,this.strokeWidth=2,this.noteBkgColor=this.noteBkgColor??"#FEF9C3",this.noteTextColor=this.noteTextColor??"#28253D",this.THEME_COLOR_LIMIT=12,this.fontFamily='"Recursive Variable", arial, sans-serif',this.fontSize="14px",this.nodeBorder="#FFFFFF",this.stateBorder="#FFFFFF",this.useGradient=!1,this.gradientStart="#0042eb",this.gradientStop="#eb0042",this.dropShadow="url(#drop-shadow)",this.nodeShadow=!0,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.clusterBkg="#1E1A2E",this.clusterBorder="#BDBCCC",this.noteBorderColor="#FACC15",this.noteFontWeight=600,this.borderColorArray=["#E879F9","#2DD4BF","#FB923C","#22D3EE","#4ADE80","#A78BFA","#F87171","#FACC15","#818CF8","#A3E635 ","#38BDF8","#FB7185"],this.bkgColorArray=[],this.filterColor="#FFFFFF"}updateColors(){this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#FFFFFF"),this.secondaryColor=this.secondaryColor||ne(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||ne(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||Jt(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||Jt(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||Jt(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||Jt(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#FFFFFF",this.secondaryTextColor=this.secondaryTextColor||Ge(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Ge(this.tertiaryColor),this.lineColor=this.lineColor||Ge(this.background),this.arrowheadColor=this.arrowheadColor||Ge(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.border1,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder="#FFFFFF",this.signalColor="#FFFFFF",this.labelBoxBorderColor="#BDBCCC",this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||this.actorBorder,this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||Xe(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Ge(this.lineColor),this.rootLabelColor="#FFFFFF",this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||je(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.vertLineColor=this.vertLineColor||this.primaryBorderColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||"#f4a8ff",this.cScale1=this.cScale1||"#46ecd5",this.cScale2=this.cScale2||"#ffb86a",this.cScale3=this.cScale3||"#dab2ff",this.cScale4=this.cScale4||"#7bf1a8",this.cScale5=this.cScale5||"#c4b4ff",this.cScale6=this.cScale6||"#ffa2a2",this.cScale7=this.cScale7||"#ffdf20",this.cScale8=this.cScale8||"#a3b3ff",this.cScale9=this.cScale9||"#bbf451",this.cScale10=this.cScale10||"#74d4ff",this.cScale11=this.cScale11||"#ffa1ad";for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleInv"+r]=this["cScaleInv"+r]||Ge(this["cScale"+r]);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this.darkMode?this["cScalePeer"+r]=this["cScalePeer"+r]||je(this["cScale"+r],10):this["cScalePeer"+r]=this["cScalePeer"+r]||Xe(this["cScale"+r],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleLabel"+r]=Xe(this["cScale"+r],75);let t=this.darkMode?-4:-1;for(let r=0;r<5;r++)this["surface"+r]=this["surface"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(5+r*3)}),this["surfacePeer"+r]=this["surfacePeer"+r]||ne(this.mainBkg,{h:180,s:-15,l:t*(8+r*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||ne(this.primaryColor,{h:64}),this.fillType3=this.fillType3||ne(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||ne(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||ne(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||ne(this.primaryColor,{h:128}),this.fillType7=this.fillType7||ne(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||ne(this.primaryColor,{l:-10}),this.pie5=this.pie5||ne(this.secondaryColor,{l:-10}),this.pie6=this.pie6||ne(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||ne(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||ne(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||ne(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||ne(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||ne(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||ne(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.vennTitleTextColor=this.vennTitleTextColor??this.titleColor,this.vennSetTextColor=this.vennSetTextColor??this.textColor,this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||ne(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||ne(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||ne(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||ne(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||ne(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||ne(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ri(this.quadrant1Fill)?je(this.quadrant1Fill):Xe(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Xe(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||ne(this.primaryColor,{h:-30}),this.git4=this.git4||ne(this.primaryColor,{h:-60}),this.git5=this.git5||ne(this.primaryColor,{h:-90}),this.git6=this.git6||ne(this.primaryColor,{h:60}),this.git7=this.git7||ne(this.primaryColor,{h:120}),this.darkMode?(this.git0=je(this.git0,25),this.git1=je(this.git1,25),this.git2=je(this.git2,25),this.git3=je(this.git3,25),this.git4=je(this.git4,25),this.git5=je(this.git5,25),this.git6=je(this.git6,25),this.git7=je(this.git7,25)):(this.git0=Xe(this.git0,25),this.git1=Xe(this.git1,25),this.git2=Xe(this.git2,25),this.git3=Xe(this.git3,25),this.git4=Xe(this.git4,25),this.git5=Xe(this.git5,25),this.git6=Xe(this.git6,25),this.git7=Xe(this.git7,25)),this.gitInv0=this.gitInv0||Ge(this.git0),this.gitInv1=this.gitInv1||Ge(this.git1),this.gitInv2=this.gitInv2||Ge(this.git2),this.gitInv3=this.gitInv3||Ge(this.git3),this.gitInv4=this.gitInv4||Ge(this.git4),this.gitInv5=this.gitInv5||Ge(this.git5),this.gitInv6=this.gitInv6||Ge(this.git6),this.gitInv7=this.gitInv7||Ge(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.commitLineColor=this.commitLineColor??"#BDBCCC",this.fontWeight=600,this.erEdgeLabelBackground="#16141F",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||kn,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||wn}calculate(t){if(typeof t!="object"){this.updateColors();return}let r=Object.keys(t);r.forEach(i=>{this[i]=t[i]}),this.updateColors(),r.forEach(i=>{this[i]=t[i]})}},zH=o(e=>{let t=new E7;return t.calculate(e),t},"getThemeVariables")});var _o,LC=z(()=>{"use strict";kH();SH();$f();AH();DH();LH();NH();OH();BH();$H();GH();_o={base:{getThemeVariables:CH},dark:{getThemeVariables:wH},default:{getThemeVariables:Ao},forest:{getThemeVariables:EH},neutral:{getThemeVariables:_H},neo:{getThemeVariables:RH},"neo-dark":{getThemeVariables:MH},redux:{getThemeVariables:IH},"redux-dark":{getThemeVariables:PH},"redux-color":{getThemeVariables:FH},"redux-dark-color":{getThemeVariables:zH}}});var Ea,VH=z(()=>{"use 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sans-serif',systemFontWeight:"normal",external_systemFontSize:14,external_systemFontFamily:'"Open Sans", sans-serif',external_systemFontWeight:"normal",system_dbFontSize:14,system_dbFontFamily:'"Open Sans", sans-serif',system_dbFontWeight:"normal",external_system_dbFontSize:14,external_system_dbFontFamily:'"Open Sans", sans-serif',external_system_dbFontWeight:"normal",system_queueFontSize:14,system_queueFontFamily:'"Open Sans", sans-serif',system_queueFontWeight:"normal",external_system_queueFontSize:14,external_system_queueFontFamily:'"Open Sans", sans-serif',external_system_queueFontWeight:"normal",boundaryFontSize:14,boundaryFontFamily:'"Open Sans", sans-serif',boundaryFontWeight:"normal",messageFontSize:12,messageFontFamily:'"Open Sans", sans-serif',messageFontWeight:"normal",containerFontSize:14,containerFontFamily:'"Open Sans", sans-serif',containerFontWeight:"normal",external_containerFontSize:14,external_containerFontFamily:'"Open Sans", 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this.expect("}"),this.nextToken=r,i}parseExpression(t,r){for(var i=[];;){this.mode==="math"&&this.consumeSpaces();var n=this.fetch();if(e.endOfExpression.indexOf(n.text)!==-1||r&&n.text===r||t&&Fh[n.text]&&Fh[n.text].infix)break;var a=this.parseAtom(r);if(a){if(a.type==="internal")continue}else break;i.push(a)}return this.mode==="text"&&this.formLigatures(i),this.handleInfixNodes(i)}handleInfixNodes(t){for(var r=-1,i,n=0;n<t.length;n++)if(t[n].type==="infix"){if(r!==-1)throw new Lt("only one infix operator per group",t[n].token);r=n,i=t[n].replaceWith}if(r!==-1&&i){var a,s,l=t.slice(0,r),u=t.slice(r+1);l.length===1&&l[0].type==="ordgroup"?a=l[0]:a={type:"ordgroup",mode:this.mode,body:l},u.length===1&&u[0].type==="ordgroup"?s=u[0]:s={type:"ordgroup",mode:this.mode,body:u};var h;return i==="\\\\abovefrac"?h=this.callFunction(i,[a,t[r],s],[]):h=this.callFunction(i,[a,s],[]),[h]}else return t}handleSupSubscript(t){var r=this.fetch(),i=r.text;this.consume(),this.consumeSpaces();var n;do{var a;n=this.parseGroup(t)}while(((a=n)==null?void 0:a.type)==="internal");if(!n)throw new Lt("Expected group after '"+i+"'",r);return n}formatUnsupportedCmd(t){for(var r=[],i=0;i<t.length;i++)r.push({type:"textord",mode:"text",text:t[i]});var n={type:"text",mode:this.mode,body:r},a={type:"color",mode:this.mode,color:this.settings.errorColor,body:[n]};return a}parseAtom(t){var r=this.parseGroup("atom",t);if(r?.type==="internal"||this.mode==="text")return r;for(var i,n;;){this.consumeSpaces();var a=this.fetch();if(a.text==="\\limits"||a.text==="\\nolimits"){if(r&&r.type==="op"){var s=a.text==="\\limits";r.limits=s,r.alwaysHandleSupSub=!0}else if(r&&r.type==="operatorname")r.alwaysHandleSupSub&&(r.limits=a.text==="\\limits");else throw new Lt("Limit controls must follow a math operator",a);this.consume()}else if(a.text==="^"){if(i)throw new Lt("Double superscript",a);i=this.handleSupSubscript("superscript")}else if(a.text==="_"){if(n)throw new Lt("Double 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+Expecting `+ht.join(", ")+", got '"+(this.terminals_[le]||le)+"'":gt="Parse error on line "+(fe+1)+": Unexpected "+(le==Re?"end of input":"'"+(this.terminals_[le]||le)+"'"),this.parseError(gt,{text:Oe.match,token:this.terminals_[le]||le,line:Oe.yylineno,loc:Ae,expected:ht})}if(ot[0]instanceof Array&&ot.length>1)throw new Error("Parse Error: multiple actions possible at state: "+me+", token: "+le);switch(ot[0]){case 1:at.push(le),Ue.push(Oe.yytext),ye.push(Oe.yylloc),at.push(ot[1]),le=null,st?(le=st,st=null):(W=Oe.yyleng,ie=Oe.yytext,fe=Oe.yylineno,Ae=Oe.yylloc,ce>0&&ce--);break;case 2:if(yt=this.productions_[ot[1]][1],Tt.$=Ue[Ue.length-yt],Tt._$={first_line:ye[ye.length-(yt||1)].first_line,last_line:ye[ye.length-1].last_line,first_column:ye[ye.length-(yt||1)].first_column,last_column:ye[ye.length-1].last_column},Ve&&(Tt._$.range=[ye[ye.length-(yt||1)].range[0],ye[ye.length-1].range[1]]),Gt=this.performAction.apply(Tt,[ie,W,fe,be.yy,ot[1],Ue,ye].concat(xe)),typeof Gt<"u")return Gt;yt&&(at=at.slice(0,-1*yt*2),Ue=Ue.slice(0,-1*yt),ye=ye.slice(0,-1*yt)),at.push(this.productions_[ot[1]][0]),Ue.push(Tt.$),ye.push(Tt._$),oe=ve[at[at.length-2]][at[at.length-1]],at.push(oe);break;case 3:return!0}}return!0},"parse")},de=(function(){var ke={EOF:1,parseError:o(function(He,at){if(this.yy.parser)this.yy.parser.parseError(He,at);else throw new Error(He)},"parseError"),setInput:o(function(Fe,He){return this.yy=He||this.yy||{},this._input=Fe,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var Fe=this._input[0];this.yytext+=Fe,this.yyleng++,this.offset++,this.match+=Fe,this.matched+=Fe;var He=Fe.match(/(?:\r\n?|\n).*/g);return He?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Fe},"input"),unput:o(function(Fe){var He=Fe.length,at=Fe.split(/(?:\r\n?|\n)/g);this._input=Fe+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-He),this.offset-=He;var qe=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),at.length-1&&(this.yylineno-=at.length-1);var Ue=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:at?(at.length===qe.length?this.yylloc.first_column:0)+qe[qe.length-at.length].length-at[0].length:this.yylloc.first_column-He},this.options.ranges&&(this.yylloc.range=[Ue[0],Ue[0]+this.yyleng-He]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(Fe){this.unput(this.match.slice(Fe))},"less"),pastInput:o(function(){var Fe=this.matched.substr(0,this.matched.length-this.match.length);return(Fe.length>20?"...":"")+Fe.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var Fe=this.match;return Fe.length<20&&(Fe+=this._input.substr(0,20-Fe.length)),(Fe.substr(0,20)+(Fe.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var Fe=this.pastInput(),He=new Array(Fe.length+1).join("-");return Fe+this.upcomingInput()+`
+`+He+"^"},"showPosition"),test_match:o(function(Fe,He){var at,qe,Ue;if(this.options.backtrack_lexer&&(Ue={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Ue.yylloc.range=this.yylloc.range.slice(0))),qe=Fe[0].match(/(?:\r\n?|\n).*/g),qe&&(this.yylineno+=qe.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:qe?qe[qe.length-1].length-qe[qe.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+Fe[0].length},this.yytext+=Fe[0],this.match+=Fe[0],this.matches=Fe,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(Fe[0].length),this.matched+=Fe[0],at=this.performAction.call(this,this.yy,this,He,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),at)return at;if(this._backtrack){for(var ye in Ue)this[ye]=Ue[ye];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var Fe,He,at,qe;this._more||(this.yytext="",this.match="");for(var Ue=this._currentRules(),ye=0;ye<Ue.length;ye++)if(at=this._input.match(this.rules[Ue[ye]]),at&&(!He||at[0].length>He[0].length)){if(He=at,qe=ye,this.options.backtrack_lexer){if(Fe=this.test_match(at,Ue[ye]),Fe!==!1)return Fe;if(this._backtrack){He=!1;continue}else return!1}else if(!this.options.flex)break}return He?(Fe=this.test_match(He,Ue[qe]),Fe!==!1?Fe:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var He=this.next();return He||this.lex()},"lex"),begin:o(function(He){this.conditionStack.push(He)},"begin"),popState:o(function(){var He=this.conditionStack.length-1;return He>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(He){return He=this.conditionStack.length-1-Math.abs(He||0),He>=0?this.conditionStack[He]:"INITIAL"},"topState"),pushState:o(function(He){this.begin(He)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(He,at,qe,Ue){var ye=Ue;switch(qe){case 0:return 6;case 1:return 7;case 2:return 8;case 3:return 9;case 4:return 22;case 5:return 23;case 6:return this.begin("acc_title"),24;break;case 7:return this.popState(),"acc_title_value";break;case 8:return this.begin("acc_descr"),26;break;case 9:return this.popState(),"acc_descr_value";break;case 10:this.begin("acc_descr_multiline");break;case 11:this.popState();break;case 12:return"acc_descr_multiline_value";case 13:break;case 14:c;break;case 15:return 12;case 16:break;case 17:return 11;case 18:return 15;case 19:return 16;case 20:return 17;case 21:return 18;case 22:return this.begin("person_ext"),45;break;case 23:return this.begin("person"),44;break;case 24:return this.begin("system_ext_queue"),51;break;case 25:return this.begin("system_ext_db"),50;break;case 26:return this.begin("system_ext"),49;break;case 27:return this.begin("system_queue"),48;break;case 28:return this.begin("system_db"),47;break;case 29:return this.begin("system"),46;break;case 30:return this.begin("boundary"),37;break;case 31:return this.begin("enterprise_boundary"),34;break;case 32:return this.begin("system_boundary"),36;break;case 33:return this.begin("container_ext_queue"),57;break;case 34:return this.begin("container_ext_db"),56;break;case 35:return this.begin("container_ext"),55;break;case 36:return this.begin("container_queue"),54;break;case 37:return this.begin("container_db"),53;break;case 38:return this.begin("container"),52;break;case 39:return this.begin("container_boundary"),38;break;case 40:return this.begin("component_ext_queue"),63;break;case 41:return this.begin("component_ext_db"),62;break;case 42:return this.begin("component_ext"),61;break;case 43:return this.begin("component_queue"),60;break;case 44:return this.begin("component_db"),59;break;case 45:return this.begin("component"),58;break;case 46:return this.begin("node"),39;break;case 47:return this.begin("node"),39;break;case 48:return this.begin("node_l"),40;break;case 49:return this.begin("node_r"),41;break;case 50:return this.begin("rel"),64;break;case 51:return this.begin("birel"),65;break;case 52:return this.begin("rel_u"),66;break;case 53:return this.begin("rel_u"),66;break;case 54:return this.begin("rel_d"),67;break;case 55:return this.begin("rel_d"),67;break;case 56:return this.begin("rel_l"),68;break;case 57:return this.begin("rel_l"),68;break;case 58:return this.begin("rel_r"),69;break;case 59:return this.begin("rel_r"),69;break;case 60:return this.begin("rel_b"),70;break;case 61:return this.begin("rel_index"),71;break;case 62:return this.begin("update_el_style"),72;break;case 63:return this.begin("update_rel_style"),73;break;case 64:return this.begin("update_layout_config"),74;break;case 65:return"EOF_IN_STRUCT";case 66:return this.begin("attribute"),"ATTRIBUTE_EMPTY";break;case 67:this.begin("attribute");break;case 68:this.popState(),this.popState();break;case 69:return 80;case 70:break;case 71:return 80;case 72:this.begin("string");break;case 73:this.popState();break;case 74:return"STR";case 75:this.begin("string_kv");break;case 76:return this.begin("string_kv_key"),"STR_KEY";break;case 77:this.popState(),this.begin("string_kv_value");break;case 78:return"STR_VALUE";case 79:this.popState(),this.popState();break;case 80:return"STR";case 81:return"LBRACE";case 82:return"RBRACE";case 83:return"SPACE";case 84:return"EOL";case 85:return 14}},"anonymous"),rules:[/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:title\s[^#\n;]+)/,/^(?:accDescription\s[^#\n;]+)/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:%%(?!\{)*[^\n]*(\r?\n?)+)/,/^(?:%%[^\n]*(\r?\n)*)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:C4Context\b)/,/^(?:C4Container\b)/,/^(?:C4Component\b)/,/^(?:C4Dynamic\b)/,/^(?:C4Deployment\b)/,/^(?:Person_Ext\b)/,/^(?:Person\b)/,/^(?:SystemQueue_Ext\b)/,/^(?:SystemDb_Ext\b)/,/^(?:System_Ext\b)/,/^(?:SystemQueue\b)/,/^(?:SystemDb\b)/,/^(?:System\b)/,/^(?:Boundary\b)/,/^(?:Enterprise_Boundary\b)/,/^(?:System_Boundary\b)/,/^(?:ContainerQueue_Ext\b)/,/^(?:ContainerDb_Ext\b)/,/^(?:Container_Ext\b)/,/^(?:ContainerQueue\b)/,/^(?:ContainerDb\b)/,/^(?:Container\b)/,/^(?:Container_Boundary\b)/,/^(?:ComponentQueue_Ext\b)/,/^(?:ComponentDb_Ext\b)/,/^(?:Component_Ext\b)/,/^(?:ComponentQueue\b)/,/^(?:ComponentDb\b)/,/^(?:Component\b)/,/^(?:Deployment_Node\b)/,/^(?:Node\b)/,/^(?:Node_L\b)/,/^(?:Node_R\b)/,/^(?:Rel\b)/,/^(?:BiRel\b)/,/^(?:Rel_Up\b)/,/^(?:Rel_U\b)/,/^(?:Rel_Down\b)/,/^(?:Rel_D\b)/,/^(?:Rel_Left\b)/,/^(?:Rel_L\b)/,/^(?:Rel_Right\b)/,/^(?:Rel_R\b)/,/^(?:Rel_Back\b)/,/^(?:RelIndex\b)/,/^(?:UpdateElementStyle\b)/,/^(?:UpdateRelStyle\b)/,/^(?:UpdateLayoutConfig\b)/,/^(?:$)/,/^(?:[(][ ]*[,])/,/^(?:[(])/,/^(?:[)])/,/^(?:,,)/,/^(?:,)/,/^(?:[ ]*["]["])/,/^(?:[ ]*["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:[ ]*[\$])/,/^(?:[^=]*)/,/^(?:[=][ ]*["])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:[^,]+)/,/^(?:\{)/,/^(?:\})/,/^(?:[\s]+)/,/^(?:[\n\r]+)/,/^(?:$)/],conditions:{acc_descr_multiline:{rules:[11,12],inclusive:!1},acc_descr:{rules:[9],inclusive:!1},acc_title:{rules:[7],inclusive:!1},string_kv_value:{rules:[78,79],inclusive:!1},string_kv_key:{rules:[77],inclusive:!1},string_kv:{rules:[76],inclusive:!1},string:{rules:[73,74],inclusive:!1},attribute:{rules:[68,69,70,71,72,75,80],inclusive:!1},update_layout_config:{rules:[65,66,67,68],inclusive:!1},update_rel_style:{rules:[65,66,67,68],inclusive:!1},update_el_style:{rules:[65,66,67,68],inclusive:!1},rel_b:{rules:[65,66,67,68],inclusive:!1},rel_r:{rules:[65,66,67,68],inclusive:!1},rel_l:{rules:[65,66,67,68],inclusive:!1},rel_d:{rules:[65,66,67,68],inclusive:!1},rel_u:{rules:[65,66,67,68],inclusive:!1},rel_bi:{rules:[],inclusive:!1},rel:{rules:[65,66,67,68],inclusive:!1},node_r:{rules:[65,66,67,68],inclusive:!1},node_l:{rules:[65,66,67,68],inclusive:!1},node:{rules:[65,66,67,68],inclusive:!1},index:{rules:[],inclusive:!1},rel_index:{rules:[65,66,67,68],inclusive:!1},component_ext_queue:{rules:[65,66,67,68],inclusive:!1},component_ext_db:{rules:[65,66,67,68],inclusive:!1},component_ext:{rules:[65,66,67,68],inclusive:!1},component_queue:{rules:[65,66,67,68],inclusive:!1},component_db:{rules:[65,66,67,68],inclusive:!1},component:{rules:[65,66,67,68],inclusive:!1},container_boundary:{rules:[65,66,67,68],inclusive:!1},container_ext_queue:{rules:[65,66,67,68],inclusive:!1},container_ext_db:{rules:[65,66,67,68],inclusive:!1},container_ext:{rules:[65,66,67,68],inclusive:!1},container_queue:{rules:[65,66,67,68],inclusive:!1},container_db:{rules:[65,66,67,68],inclusive:!1},container:{rules:[65,66,67,68],inclusive:!1},birel:{rules:[65,66,67,68],inclusive:!1},system_boundary:{rules:[65,66,67,68],inclusive:!1},enterprise_boundary:{rules:[65,66,67,68],inclusive:!1},boundary:{rules:[65,66,67,68],inclusive:!1},system_ext_queue:{rules:[65,66,67,68],inclusive:!1},system_ext_db:{rules:[65,66,67,68],inclusive:!1},system_ext:{rules:[65,66,67,68],inclusive:!1},system_queue:{rules:[65,66,67,68],inclusive:!1},system_db:{rules:[65,66,67,68],inclusive:!1},system:{rules:[65,66,67,68],inclusive:!1},person_ext:{rules:[65,66,67,68],inclusive:!1},person:{rules:[65,66,67,68],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,8,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,81,82,83,84,85],inclusive:!0}}};return ke})();$e.lexer=de;function rt(){this.yy={}}return o(rt,"Parser"),rt.prototype=$e,$e.Parser=rt,new rt})();kv.parser=kv;eX=kv});var uOe,hOe,Br,Io,Di=z(()=>{"use strict";St();uOe=o(function(e,t){for(let r of t)e.attr(r[0],r[1])},"d3Attrs"),hOe=o(function(e,t,r){let i=new Map;return r?(i.set("width","100%"),i.set("style",`max-width: ${t}px;`)):(i.set("height",e),i.set("width",t)),i},"calculateSvgSizeAttrs"),Br=o(function(e,t,r,i){let n=hOe(t,r,i);uOe(e,n)},"configureSvgSize"),Io=o(function(e,t,r,i){let n=t.node().getBBox(),a=n.width,s=n.height;Z.info(`SVG bounds: ${a}x${s}`,n);let l=0,u=0;Z.info(`Graph bounds: ${l}x${u}`,e),l=a+r*2,u=s+r*2,Z.info(`Calculated bounds: ${l}x${u}`),Br(t,u,l,i);let h=`${n.x-r} ${n.y-r} ${n.width+2*r} ${n.height+2*r}`;t.attr("viewBox",h)},"setupGraphViewbox")});function ID(e){return[...e.cssRules].map(t=>t.cssText).join(`
+`)}var gk,dOe,tX,rX,OD=z(()=>{"use strict";St();gk={};o(ID,"cssStyleSheetToString");dOe=o((e,t,r,i)=>{let n="";return e in gk&&gk[e]?n=gk[e]({...r,svgId:i}):Z.warn(`No theme found for ${e}`),` & {
+    font-family: ${r.fontFamily};
+    font-size: ${r.fontSize};
+    fill: ${r.textColor}
+  }
+  @keyframes edge-animation-frame {
+    from {
+      stroke-dashoffset: 0;
+    }
+  }
+  @keyframes dash {
+    to {
+      stroke-dashoffset: 0;
+    }
+  }
+  & .edge-animation-slow {
+    stroke-dasharray: 9,5 !important;
+    stroke-dashoffset: 900;
+    animation: dash 50s linear infinite;
+    stroke-linecap: round;
+  }
+  & .edge-animation-fast {
+    stroke-dasharray: 9,5 !important;
+    stroke-dashoffset: 900;
+    animation: dash 20s linear infinite;
+    stroke-linecap: round;
+  }
+  /* Classes common for multiple diagrams */
+
+  & .error-icon {
+    fill: ${r.errorBkgColor};
+  }
+  & .error-text {
+    fill: ${r.errorTextColor};
+    stroke: ${r.errorTextColor};
+  }
+
+  & .edge-thickness-normal {
+    stroke-width: ${r.strokeWidth??1}px;
+  }
+  & .edge-thickness-thick {
+    stroke-width: 3.5px
+  }
+  & .edge-pattern-solid {
+    stroke-dasharray: 0;
+  }
+  & .edge-thickness-invisible {
+    stroke-width: 0;
+    fill: none;
+  }
+  & .edge-pattern-dashed{
+    stroke-dasharray: 3;
+  }
+  .edge-pattern-dotted {
+    stroke-dasharray: 2;
+  }
+
+  & .marker {
+    fill: ${r.lineColor};
+    stroke: ${r.lineColor};
+  }
+  & .marker.cross {
+    stroke: ${r.lineColor};
+  }
+
+  & svg {
+    font-family: ${r.fontFamily};
+    font-size: ${r.fontSize};
+  }
+   & p {
+    margin: 0
+   }
+
+  ${n}
+  .node .neo-node {
+    stroke: ${r.nodeBorder};
+  }
+
+  [data-look="neo"].node rect, [data-look="neo"].cluster rect, [data-look="neo"].node polygon {
+    stroke: ${r.useGradient?"url("+i+"-gradient)":r.nodeBorder};
+    filter: ${r.dropShadow?r.dropShadow.replace("url(#drop-shadow)",`url(${i}-drop-shadow)`):"none"};
+  }
+
+
+  [data-look="neo"].node path {
+    stroke: ${r.useGradient?"url("+i+"-gradient)":r.nodeBorder};
+    stroke-width: ${r.strokeWidth??1}px;
+  }
+
+  [data-look="neo"].node .outer-path {
+    filter: ${r.dropShadow?r.dropShadow.replace("url(#drop-shadow)",`url(${i}-drop-shadow)`):"none"};
+  }
+
+  [data-look="neo"].node .neo-line path {
+    stroke: ${r.nodeBorder};
+    filter: none;
+  }
+
+  [data-look="neo"].node circle{
+    stroke: ${r.useGradient?"url("+i+"-gradient)":r.nodeBorder};
+    filter: ${r.dropShadow?r.dropShadow.replace("url(#drop-shadow)",`url(${i}-drop-shadow)`):"none"};
+  }
+
+  [data-look="neo"].node circle .state-start{
+    fill: #000000;
+  }
+
+  [data-look="neo"].icon-shape .icon {
+    fill: ${r.useGradient?"url("+i+"-gradient)":r.nodeBorder};
+    filter: ${r.dropShadow?r.dropShadow.replace("url(#drop-shadow)",`url(${i}-drop-shadow)`):"none"};
+  }
+
+    [data-look="neo"].icon-shape .icon-neo path {
+    stroke: ${r.useGradient?"url("+i+"-gradient)":r.nodeBorder};
+    filter: ${r.dropShadow?r.dropShadow.replace("url(#drop-shadow)",`url(${i}-drop-shadow)`):"none"};
+  }
+
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+`,e=e.substring(k.length+1),f=E.slice(g)}}n.loose||(s?n.loose=!0:this.rules.other.doubleBlankLine.test(h)&&(s=!0));let y=null,v;this.options.gfm&&(y=this.rules.other.listIsTask.exec(d),y&&(v=y[0]!=="[ ] ",d=d.replace(this.rules.other.listReplaceTask,""))),n.items.push({type:"list_item",raw:h,task:!!y,checked:v,loose:!1,text:d,tokens:[]}),n.raw+=h}let l=n.items.at(-1);if(l)l.raw=l.raw.trimEnd(),l.text=l.text.trimEnd();else return;n.raw=n.raw.trimEnd();for(let u=0;u<n.items.length;u++)if(this.lexer.state.top=!1,n.items[u].tokens=this.lexer.blockTokens(n.items[u].text,[]),!n.loose){let h=n.items[u].tokens.filter(f=>f.type==="space"),d=h.length>0&&h.some(f=>this.rules.other.anyLine.test(f.raw));n.loose=d}if(n.loose)for(let u=0;u<n.items.length;u++)n.items[u].loose=!0;return n}}html(e){let t=this.rules.block.html.exec(e);if(t)return{type:"html",block:!0,raw:t[0],pre:t[1]==="pre"||t[1]==="script"||t[1]==="style",text:t[0]}}def(e){let t=this.rules.block.def.exec(e);if(t){let r=t[1].toLowerCase().replace(this.rules.other.multipleSpaceGlobal," "),i=t[2]?t[2].replace(this.rules.other.hrefBrackets,"$1").replace(this.rules.inline.anyPunctuation,"$1"):"",n=t[3]?t[3].substring(1,t[3].length-1).replace(this.rules.inline.anyPunctuation,"$1"):t[3];return{type:"def",tag:r,raw:t[0],href:i,title:n}}}table(e){let t=this.rules.block.table.exec(e);if(!t||!this.rules.other.tableDelimiter.test(t[2]))return;let r=rre(t[1]),i=t[2].replace(this.rules.other.tableAlignChars,"").split("|"),n=t[3]?.trim()?t[3].replace(this.rules.other.tableRowBlankLine,"").split(`
+`):[],a={type:"table",raw:t[0],header:[],align:[],rows:[]};if(r.length===i.length){for(let s of i)this.rules.other.tableAlignRight.test(s)?a.align.push("right"):this.rules.other.tableAlignCenter.test(s)?a.align.push("center"):this.rules.other.tableAlignLeft.test(s)?a.align.push("left"):a.align.push(null);for(let s=0;s<r.length;s++)a.header.push({text:r[s],tokens:this.lexer.inline(r[s]),header:!0,align:a.align[s]});for(let s of n)a.rows.push(rre(s,a.header.length).map((l,u)=>({text:l,tokens:this.lexer.inline(l),header:!1,align:a.align[u]})));return a}}lheading(e){let t=this.rules.block.lheading.exec(e);if(t)return{type:"heading",raw:t[0],depth:t[2].charAt(0)==="="?1:2,text:t[1],tokens:this.lexer.inline(t[1])}}paragraph(e){let t=this.rules.block.paragraph.exec(e);if(t){let r=t[1].charAt(t[1].length-1)===`
+`?t[1].slice(0,-1):t[1];return{type:"paragraph",raw:t[0],text:r,tokens:this.lexer.inline(r)}}}text(e){let t=this.rules.block.text.exec(e);if(t)return{type:"text",raw:t[0],text:t[0],tokens:this.lexer.inline(t[0])}}escape(e){let t=this.rules.inline.escape.exec(e);if(t)return{type:"escape",raw:t[0],text:t[1]}}tag(e){let t=this.rules.inline.tag.exec(e);if(t)return!this.lexer.state.inLink&&this.rules.other.startATag.test(t[0])?this.lexer.state.inLink=!0:this.lexer.state.inLink&&this.rules.other.endATag.test(t[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&this.rules.other.startPreScriptTag.test(t[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&this.rules.other.endPreScriptTag.test(t[0])&&(this.lexer.state.inRawBlock=!1),{type:"html",raw:t[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,block:!1,text:t[0]}}link(e){let t=this.rules.inline.link.exec(e);if(t){let r=t[2].trim();if(!this.options.pedantic&&this.rules.other.startAngleBracket.test(r)){if(!this.rules.other.endAngleBracket.test(r))return;let a=Ux(r.slice(0,-1),"\\");if((r.length-a.length)%2===0)return}else{let a=KWe(t[2],"()");if(a===-2)return;if(a>-1){let s=(t[0].indexOf("!")===0?5:4)+t[1].length+a;t[2]=t[2].substring(0,a),t[0]=t[0].substring(0,s).trim(),t[3]=""}}let i=t[2],n="";if(this.options.pedantic){let a=this.rules.other.pedanticHrefTitle.exec(i);a&&(i=a[1],n=a[3])}else n=t[3]?t[3].slice(1,-1):"";return i=i.trim(),this.rules.other.startAngleBracket.test(i)&&(this.options.pedantic&&!this.rules.other.endAngleBracket.test(r)?i=i.slice(1):i=i.slice(1,-1)),ire(t,{href:i&&i.replace(this.rules.inline.anyPunctuation,"$1"),title:n&&n.replace(this.rules.inline.anyPunctuation,"$1")},t[0],this.lexer,this.rules)}}reflink(e,t){let r;if((r=this.rules.inline.reflink.exec(e))||(r=this.rules.inline.nolink.exec(e))){let i=(r[2]||r[1]).replace(this.rules.other.multipleSpaceGlobal," "),n=t[i.toLowerCase()];if(!n){let a=r[0].charAt(0);return{type:"text",raw:a,text:a}}return ire(r,n,r[0],this.lexer,this.rules)}}emStrong(e,t,r=""){let i=this.rules.inline.emStrongLDelim.exec(e);if(!(!i||i[3]&&r.match(this.rules.other.unicodeAlphaNumeric))&&(!(i[1]||i[2])||!r||this.rules.inline.punctuation.exec(r))){let n=[...i[0]].length-1,a,s,l=n,u=0,h=i[0][0]==="*"?this.rules.inline.emStrongRDelimAst:this.rules.inline.emStrongRDelimUnd;for(h.lastIndex=0,t=t.slice(-1*e.length+n);(i=h.exec(t))!=null;){if(a=i[1]||i[2]||i[3]||i[4]||i[5]||i[6],!a)continue;if(s=[...a].length,i[3]||i[4]){l+=s;continue}else if((i[5]||i[6])&&n%3&&!((n+s)%3)){u+=s;continue}if(l-=s,l>0)continue;s=Math.min(s,s+l+u);let d=[...i[0]][0].length,f=e.slice(0,n+i.index+d+s);if(Math.min(n,s)%2){let m=f.slice(1,-1);return{type:"em",raw:f,text:m,tokens:this.lexer.inlineTokens(m)}}let p=f.slice(2,-2);return{type:"strong",raw:f,text:p,tokens:this.lexer.inlineTokens(p)}}}}codespan(e){let t=this.rules.inline.code.exec(e);if(t){let r=t[2].replace(this.rules.other.newLineCharGlobal," "),i=this.rules.other.nonSpaceChar.test(r),n=this.rules.other.startingSpaceChar.test(r)&&this.rules.other.endingSpaceChar.test(r);return i&&n&&(r=r.substring(1,r.length-1)),{type:"codespan",raw:t[0],text:r}}}br(e){let t=this.rules.inline.br.exec(e);if(t)return{type:"br",raw:t[0]}}del(e){let t=this.rules.inline.del.exec(e);if(t)return{type:"del",raw:t[0],text:t[2],tokens:this.lexer.inlineTokens(t[2])}}autolink(e){let t=this.rules.inline.autolink.exec(e);if(t){let r,i;return t[2]==="@"?(r=t[1],i="mailto:"+r):(r=t[1],i=r),{type:"link",raw:t[0],text:r,href:i,tokens:[{type:"text",raw:r,text:r}]}}}url(e){let t;if(t=this.rules.inline.url.exec(e)){let r,i;if(t[2]==="@")r=t[0],i="mailto:"+r;else{let n;do n=t[0],t[0]=this.rules.inline._backpedal.exec(t[0])?.[0]??"";while(n!==t[0]);r=t[0],t[1]==="www."?i="http://"+t[0]:i=t[0]}return{type:"link",raw:t[0],text:r,href:i,tokens:[{type:"text",raw:r,text:r}]}}}inlineText(e){let t=this.rules.inline.text.exec(e);if(t){let r=this.lexer.state.inRawBlock;return{type:"text",raw:t[0],text:t[0],escaped:r}}}},Pu=class XM{static{o(this,"l")}tokens;options;state;tokenizer;inlineQueue;constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||Sp,this.options.tokenizer=this.options.tokenizer||new C4,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};let r={other:Ja,block:b4.normal,inline:qx.normal};this.options.pedantic?(r.block=b4.pedantic,r.inline=qx.pedantic):this.options.gfm&&(r.block=b4.gfm,this.options.breaks?r.inline=qx.breaks:r.inline=qx.gfm),this.tokenizer.rules=r}static get rules(){return{block:b4,inline:qx}}static lex(t,r){return new XM(r).lex(t)}static lexInline(t,r){return new XM(r).inlineTokens(t)}lex(t){t=t.replace(Ja.carriageReturn,`
+`),this.blockTokens(t,this.tokens);for(let r=0;r<this.inlineQueue.length;r++){let i=this.inlineQueue[r];this.inlineTokens(i.src,i.tokens)}return this.inlineQueue=[],this.tokens}blockTokens(t,r=[],i=!1){for(this.options.pedantic&&(t=t.replace(Ja.tabCharGlobal,"    ").replace(Ja.spaceLine,""));t;){let n;if(this.options.extensions?.block?.some(s=>(n=s.call({lexer:this},t,r))?(t=t.substring(n.raw.length),r.push(n),!0):!1))continue;if(n=this.tokenizer.space(t)){t=t.substring(n.raw.length);let s=r.at(-1);n.raw.length===1&&s!==void 0?s.raw+=`
+`:r.push(n);continue}if(n=this.tokenizer.code(t)){t=t.substring(n.raw.length);let s=r.at(-1);s?.type==="paragraph"||s?.type==="text"?(s.raw+=(s.raw.endsWith(`
+`)?"":`
+`)+n.raw,s.text+=`
+`+n.text,this.inlineQueue.at(-1).src=s.text):r.push(n);continue}if(n=this.tokenizer.fences(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.heading(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.hr(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.blockquote(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.list(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.html(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.def(t)){t=t.substring(n.raw.length);let s=r.at(-1);s?.type==="paragraph"||s?.type==="text"?(s.raw+=(s.raw.endsWith(`
+`)?"":`
+`)+n.raw,s.text+=`
+`+n.raw,this.inlineQueue.at(-1).src=s.text):this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title},r.push(n));continue}if(n=this.tokenizer.table(t)){t=t.substring(n.raw.length),r.push(n);continue}if(n=this.tokenizer.lheading(t)){t=t.substring(n.raw.length),r.push(n);continue}let a=t;if(this.options.extensions?.startBlock){let s=1/0,l=t.slice(1),u;this.options.extensions.startBlock.forEach(h=>{u=h.call({lexer:this},l),typeof u=="number"&&u>=0&&(s=Math.min(s,u))}),s<1/0&&s>=0&&(a=t.substring(0,s+1))}if(this.state.top&&(n=this.tokenizer.paragraph(a))){let s=r.at(-1);i&&s?.type==="paragraph"?(s.raw+=(s.raw.endsWith(`
+`)?"":`
+`)+n.raw,s.text+=`
+`+n.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=s.text):r.push(n),i=a.length!==t.length,t=t.substring(n.raw.length);continue}if(n=this.tokenizer.text(t)){t=t.substring(n.raw.length);let s=r.at(-1);s?.type==="text"?(s.raw+=(s.raw.endsWith(`
+`)?"":`
+`)+n.raw,s.text+=`
+`+n.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=s.text):r.push(n);continue}if(t){let s="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(s);break}else throw new Error(s)}}return this.state.top=!0,r}inline(t,r=[]){return this.inlineQueue.push({src:t,tokens:r}),r}inlineTokens(t,r=[]){let i=t,n=null;if(this.tokens.links){let l=Object.keys(this.tokens.links);if(l.length>0)for(;(n=this.tokenizer.rules.inline.reflinkSearch.exec(i))!=null;)l.includes(n[0].slice(n[0].lastIndexOf("[")+1,-1))&&(i=i.slice(0,n.index)+"["+"a".repeat(n[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(n=this.tokenizer.rules.inline.anyPunctuation.exec(i))!=null;)i=i.slice(0,n.index)+"++"+i.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);for(;(n=this.tokenizer.rules.inline.blockSkip.exec(i))!=null;)i=i.slice(0,n.index)+"["+"a".repeat(n[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);i=this.options.hooks?.emStrongMask?.call({lexer:this},i)??i;let a=!1,s="";for(;t;){a||(s=""),a=!1;let l;if(this.options.extensions?.inline?.some(h=>(l=h.call({lexer:this},t,r))?(t=t.substring(l.raw.length),r.push(l),!0):!1))continue;if(l=this.tokenizer.escape(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.tag(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.link(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.reflink(t,this.tokens.links)){t=t.substring(l.raw.length);let h=r.at(-1);l.type==="text"&&h?.type==="text"?(h.raw+=l.raw,h.text+=l.text):r.push(l);continue}if(l=this.tokenizer.emStrong(t,i,s)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.codespan(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.br(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.del(t)){t=t.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.autolink(t)){t=t.substring(l.raw.length),r.push(l);continue}if(!this.state.inLink&&(l=this.tokenizer.url(t))){t=t.substring(l.raw.length),r.push(l);continue}let u=t;if(this.options.extensions?.startInline){let h=1/0,d=t.slice(1),f;this.options.extensions.startInline.forEach(p=>{f=p.call({lexer:this},d),typeof f=="number"&&f>=0&&(h=Math.min(h,f))}),h<1/0&&h>=0&&(u=t.substring(0,h+1))}if(l=this.tokenizer.inlineText(u)){t=t.substring(l.raw.length),l.raw.slice(-1)!=="_"&&(s=l.raw.slice(-1)),a=!0;let h=r.at(-1);h?.type==="text"?(h.raw+=l.raw,h.text+=l.text):r.push(l);continue}if(t){let h="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(h);break}else throw new Error(h)}}return r}},k4=class{static{o(this,"P")}options;parser;constructor(e){this.options=e||Sp}space(e){return""}code({text:e,lang:t,escaped:r}){let i=(t||"").match(Ja.notSpaceStart)?.[0],n=e.replace(Ja.endingNewline,"")+`
+`;return i?'<pre><code class="language-'+Cc(i)+'">'+(r?n:Cc(n,!0))+`</code></pre>
+`:"<pre><code>"+(r?n:Cc(n,!0))+`</code></pre>
+`}blockquote({tokens:e}){return`<blockquote>
+${this.parser.parse(e)}</blockquote>
+`}html({text:e}){return e}def(e){return""}heading({tokens:e,depth:t}){return`<h${t}>${this.parser.parseInline(e)}</h${t}>
+`}hr(e){return`<hr>
+`}list(e){let t=e.ordered,r=e.start,i="";for(let s=0;s<e.items.length;s++){let l=e.items[s];i+=this.listitem(l)}let n=t?"ol":"ul",a=t&&r!==1?' start="'+r+'"':"";return"<"+n+a+`>
+`+i+"</"+n+`>
+`}listitem(e){let t="";if(e.task){let r=this.checkbox({checked:!!e.checked});e.loose?e.tokens[0]?.type==="paragraph"?(e.tokens[0].text=r+" "+e.tokens[0].text,e.tokens[0].tokens&&e.tokens[0].tokens.length>0&&e.tokens[0].tokens[0].type==="text"&&(e.tokens[0].tokens[0].text=r+" "+Cc(e.tokens[0].tokens[0].text),e.tokens[0].tokens[0].escaped=!0)):e.tokens.unshift({type:"text",raw:r+" ",text:r+" ",escaped:!0}):t+=r+" "}return t+=this.parser.parse(e.tokens,!!e.loose),`<li>${t}</li>
+`}checkbox({checked:e}){return"<input "+(e?'checked="" ':"")+'disabled="" type="checkbox">'}paragraph({tokens:e}){return`<p>${this.parser.parseInline(e)}</p>
+`}table(e){let t="",r="";for(let n=0;n<e.header.length;n++)r+=this.tablecell(e.header[n]);t+=this.tablerow({text:r});let i="";for(let n=0;n<e.rows.length;n++){let a=e.rows[n];r="";for(let s=0;s<a.length;s++)r+=this.tablecell(a[s]);i+=this.tablerow({text:r})}return i&&(i=`<tbody>${i}</tbody>`),`<table>
+<thead>
+`+t+`</thead>
+`+i+`</table>
+`}tablerow({text:e}){return`<tr>
+${e}</tr>
+`}tablecell(e){let t=this.parser.parseInline(e.tokens),r=e.header?"th":"td";return(e.align?`<${r} align="${e.align}">`:`<${r}>`)+t+`</${r}>
+`}strong({tokens:e}){return`<strong>${this.parser.parseInline(e)}</strong>`}em({tokens:e}){return`<em>${this.parser.parseInline(e)}</em>`}codespan({text:e}){return`<code>${Cc(e,!0)}</code>`}br(e){return"<br>"}del({tokens:e}){return`<del>${this.parser.parseInline(e)}</del>`}link({href:e,title:t,tokens:r}){let i=this.parser.parseInline(r),n=tre(e);if(n===null)return i;e=n;let a='<a href="'+e+'"';return t&&(a+=' title="'+Cc(t)+'"'),a+=">"+i+"</a>",a}image({href:e,title:t,text:r,tokens:i}){i&&(r=this.parser.parseInline(i,this.parser.textRenderer));let n=tre(e);if(n===null)return Cc(r);e=n;let a=`<img src="${e}" alt="${r}"`;return t&&(a+=` title="${Cc(t)}"`),a+=">",a}text(e){return"tokens"in e&&e.tokens?this.parser.parseInline(e.tokens):"escaped"in e&&e.escaped?e.text:Cc(e.text)}},aN=class{static{o(this,"$")}strong({text:e}){return e}em({text:e}){return e}codespan({text:e}){return e}del({text:e}){return e}html({text:e}){return e}text({text:e}){return e}link({text:e}){return""+e}image({text:e}){return""+e}br(){return""}},Bu=class KM{static{o(this,"l")}options;renderer;textRenderer;constructor(t){this.options=t||Sp,this.options.renderer=this.options.renderer||new k4,this.renderer=this.options.renderer,this.renderer.options=this.options,this.renderer.parser=this,this.textRenderer=new aN}static parse(t,r){return new KM(r).parse(t)}static parseInline(t,r){return new KM(r).parseInline(t)}parse(t,r=!0){let i="";for(let n=0;n<t.length;n++){let a=t[n];if(this.options.extensions?.renderers?.[a.type]){let l=a,u=this.options.extensions.renderers[l.type].call({parser:this},l);if(u!==!1||!["space","hr","heading","code","table","blockquote","list","html","def","paragraph","text"].includes(l.type)){i+=u||"";continue}}let s=a;switch(s.type){case"space":{i+=this.renderer.space(s);continue}case"hr":{i+=this.renderer.hr(s);continue}case"heading":{i+=this.renderer.heading(s);continue}case"code":{i+=this.renderer.code(s);continue}case"table":{i+=this.renderer.table(s);continue}case"blockquote":{i+=this.renderer.blockquote(s);continue}case"list":{i+=this.renderer.list(s);continue}case"html":{i+=this.renderer.html(s);continue}case"def":{i+=this.renderer.def(s);continue}case"paragraph":{i+=this.renderer.paragraph(s);continue}case"text":{let l=s,u=this.renderer.text(l);for(;n+1<t.length&&t[n+1].type==="text";)l=t[++n],u+=`
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+Initialize mermaid with maxEdges set to a higher number to allow more edges.
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p=Math.cos(-f),m=Math.sin(-f),g=0;g<d.length/2;g++)d[g*2]=s/2*(i[g*2]*p-i[g*2+1]*m),d[g*2+1]=l/2*(i[g*2+1]*p+i[g*2]*m),d[g*2]+=n,d[g*2+1]+=a;var y;if(h>0){var v=F3(d,-h);y=B3(v)}else y=d;return Bs(t,r,y)},"pointInsidePolygon"),jrt=o(function(t,r,i,n,a,s,l,u){for(var h=new Array(i.length*2),d=0;d<u.length;d++){var f=u[d];h[d*4+0]=f.startX,h[d*4+1]=f.startY,h[d*4+2]=f.stopX,h[d*4+3]=f.stopY;var p=Math.pow(f.cx-t,2)+Math.pow(f.cy-r,2);if(p<=Math.pow(f.radius,2))return!0}return Bs(t,r,h)},"pointInsideRoundPolygon"),B3=o(function(t){for(var r=new Array(t.length/2),i,n,a,s,l,u,h,d,f=0;f<t.length/4;f++){i=t[f*4],n=t[f*4+1],a=t[f*4+2],s=t[f*4+3],f<t.length/4-1?(l=t[(f+1)*4],u=t[(f+1)*4+1],h=t[(f+1)*4+2],d=t[(f+1)*4+3]):(l=t[0],u=t[1],h=t[2],d=t[3]);var p=Gd(i,n,a,s,l,u,h,d,!0);r[f*2]=p[0],r[f*2+1]=p[1]}return r},"joinLines"),F3=o(function(t,r){for(var i=new Array(t.length*2),n,a,s,l,u=0;u<t.length/2;u++){n=t[u*2],a=t[u*2+1],u<t.length/2-1?(s=t[(u+1)*2],l=t[(u+1)*2+1]):(s=t[0],l=t[1]);var 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h},"polygonIntersectLine"),Zrt=o(function(t,r,i,n,a,s,l,u,h){var d=[],f,p=new Array(i.length*2);h.forEach(function(b,T){T===0?(p[p.length-2]=b.startX,p[p.length-1]=b.startY):(p[T*4-2]=b.startX,p[T*4-1]=b.startY),p[T*4]=b.stopX,p[T*4+1]=b.stopY,f=zb(t,r,n,a,b.cx,b.cy,b.radius),f.length!==0&&d.push(f[0],f[1])});for(var m=0;m<p.length/4;m++)f=Gd(t,r,n,a,p[m*4],p[m*4+1],p[m*4+2],p[m*4+3],!1),f.length!==0&&d.push(f[0],f[1]);if(d.length>2){for(var g=[d[0],d[1]],y=Math.pow(g[0]-t,2)+Math.pow(g[1]-r,2),v=1;v<d.length/2;v++){var x=Math.pow(d[v*2]-t,2)+Math.pow(d[v*2+1]-r,2);x<=y&&(g[0]=d[v*2],g[1]=d[v*2+1],y=x)}return g}return d},"roundPolygonIntersectLine"),d3=o(function(t,r,i){var n=[t[0]-r[0],t[1]-r[1]],a=Math.sqrt(n[0]*n[0]+n[1]*n[1]),s=(a-i)/a;return s<0&&(s=1e-5),[r[0]+s*n[0],r[1]+s*n[1]]},"shortenIntersection"),rs=o(function(t,r){var i=UO(t,r);return i=fme(i),i},"generateUnitNgonPointsFitToSquare"),fme=o(function(t){for(var r,i,n=t.length/2,a=1/0,s=1/0,l=-1/0,u=-1/0,h=0;h<n;h++)r=t[2*h],i=t[2*h+1],a=Math.min(a,r),l=Math.max(l,r),s=Math.min(s,i),u=Math.max(u,i);for(var d=2/(l-a),f=2/(u-s),p=0;p<n;p++)r=t[2*p]=t[2*p]*d,i=t[2*p+1]=t[2*p+1]*f,a=Math.min(a,r),l=Math.max(l,r),s=Math.min(s,i),u=Math.max(u,i);if(s<-1)for(var m=0;m<n;m++)i=t[2*m+1]=t[2*m+1]+(-1-s);return t},"fitPolygonToSquare"),UO=o(function(t,r){var i=1/t*2*Math.PI,n=t%2===0?Math.PI/2+i/2:Math.PI/2;n+=r;for(var a=new Array(t*2),s,l=0;l<t;l++)s=l*i+n,a[2*l]=Math.cos(s),a[2*l+1]=Math.sin(-s);return a},"generateUnitNgonPoints"),Hd=o(function(t,r){return Math.min(t/4,r/4,8)},"getRoundRectangleRadius"),pme=o(function(t,r){return Math.min(t/10,r/10,8)},"getRoundPolygonRadius"),bP=o(function(){return 8},"getCutRectangleCornerLength"),Qrt=o(function(t,r,i){return[t-2*r+i,2*(r-t),t]},"bezierPtsToQuadCoeff"),HO=o(function(t,r){return{heightOffset:Math.min(15,.05*r),widthOffset:Math.min(100,.25*t),ctrlPtOffsetPct:.05}},"getBarrelCurveConstants");o(N9,"satPolygonIntersection");Jrt=va({dampingFactor:.8,precision:1e-6,iterations:200,weight:o(function(t){return 1},"weight")}),eit={pageRank:o(function(t){for(var r=Jrt(t),i=r.dampingFactor,n=r.precision,a=r.iterations,s=r.weight,l=this._private.cy,u=this.byGroup(),h=u.nodes,d=u.edges,f=h.length,p=f*f,m=d.length,g=new Array(p),y=new Array(f),v=(1-i)/f,x=0;x<f;x++){for(var b=0;b<f;b++){var T=x*f+b;g[T]=0}y[x]=0}for(var w=0;w<m;w++){var C=d[w],k=C.data("source"),E=C.data("target");if(k!==E){var A=h.indexOfId(k),N=h.indexOfId(E),P=s(C),I=N*f+A;g[I]+=P,y[A]+=P}}for(var D=1/f+v,_=0;_<f;_++)if(y[_]===0)for(var R=0;R<f;R++){var M=R*f+_;g[M]=D}else for(var L=0;L<f;L++){var B=L*f+_;g[B]=g[B]/y[_]+v}for(var O=new Array(f),$=new 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g=m[0],y=m[1];h.styleEnabled&&r.style().append(g),p(y,function(){r.startAnimationLoop(),h.ready=!0,Ji(l.ready)&&r.on("ready",l.ready);for(var v=0;v<a.length;v++){var x=a[v];r.on("ready",x)}n&&(n.readies=[]),r.emit("ready")},l.done)})},"Core"),G3=e2.prototype;hr(G3,{instanceString:o(function(){return"core"},"instanceString"),isReady:o(function(){return this._private.ready},"isReady"),destroyed:o(function(){return this._private.destroyed},"destroyed"),ready:o(function(t){return this.isReady()?this.emitter().emit("ready",[],t):this.on("ready",t),this},"ready"),destroy:o(function(){var t=this;if(!t.destroyed())return t.stopAnimationLoop(),t.destroyRenderer(),this.emit("destroy"),t._private.destroyed=!0,t},"destroy"),hasElementWithId:o(function(t){return this._private.elements.hasElementWithId(t)},"hasElementWithId"),getElementById:o(function(t){return this._private.elements.getElementById(t)},"getElementById"),hasCompoundNodes:o(function(){return this._private.hasCompoundNodes},"hasCompoundNodes"),headless:o(function(){return this._private.renderer.isHeadless()},"headless"),styleEnabled:o(function(){return this._private.styleEnabled},"styleEnabled"),addToPool:o(function(t){return this._private.elements.merge(t),this},"addToPool"),removeFromPool:o(function(t){return this._private.elements.unmerge(t),this},"removeFromPool"),container:o(function(){return this._private.container||null},"container"),window:o(function(){var t=this._private.container;if(t==null)return Gn;var r=this._private.container.ownerDocument;return r===void 0||r==null?Gn:r.defaultView||Gn},"window"),mount:o(function(t){if(t!=null){var r=this,i=r._private,n=i.options;return!I3(t)&&I3(t[0])&&(t=t[0]),r.stopAnimationLoop(),r.destroyRenderer(),i.container=t,i.styleEnabled=!0,r.invalidateSize(),r.initRenderer(hr({},n,n.renderer,{name:n.renderer.name==="null"?"canvas":n.renderer.name})),r.startAnimationLoop(),r.style(n.style),r.emit("mount"),r}},"mount"),unmount:o(function(){var t=this;return t.stopAnimationLoop(),t.destroyRenderer(),t.initRenderer({name:"null"}),t.emit("unmount"),t},"unmount"),options:o(function(){return Bc(this._private.options)},"options"),json:o(function(t){var r=this,i=r._private,n=r.mutableElements(),a=o(function(w){return r.getElementById(w.id())},"getFreshRef");if(Xr(t)){if(r.startBatch(),t.elements){var s={},l=o(function(w,C){for(var k=[],E=[],A=0;A<w.length;A++){var N=w[A];if(!N.data.id){di("cy.json() cannot handle elements without an ID attribute");continue}var P=""+N.data.id,I=r.getElementById(P);s[P]=!0,I.length!==0?E.push({ele:I,json:N}):(C&&(N.group=C),k.push(N))}r.add(k);for(var D=0;D<E.length;D++){var _=E[D],R=_.ele,M=_.json;R.json(M)}},"updateEles");if(Si(t.elements))l(t.elements);else for(var u=["nodes","edges"],h=0;h<u.length;h++){var d=u[h],f=t.elements[d];Si(f)&&l(f,d)}var p=r.collection();n.filter(function(T){return!s[T.id()]}).forEach(function(T){T.isParent()?p.merge(T):T.remove()}),p.forEach(function(T){return T.children().move({parent:null})}),p.forEach(function(T){return a(T).remove()})}t.style&&r.style(t.style),t.zoom!=null&&t.zoom!==i.zoom&&r.zoom(t.zoom),t.pan&&(t.pan.x!==i.pan.x||t.pan.y!==i.pan.y)&&r.pan(t.pan),t.data&&r.data(t.data);for(var m=["minZoom","maxZoom","zoomingEnabled","userZoomingEnabled","panningEnabled","userPanningEnabled","boxSelectionEnabled","autolock","autoungrabify","autounselectify","multiClickDebounceTime"],g=0;g<m.length;g++){var y=m[g];t[y]!=null&&r[y](t[y])}return r.endBatch(),this}else{var v=!!t,x={};v?x.elements=this.elements().map(function(T){return T.json()}):(x.elements={},n.forEach(function(T){var w=T.group();x.elements[w]||(x.elements[w]=[]),x.elements[w].push(T.json())})),this._private.styleEnabled&&(x.style=r.style().json()),x.data=Bc(r.data());var b=i.options;return x.zoomingEnabled=i.zoomingEnabled,x.userZoomingEnabled=i.userZoomingEnabled,x.zoom=i.zoom,x.minZoom=i.minZoom,x.maxZoom=i.maxZoom,x.panningEnabled=i.panningEnabled,x.userPanningEnabled=i.userPanningEnabled,x.pan=Bc(i.pan),x.boxSelectionEnabled=i.boxSelectionEnabled,x.renderer=Bc(b.renderer),x.hideEdgesOnViewport=b.hideEdgesOnViewport,x.textureOnViewport=b.textureOnViewport,x.wheelSensitivity=b.wheelSensitivity,x.motionBlur=b.motionBlur,x.multiClickDebounceTime=b.multiClickDebounceTime,x}},"json")});G3.$id=G3.getElementById;[Aat,Mat,jme,JO,_3,Iat,eP,D3,Bat,r0,Jb].forEach(function(e){hr(G3,e)});$at={fit:!0,directed:!1,direction:"downward",padding:30,circle:!1,grid:!1,spacingFactor:1.75,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,roots:void 0,depthSort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(t,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(t,r){return r},"transform")},zat={maximal:!1,acyclic:!1},r1=o(function(t){return t.scratch("breadthfirst")},"getInfo"),g0e=o(function(t,r){return t.scratch("breadthfirst",r)},"setInfo");o(Xme,"BreadthFirstLayout");Xme.prototype.run=function(){var e=this.options,t=e.cy,r=e.eles,i=r.nodes().filter(function(Me){return Me.isChildless()}),n=r,a=e.directed,s=e.acyclic||e.maximal||e.maximalAdjustments>0,l=!!e.boundingBox,u=is(l?e.boundingBox:structuredClone(t.extent())),h;if(uo(e.roots))h=e.roots;else if(Si(e.roots)){for(var d=[],f=0;f<e.roots.length;f++){var p=e.roots[f],m=t.getElementById(p);d.push(m)}h=t.collection(d)}else if(sr(e.roots))h=t.$(e.roots);else if(a)h=i.roots();else{var g=r.components();h=t.collection();for(var y=o(function(){var $e=g[v],de=$e.maxDegree(!1),rt=$e.filter(function(ke){return ke.degree(!1)===de});h=h.add(rt)},"_loop"),v=0;v<g.length;v++)y()}var x=[],b={},T=o(function($e,de){x[de]==null&&(x[de]=[]);var rt=x[de].length;x[de].push($e),g0e($e,{index:rt,depth:de})},"addToDepth"),w=o(function($e,de){var rt=r1($e),ke=rt.depth,Fe=rt.index;x[ke][Fe]=null,$e.isChildless()&&T($e,de)},"changeDepth");n.bfs({roots:h,directed:e.directed,visit:o(function($e,de,rt,ke,Fe){var He=$e[0],at=He.id();He.isChildless()&&T(He,Fe),b[at]=!0},"visit")});for(var C=[],k=0;k<i.length;k++){var E=i[k];b[E.id()]||C.push(E)}var A=o(function($e){for(var de=x[$e],rt=0;rt<de.length;rt++){var ke=de[rt];if(ke==null){de.splice(rt,1),rt--;continue}g0e(ke,{depth:$e,index:rt})}},"assignDepthsAt"),N=o(function($e,de){for(var rt=r1($e),ke=$e.incomers().filter(function(ve){return ve.isNode()&&r.has(ve)}),Fe=-1,He=$e.id(),at=0;at<ke.length;at++){var qe=ke[at],Ue=r1(qe);Fe=Math.max(Fe,Ue.depth)}if(rt.depth<=Fe){if(!e.acyclic&&de[He])return null;var ye=Fe+1;return w($e,ye),de[He]=ye,!0}return!1},"adjustMaximally");if(a&&s){var P=[],I={},D=o(function($e){return P.push($e)},"enqueue"),_=o(function(){return P.shift()},"dequeue");for(i.forEach(function(Me){return P.push(Me)});P.length>0;){var R=_(),M=N(R,I);if(M)R.outgoers().filter(function(Me){return Me.isNode()&&r.has(Me)}).forEach(D);else if(M===null){di("Detected double maximal shift for node `"+R.id()+"`.  Bailing maximal adjustment due to cycle.  Use `options.maximal: true` only on DAGs.");break}}}var L=0;if(e.avoidOverlap)for(var B=0;B<i.length;B++){var O=i[B],$=O.layoutDimensions(e),G=$.w,F=$.h;L=Math.max(L,G,F)}var V={},H=o(function($e){if(V[$e.id()])return V[$e.id()];for(var de=r1($e).depth,rt=$e.neighborhood(),ke=0,Fe=0,He=0;He<rt.length;He++){var at=rt[He];if(!(at.isEdge()||at.isParent()||!i.has(at))){var qe=r1(at);if(qe!=null){var Ue=qe.index,ye=qe.depth;if(!(Ue==null||ye==null)){var ve=x[ye].length;ye<de&&(ke+=Ue/ve,Fe++)}}}}return Fe=Math.max(1,Fe),ke=ke/Fe,Fe===0&&(ke=0),V[$e.id()]=ke,ke},"getWeightedPercent"),j=o(function($e,de){var rt=H($e),ke=H(de),Fe=rt-ke;return Fe===0?K0e($e.id(),de.id()):Fe},"sortFn");e.depthSort!==void 0&&(j=e.depthSort);for(var U=x.length,Q=0;Q<U;Q++)x[Q].sort(j),A(Q);for(var Y=[],ae=0;ae<C.length;ae++)Y.push(C[ae]);var J=o(function(){for(var $e=0;$e<U;$e++)A($e)},"assignDepths");Y.length&&(x.unshift(Y),U=x.length,J());for(var te=0,re=0;re<U;re++)te=Math.max(x[re].length,te);var ee={x:u.x1+u.w/2,y:u.y1+u.h/2},Te=i.reduce(function(Me,$e){return(function(de){return{w:Me.w===-1?de.w:(Me.w+de.w)/2,h:Me.h===-1?de.h:(Me.h+de.h)/2}})($e.boundingBox({includeLabels:e.nodeDimensionsIncludeLabels}))},{w:-1,h:-1}),ue=Math.max(U===1?0:l?(u.h-e.padding*2-Te.h)/(U-1):(u.h-e.padding*2-Te.h)/(U+1),L),De=x.reduce(function(Me,$e){return Math.max(Me,$e.length)},0),Ie=o(function($e){var de=r1($e),rt=de.depth,ke=de.index;if(e.circle){var Fe=Math.min(u.w/2/U,u.h/2/U);Fe=Math.max(Fe,L);var He=Fe*rt+Fe-(U>0&&x[0].length<=3?Fe/2:0),at=2*Math.PI/x[rt].length*ke;return rt===0&&x[0].length===1&&(He=1),{x:ee.x+He*Math.cos(at),y:ee.y+He*Math.sin(at)}}else{var qe=x[rt].length,Ue=Math.max(qe===1?0:l?(u.w-e.padding*2-Te.w)/((e.grid?De:qe)-1):(u.w-e.padding*2-Te.w)/((e.grid?De:qe)+1),L),ye={x:ee.x+(ke+1-(qe+1)/2)*Ue,y:ee.y+(rt+1-(U+1)/2)*ue};return ye}},"getPositionTopBottom"),Ee={downward:0,leftward:90,upward:180,rightward:-90};Object.keys(Ee).indexOf(e.direction)===-1&&Yi("Invalid direction '".concat(e.direction,"' specified for breadthfirst layout. 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m=a[p],g=m.layoutDimensions(t),y=g.w,v=g.h;f=Math.max(f,y,v)}if(Ot(t.radius)?d=t.radius:a.length<=1?d=0:d=Math.min(s.h,s.w)/2-f,a.length>1&&t.avoidOverlap){f*=1.75;var x=Math.cos(h)-Math.cos(0),b=Math.sin(h)-Math.sin(0),T=Math.sqrt(f*f/(x*x+b*b));d=Math.max(T,d)}var w=o(function(k,E){var A=t.startAngle+E*h*(n?1:-1),N=d*Math.cos(A),P=d*Math.sin(A),I={x:l.x+N,y:l.y+P};return I},"getPos");return i.nodes().layoutPositions(this,t,w),this};Vat={fit:!0,padding:30,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:o(function(t){return t.degree()},"concentric"),levelWidth:o(function(t){return t.maxDegree()/4},"levelWidth"),animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(t,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(t,r){return r},"transform")};o(Zme,"ConcentricLayout");Zme.prototype.run=function(){for(var e=this.options,t=e,r=t.counterclockwise!==void 0?!t.counterclockwise:t.clockwise,i=e.cy,n=t.eles,a=n.nodes().not(":parent"),s=is(t.boundingBox?t.boundingBox:{x1:0,y1:0,w:i.width(),h:i.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=[],h=0,d=0;d<a.length;d++){var f=a[d],p=void 0;p=t.concentric(f),u.push({value:p,node:f}),f._private.scratch.concentric=p}a.updateStyle();for(var m=0;m<a.length;m++){var g=a[m],y=g.layoutDimensions(t);h=Math.max(h,y.w,y.h)}u.sort(function(ue,De){return De.value-ue.value});for(var v=t.levelWidth(a),x=[[]],b=x[0],T=0;T<u.length;T++){var w=u[T];if(b.length>0){var C=Math.abs(b[0].value-w.value);C>=v&&(b=[],x.push(b))}b.push(w)}var k=h+t.minNodeSpacing;if(!t.avoidOverlap){var E=x.length>0&&x[0].length>1,A=Math.min(s.w,s.h)/2-k,N=A/(x.length+E?1:0);k=Math.min(k,N)}for(var P=0,I=0;I<x.length;I++){var D=x[I],_=t.sweep===void 0?2*Math.PI-2*Math.PI/D.length:t.sweep,R=D.dTheta=_/Math.max(1,D.length-1);if(D.length>1&&t.avoidOverlap){var M=Math.cos(R)-Math.cos(0),L=Math.sin(R)-Math.sin(0),B=Math.sqrt(k*k/(M*M+L*L));P=Math.max(B,P)}D.r=P,P+=k}if(t.equidistant){for(var O=0,$=0,G=0;G<x.length;G++){var F=x[G],V=F.r-$;O=Math.max(O,V)}$=0;for(var H=0;H<x.length;H++){var j=x[H];H===0&&($=j.r),j.r=$,$+=O}}for(var U={},Q=0;Q<x.length;Q++)for(var Y=x[Q],ae=Y.dTheta,J=Y.r,te=0;te<Y.length;te++){var re=Y[te],ee=t.startAngle+(r?1:-1)*ae*te,Te={x:l.x+J*Math.cos(ee),y:l.y+J*Math.sin(ee)};U[re.node.id()]=Te}return n.nodes().layoutPositions(this,t,function(ue){var De=ue.id();return U[De]}),this};Wat={ready:o(function(){},"ready"),stop:o(function(){},"stop"),animate:!0,animationEasing:void 0,animationDuration:void 0,animateFilter:o(function(t,r){return!0},"animateFilter"),animationThreshold:250,refresh:20,fit:!0,padding:30,boundingBox:void 0,nodeDimensionsIncludeLabels:!1,randomize:!1,componentSpacing:40,nodeRepulsion:o(function(t){return 2048},"nodeRepulsion"),nodeOverlap:4,idealEdgeLength:o(function(t){return 32},"idealEdgeLength"),edgeElasticity:o(function(t){return 32},"edgeElasticity"),nestingFactor:1.2,gravity:1,numIter:1e3,initialTemp:1e3,coolingFactor:.99,minTemp:1};o(lE,"CoseLayout");lE.prototype.run=function(){var e=this.options,t=e.cy,r=this;r.stopped=!1,(e.animate===!0||e.animate===!1)&&r.emit({type:"layoutstart",layout:r}),e.debug===!0?OO=!0:OO=!1;var i=qat(t,r,e);OO&&Hat(i),e.randomize&&Yat(i);var n=Zu(),a=o(function(){jat(i,t,e),e.fit===!0&&t.fit(e.padding)},"refresh"),s=o(function(p){return!(r.stopped||p>=e.numIter||(Xat(i,e),i.temperature=i.temperature*e.coolingFactor,i.temperature<e.minTemp))},"mainLoop"),l=o(function(){if(e.animate===!0||e.animate===!1)a(),r.one("layoutstop",e.stop),r.emit({type:"layoutstop",layout:r});else{var p=e.eles.nodes(),m=Jme(i,e,p);p.layoutPositions(r,e,m)}},"done"),u=0,h=!0;if(e.animate===!0){var d=o(function(){for(var p=0;h&&p<e.refresh;)h=s(u),u++,p++;if(!h)v0e(i,e),l();else{var m=Zu();m-n>=e.animationThreshold&&a(),O3(d)}},"frame");d()}else{for(;h;)h=s(u),u++;v0e(i,e),l()}return this};lE.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this};lE.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};qat=o(function(t,r,i){for(var n=i.eles.edges(),a=i.eles.nodes(),s=is(i.boundingBox?i.boundingBox:{x1:0,y1:0,w:t.width(),h:t.height()}),l={isCompound:t.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:a.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:n.size(),temperature:i.initialTemp,clientWidth:s.w,clientHeight:s.h,boundingBox:s},u=i.eles.components(),h={},d=0;d<u.length;d++)for(var f=u[d],p=0;p<f.length;p++){var m=f[p];h[m.id()]=d}for(var d=0;d<l.nodeSize;d++){var g=a[d],y=g.layoutDimensions(i),v={};v.isLocked=g.locked(),v.id=g.data("id"),v.parentId=g.data("parent"),v.cmptId=h[g.id()],v.children=[],v.positionX=g.position("x"),v.positionY=g.position("y"),v.offsetX=0,v.offsetY=0,v.height=y.w,v.width=y.h,v.maxX=v.positionX+v.width/2,v.minX=v.positionX-v.width/2,v.maxY=v.positionY+v.height/2,v.minY=v.positionY-v.height/2,v.padLeft=parseFloat(g.style("padding")),v.padRight=parseFloat(g.style("padding")),v.padTop=parseFloat(g.style("padding")),v.padBottom=parseFloat(g.style("padding")),v.nodeRepulsion=Ji(i.nodeRepulsion)?i.nodeRepulsion(g):i.nodeRepulsion,l.layoutNodes.push(v),l.idToIndex[v.id]=d}for(var x=[],b=0,T=-1,w=[],d=0;d<l.nodeSize;d++){var g=l.layoutNodes[d],C=g.parentId;C!=null?l.layoutNodes[l.idToIndex[C]].children.push(g.id):(x[++T]=g.id,w.push(g.id))}for(l.graphSet.push(w);b<=T;){var k=x[b++],E=l.idToIndex[k],m=l.layoutNodes[E],A=m.children;if(A.length>0){l.graphSet.push(A);for(var d=0;d<A.length;d++)x[++T]=A[d]}}for(var d=0;d<l.graphSet.length;d++)for(var N=l.graphSet[d],p=0;p<N.length;p++){var P=l.idToIndex[N[p]];l.indexToGraph[P]=d}for(var d=0;d<l.edgeSize;d++){var I=n[d],D={};D.id=I.data("id"),D.sourceId=I.data("source"),D.targetId=I.data("target");var _=Ji(i.idealEdgeLength)?i.idealEdgeLength(I):i.idealEdgeLength,R=Ji(i.edgeElasticity)?i.edgeElasticity(I):i.edgeElasticity,M=l.idToIndex[D.sourceId],L=l.idToIndex[D.targetId],B=l.indexToGraph[M],O=l.indexToGraph[L];if(B!=O){for(var $=Uat(D.sourceId,D.targetId,l),G=l.graphSet[$],F=0,v=l.layoutNodes[M];G.indexOf(v.id)===-1;)v=l.layoutNodes[l.idToIndex[v.parentId]],F++;for(v=l.layoutNodes[L];G.indexOf(v.id)===-1;)v=l.layoutNodes[l.idToIndex[v.parentId]],F++;_*=F*i.nestingFactor}D.idealLength=_,D.elasticity=R,l.layoutEdges.push(D)}return l},"createLayoutInfo"),Uat=o(function(t,r,i){var n=Qme(t,r,0,i);return 2>n.count?0:n.graph},"findLCA"),Qme=o(function(t,r,i,n){var a=n.graphSet[i];if(-1<a.indexOf(t)&&-1<a.indexOf(r))return{count:2,graph:i};for(var s=0,l=0;l<a.length;l++){var u=a[l],h=n.idToIndex[u],d=n.layoutNodes[h].children;if(d.length!==0){var f=n.indexToGraph[n.idToIndex[d[0]]],p=Qme(t,r,f,n);if(p.count!==0)if(p.count===1){if(s++,s===2)break}else return p}}return{count:s,graph:i}},"findLCA_aux"),Yat=o(function(t,r){for(var i=t.clientWidth,n=t.clientHeight,a=0;a<t.nodeSize;a++){var s=t.layoutNodes[a];s.children.length===0&&!s.isLocked&&(s.positionX=Math.random()*i,s.positionY=Math.random()*n)}},"randomizePositions"),Jme=o(function(t,r,i){var n=t.boundingBox,a={x1:1/0,x2:-1/0,y1:1/0,y2:-1/0};return r.boundingBox&&(i.forEach(function(s){var l=t.layoutNodes[t.idToIndex[s.data("id")]];a.x1=Math.min(a.x1,l.positionX),a.x2=Math.max(a.x2,l.positionX),a.y1=Math.min(a.y1,l.positionY),a.y2=Math.max(a.y2,l.positionY)}),a.w=a.x2-a.x1,a.h=a.y2-a.y1),function(s,l){var u=t.layoutNodes[t.idToIndex[s.data("id")]];if(r.boundingBox){var h=a.w===0?.5:(u.positionX-a.x1)/a.w,d=a.h===0?.5:(u.positionY-a.y1)/a.h;return{x:n.x1+h*n.w,y:n.y1+d*n.h}}else return{x:u.positionX,y:u.positionY}}},"getScaleInBoundsFn"),jat=o(function(t,r,i){var n=i.layout,a=i.eles.nodes(),s=Jme(t,i,a);a.positions(s),t.ready!==!0&&(t.ready=!0,n.one("layoutready",i.ready),n.emit({type:"layoutready",layout:this}))},"refreshPositions"),Xat=o(function(t,r,i){Kat(t,r),Jat(t),est(t,r),tst(t),rst(t)},"step"),Kat=o(function(t,r){for(var i=0;i<t.graphSet.length;i++)for(var n=t.graphSet[i],a=n.length,s=0;s<a;s++)for(var l=t.layoutNodes[t.idToIndex[n[s]]],u=s+1;u<a;u++){var h=t.layoutNodes[t.idToIndex[n[u]]];Zat(l,h,t,r)}},"calculateNodeForces"),y0e=o(function(t){return-1+2*t*Math.random()},"randomDistance"),Zat=o(function(t,r,i,n){var a=t.cmptId,s=r.cmptId;if(!(a!==s&&!i.isCompound)){var l=r.positionX-t.positionX,u=r.positionY-t.positionY,h=1;l===0&&u===0&&(l=y0e(h),u=y0e(h));var d=Qat(t,r,l,u);if(d>0)var f=n.nodeOverlap*d,p=Math.sqrt(l*l+u*u),m=f*l/p,g=f*u/p;else var y=V3(t,l,u),v=V3(r,-1*l,-1*u),x=v.x-y.x,b=v.y-y.y,T=x*x+b*b,p=Math.sqrt(T),f=(t.nodeRepulsion+r.nodeRepulsion)/T,m=f*x/p,g=f*b/p;t.isLocked||(t.offsetX-=m,t.offsetY-=g),r.isLocked||(r.offsetX+=m,r.offsetY+=g)}},"nodeRepulsion"),Qat=o(function(t,r,i,n){if(i>0)var a=t.maxX-r.minX;else var a=r.maxX-t.minX;if(n>0)var s=t.maxY-r.minY;else var s=r.maxY-t.minY;return a>=0&&s>=0?Math.sqrt(a*a+s*s):0},"nodesOverlap"),V3=o(function(t,r,i){var n=t.positionX,a=t.positionY,s=t.height||1,l=t.width||1,u=i/r,h=s/l,d={};return r===0&&0<i||r===0&&0>i?(d.x=n,d.y=a+s/2,d):0<r&&-1*h<=u&&u<=h?(d.x=n+l/2,d.y=a+l*i/2/r,d):0>r&&-1*h<=u&&u<=h?(d.x=n-l/2,d.y=a-l*i/2/r,d):0<i&&(u<=-1*h||u>=h)?(d.x=n+s*r/2/i,d.y=a+s/2,d):(0>i&&(u<=-1*h||u>=h)&&(d.x=n-s*r/2/i,d.y=a-s/2),d)},"findClippingPoint"),Jat=o(function(t,r){for(var i=0;i<t.edgeSize;i++){var n=t.layoutEdges[i],a=t.idToIndex[n.sourceId],s=t.layoutNodes[a],l=t.idToIndex[n.targetId],u=t.layoutNodes[l],h=u.positionX-s.positionX,d=u.positionY-s.positionY;if(!(h===0&&d===0)){var f=V3(s,h,d),p=V3(u,-1*h,-1*d),m=p.x-f.x,g=p.y-f.y,y=Math.sqrt(m*m+g*g),v=Math.pow(n.idealLength-y,2)/n.elasticity;if(y!==0)var x=v*m/y,b=v*g/y;else var x=0,b=0;s.isLocked||(s.offsetX+=x,s.offsetY+=b),u.isLocked||(u.offsetX-=x,u.offsetY-=b)}}},"calculateEdgeForces"),est=o(function(t,r){if(r.gravity!==0)for(var i=1,n=0;n<t.graphSet.length;n++){var a=t.graphSet[n],s=a.length;if(n===0)var l=t.clientHeight/2,u=t.clientWidth/2;else var h=t.layoutNodes[t.idToIndex[a[0]]],d=t.layoutNodes[t.idToIndex[h.parentId]],l=d.positionX,u=d.positionY;for(var f=0;f<s;f++){var p=t.layoutNodes[t.idToIndex[a[f]]];if(!p.isLocked){var m=l-p.positionX,g=u-p.positionY,y=Math.sqrt(m*m+g*g);if(y>i){var v=r.gravity*m/y,x=r.gravity*g/y;p.offsetX+=v,p.offsetY+=x}}}}},"calculateGravityForces"),tst=o(function(t,r){var i=[],n=0,a=-1;for(i.push.apply(i,t.graphSet[0]),a+=t.graphSet[0].length;n<=a;){var s=i[n++],l=t.idToIndex[s],u=t.layoutNodes[l],h=u.children;if(0<h.length&&!u.isLocked){for(var d=u.offsetX,f=u.offsetY,p=0;p<h.length;p++){var m=t.layoutNodes[t.idToIndex[h[p]]];m.offsetX+=d,m.offsetY+=f,i[++a]=h[p]}u.offsetX=0,u.offsetY=0}}},"propagateForces"),rst=o(function(t,r){for(var i=0;i<t.nodeSize;i++){var n=t.layoutNodes[i];0<n.children.length&&(n.maxX=void 0,n.minX=void 0,n.maxY=void 0,n.minY=void 0)}for(var i=0;i<t.nodeSize;i++){var n=t.layoutNodes[i];if(!(0<n.children.length||n.isLocked)){var a=ist(n.offsetX,n.offsetY,t.temperature);n.positionX+=a.x,n.positionY+=a.y,n.offsetX=0,n.offsetY=0,n.minX=n.positionX-n.width,n.maxX=n.positionX+n.width,n.minY=n.positionY-n.height,n.maxY=n.positionY+n.height,ege(n,t)}}for(var i=0;i<t.nodeSize;i++){var n=t.layoutNodes[i];0<n.children.length&&!n.isLocked&&(n.positionX=(n.maxX+n.minX)/2,n.positionY=(n.maxY+n.minY)/2,n.width=n.maxX-n.minX,n.height=n.maxY-n.minY)}},"updatePositions"),ist=o(function(t,r,i){var n=Math.sqrt(t*t+r*r);if(n>i)var a={x:i*t/n,y:i*r/n};else var a={x:t,y:r};return a},"limitForce"),ege=o(function(t,r){var i=t.parentId;if(i!=null){var n=r.layoutNodes[r.idToIndex[i]],a=!1;if((n.maxX==null||t.maxX+n.padRight>n.maxX)&&(n.maxX=t.maxX+n.padRight,a=!0),(n.minX==null||t.minX-n.padLeft<n.minX)&&(n.minX=t.minX-n.padLeft,a=!0),(n.maxY==null||t.maxY+n.padBottom>n.maxY)&&(n.maxY=t.maxY+n.padBottom,a=!0),(n.minY==null||t.minY-n.padTop<n.minY)&&(n.minY=t.minY-n.padTop,a=!0),a)return ege(n,r)}},"updateAncestryBoundaries"),v0e=o(function(t,r){for(var i=t.layoutNodes,n=[],a=0;a<i.length;a++){var s=i[a],l=s.cmptId,u=n[l]=n[l]||[];u.push(s)}for(var h=0,a=0;a<n.length;a++){var d=n[a];if(d){d.x1=1/0,d.x2=-1/0,d.y1=1/0,d.y2=-1/0;for(var f=0;f<d.length;f++){var p=d[f];d.x1=Math.min(d.x1,p.positionX-p.width/2),d.x2=Math.max(d.x2,p.positionX+p.width/2),d.y1=Math.min(d.y1,p.positionY-p.height/2),d.y2=Math.max(d.y2,p.positionY+p.height/2)}d.w=d.x2-d.x1,d.h=d.y2-d.y1,h+=d.w*d.h}}n.sort(function(b,T){return T.w*T.h-b.w*b.h});for(var m=0,g=0,y=0,v=0,x=Math.sqrt(h)*t.clientWidth/t.clientHeight,a=0;a<n.length;a++){var d=n[a];if(d){for(var f=0;f<d.length;f++){var p=d[f];p.isLocked||(p.positionX+=m-d.x1,p.positionY+=g-d.y1)}m+=d.w+r.componentSpacing,y+=d.w+r.componentSpacing,v=Math.max(v,d.h),y>x&&(g+=v+r.componentSpacing,m=0,y=0,v=0)}}},"separateComponents"),nst={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,avoidOverlapPadding:10,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,condense:!1,rows:void 0,cols:void 0,position:o(function(t){},"position"),sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(t,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(t,r){return r},"transform")};o(tge,"GridLayout");tge.prototype.run=function(){var e=this.options,t=e,r=e.cy,i=t.eles,n=i.nodes().not(":parent");t.sort&&(n=n.sort(t.sort));var a=is(t.boundingBox?t.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()});if(a.h===0||a.w===0)i.nodes().layoutPositions(this,t,function(H){return{x:a.x1,y:a.y1}});else{var s=n.size(),l=Math.sqrt(s*a.h/a.w),u=Math.round(l),h=Math.round(a.w/a.h*l),d=o(function(j){if(j==null)return Math.min(u,h);var U=Math.min(u,h);U==u?u=j:h=j},"small"),f=o(function(j){if(j==null)return Math.max(u,h);var U=Math.max(u,h);U==u?u=j:h=j},"large"),p=t.rows,m=t.cols!=null?t.cols:t.columns;if(p!=null&&m!=null)u=p,h=m;else if(p!=null&&m==null)u=p,h=Math.ceil(s/u);else if(p==null&&m!=null)h=m,u=Math.ceil(s/h);else if(h*u>s){var g=d(),y=f();(g-1)*y>=s?d(g-1):(y-1)*g>=s&&f(y-1)}else for(;h*u<s;){var v=d(),x=f();(x+1)*v>=s?f(x+1):d(v+1)}var b=a.w/h,T=a.h/u;if(t.condense&&(b=0,T=0),t.avoidOverlap)for(var w=0;w<n.length;w++){var C=n[w],k=C._private.position;(k.x==null||k.y==null)&&(k.x=0,k.y=0);var E=C.layoutDimensions(t),A=t.avoidOverlapPadding,N=E.w+A,P=E.h+A;b=Math.max(b,N),T=Math.max(T,P)}for(var I={},D=o(function(j,U){return!!I["c-"+j+"-"+U]},"used"),_=o(function(j,U){I["c-"+j+"-"+U]=!0},"use"),R=0,M=0,L=o(function(){M++,M>=h&&(M=0,R++)},"moveToNextCell"),B={},O=0;O<n.length;O++){var $=n[O],G=t.position($);if(G&&(G.row!==void 0||G.col!==void 0)){var F={row:G.row,col:G.col};if(F.col===void 0)for(F.col=0;D(F.row,F.col);)F.col++;else if(F.row===void 0)for(F.row=0;D(F.row,F.col);)F.row++;B[$.id()]=F,_(F.row,F.col)}}var V=o(function(j,U){var Q,Y;if(j.locked()||j.isParent())return!1;var ae=B[j.id()];if(ae)Q=ae.col*b+b/2+a.x1,Y=ae.row*T+T/2+a.y1;else{for(;D(R,M);)L();Q=M*b+b/2+a.x1,Y=R*T+T/2+a.y1,_(R,M),L()}return{x:Q,y:Y}},"getPos");n.layoutPositions(this,t,V)}return this};ast={ready:o(function(){},"ready"),stop:o(function(){},"stop")};o(DP,"NullLayout");DP.prototype.run=function(){var 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n=this.getCachedZSortedEles().interactive,a=this.cy.zoom(),s=2/a,l=[],u=Math.min(e,r),h=Math.max(e,r),d=Math.min(t,i),f=Math.max(t,i);e=u,r=h,t=d,i=f;var p=is({x1:e,y1:t,x2:r,y2:i}),m=[{x:p.x1,y:p.y1},{x:p.x2,y:p.y1},{x:p.x2,y:p.y2},{x:p.x1,y:p.y2}],g=[[m[0],m[1]],[m[1],m[2]],[m[2],m[3]],[m[3],m[0]]];function y(Te,ue,De){return Ps(Te,ue,De)}o(y,"preprop");function v(Te,ue){var De=Te._private,Ie=s,Ee="";Te.boundingBox();var we=De.labelBounds.main;if(!we)return null;var Me=y(De.rscratch,"labelX",ue),$e=y(De.rscratch,"labelY",ue),de=y(De.rscratch,"labelAngle",ue),rt=Te.pstyle(Ee+"text-margin-x").pfValue,ke=Te.pstyle(Ee+"text-margin-y").pfValue,Fe=we.x1-Ie-rt,He=we.x2+Ie-rt,at=we.y1-Ie-ke,qe=we.y2+Ie-ke;if(de){var Ue=Math.cos(de),ye=Math.sin(de),ve=o(function(fe,W){return fe=fe-Me,W=W-$e,{x:fe*Ue-W*ye+Me,y:fe*ye+W*Ue+$e}},"rotate");return[ve(Fe,at),ve(He,at),ve(He,qe),ve(Fe,qe)]}else return[{x:Fe,y:at},{x:He,y:at},{x:He,y:qe},{x:Fe,y:qe}]}o(v,"getRotatedLabelBox");function x(Te,ue,De,Ie){function Ee(we,Me,$e){return($e.y-we.y)*(Me.x-we.x)>(Me.y-we.y)*($e.x-we.x)}return o(Ee,"ccw"),Ee(Te,De,Ie)!==Ee(ue,De,Ie)&&Ee(Te,ue,De)!==Ee(Te,ue,Ie)}o(x,"doLinesIntersect");for(var b=0;b<n.length;b++){var T=n[b];if(T.isNode()){var w=T,C=w.pstyle("text-events").strValue==="yes",k=w.pstyle("box-selection").strValue,E=w.pstyle("box-select-labels").strValue==="yes";if(k==="none")continue;var A=(k==="overlap"||E)&&C,N=w.boundingBox({includeNodes:!0,includeEdges:!1,includeLabels:A});if(k==="contain"){var P=!1;if(E&&C){var I=v(w);I&&N9(I,m)&&(l.push(w),P=!0)}!P&&hme(p,N)&&l.push(w)}else if(k==="overlap"&&xP(p,N)){var D=w.boundingBox({includeNodes:!0,includeEdges:!0,includeLabels:!1,includeMainLabels:!1,includeSourceLabels:!1,includeTargetLabels:!1}),_=[{x:D.x1,y:D.y1},{x:D.x2,y:D.y1},{x:D.x2,y:D.y2},{x:D.x1,y:D.y2}];if(N9(_,m))l.push(w);else{var R=v(w);R&&N9(R,m)&&l.push(w)}}}else{var M=T,L=M._private,B=L.rscratch,O=M.pstyle("box-selection").strValue;if(O==="none")continue;if(O==="contain"){if(B.startX!=null&&B.startY!=null&&!zd(p,B.startX,B.startY)||B.endX!=null&&B.endY!=null&&!zd(p,B.endX,B.endY))continue;if(B.edgeType==="bezier"||B.edgeType==="multibezier"||B.edgeType==="self"||B.edgeType==="compound"||B.edgeType==="segments"||B.edgeType==="haystack"){for(var $=L.rstyle.bezierPts||L.rstyle.linePts||L.rstyle.haystackPts,G=!0,F=0;F<$.length;F++)if(!$fe(p,$[F])){G=!1;break}G&&l.push(M)}else B.edgeType==="straight"&&l.push(M)}else if(O==="overlap"){var V=!1;if(B.startX!=null&&B.startY!=null&&B.endX!=null&&B.endY!=null&&(zd(p,B.startX,B.startY)||zd(p,B.endX,B.endY)))l.push(M),V=!0;else if(!V&&B.edgeType==="haystack"){for(var H=L.rstyle.haystackPts,j=0;j<H.length;j++)if($fe(p,H[j])){l.push(M),V=!0;break}}if(!V){var U=L.rstyle.bezierPts||L.rstyle.linePts||L.rstyle.haystackPts;if((!U||U.length<2)&&B.edgeType==="straight"&&B.startX!=null&&B.startY!=null&&B.endX!=null&&B.endY!=null&&(U=[{x:B.startX,y:B.startY},{x:B.endX,y:B.endY}]),!U||U.length<2)continue;for(var Q=0;Q<U.length-1;Q++){for(var Y=U[Q],ae=U[Q+1],J=0;J<g.length;J++){var te=Dn(g[J],2),re=te[0],ee=te[1];if(x(Y,ae,re,ee)){l.push(M),V=!0;break}}if(V)break}}}}}return l};W3={};W3.calculateArrowAngles=function(e){var t=e._private.rscratch,r=t.edgeType==="haystack",i=t.edgeType==="bezier",n=t.edgeType==="multibezier",a=t.edgeType==="segments",s=t.edgeType==="compound",l=t.edgeType==="self",u,h,d,f,p,m,x,b;if(r?(d=t.haystackPts[0],f=t.haystackPts[1],p=t.haystackPts[2],m=t.haystackPts[3]):(d=t.arrowStartX,f=t.arrowStartY,p=t.arrowEndX,m=t.arrowEndY),x=t.midX,b=t.midY,a)u=d-t.segpts[0],h=f-t.segpts[1];else if(n||s||l||i){var g=t.allpts,y=na(g[0],g[2],g[4],.1),v=na(g[1],g[3],g[5],.1);u=d-y,h=f-v}else u=d-x,h=f-b;t.srcArrowAngle=h3(u,h);var x=t.midX,b=t.midY;if(r&&(x=(d+p)/2,b=(f+m)/2),u=p-d,h=m-f,a){var g=t.allpts;if(g.length/2%2===0){var T=g.length/2,w=T-2;u=g[T]-g[w],h=g[T+1]-g[w+1]}else if(t.isRound)u=t.midVector[1],h=-t.midVector[0];else{var T=g.length/2-1,w=T-2;u=g[T]-g[w],h=g[T+1]-g[w+1]}}else if(n||s||l){var g=t.allpts,C=t.ctrlpts,k,E,A,N;if(C.length/2%2===0){var P=g.length/2-1,I=P+2,D=I+2;k=na(g[P],g[I],g[D],0),E=na(g[P+1],g[I+1],g[D+1],0),A=na(g[P],g[I],g[D],1e-4),N=na(g[P+1],g[I+1],g[D+1],1e-4)}else{var I=g.length/2-1,P=I-2,D=I+2;k=na(g[P],g[I],g[D],.4999),E=na(g[P+1],g[I+1],g[D+1],.4999),A=na(g[P],g[I],g[D],.5),N=na(g[P+1],g[I+1],g[D+1],.5)}u=A-k,h=N-E}if(t.midtgtArrowAngle=h3(u,h),t.midDispX=u,t.midDispY=h,u*=-1,h*=-1,a){var g=t.allpts;if(g.length/2%2!==0){if(!t.isRound){var T=g.length/2-1,_=T+2;u=-(g[_]-g[T]),h=-(g[_+1]-g[T+1])}}}if(t.midsrcArrowAngle=h3(u,h),a)u=p-t.segpts[t.segpts.length-2],h=m-t.segpts[t.segpts.length-1];else if(n||s||l||i){var g=t.allpts,R=g.length,y=na(g[R-6],g[R-4],g[R-2],.9),v=na(g[R-5],g[R-3],g[R-1],.9);u=p-y,h=m-v}else u=p-x,h=m-b;t.tgtArrowAngle=h3(u,h)};W3.getArrowWidth=W3.getArrowHeight=function(e,t){var r=this.arrowWidthCache=this.arrowWidthCache||{},i=r[e+", "+t];return i||(i=Math.max(Math.pow(e*13.37,.9),29)*t,r[e+", "+t]=i,i)};Pc={},Yo={},w0e=o(function(t,r,i){i.x=r.x-t.x,i.y=r.y-t.y,i.len=Math.sqrt(i.x*i.x+i.y*i.y),i.nx=i.x/i.len,i.ny=i.y/i.len,i.ang=Math.atan2(i.ny,i.nx)},"asVec"),cst=o(function(t,r){r.x=t.x*-1,r.y=t.y*-1,r.nx=t.nx*-1,r.ny=t.ny*-1,r.ang=t.ang>0?-(Math.PI-t.ang):Math.PI+t.ang},"invertVec"),ust=o(function(t,r,i,n,a){if(t!==k0e?w0e(r,t,Pc):cst(Yo,Pc),w0e(r,i,Yo),T0e=Pc.nx*Yo.ny-Pc.ny*Yo.nx,C0e=Pc.nx*Yo.nx-Pc.ny*-Yo.ny,ju=Math.asin(Math.max(-1,Math.min(1,T0e))),Math.abs(ju)<1e-6){tP=r.x,rP=r.y,Xp=i1=0;return}Zp=1,R3=!1,C0e<0?ju<0?ju=Math.PI+ju:(ju=Math.PI-ju,Zp=-1,R3=!0):ju>0&&(Zp=-1,R3=!0),r.radius!==void 0?i1=r.radius:i1=n,Up=ju/2,x3=Math.min(Pc.len/2,Yo.len/2),a?(Ic=Math.abs(Math.cos(Up)*i1/Math.sin(Up)),Ic>x3?(Ic=x3,Xp=Math.abs(Ic*Math.sin(Up)/Math.cos(Up))):Xp=i1):(Ic=Math.min(x3,i1),Xp=Math.abs(Ic*Math.sin(Up)/Math.cos(Up))),iP=r.x+Yo.nx*Ic,nP=r.y+Yo.ny*Ic,tP=iP-Yo.ny*Xp*Zp,rP=nP+Yo.nx*Xp*Zp,age=r.x+Pc.nx*Ic,sge=r.y+Pc.ny*Ic,k0e=r},"calcCornerArc");o(oge,"drawPreparedRoundCorner");o(LP,"getRoundCorner");t2=.01,hst=Math.sqrt(2*t2),Ba={};Ba.findMidptPtsEtc=function(e,t){var r=t.posPts,i=t.intersectionPts,n=t.vectorNormInverse,a,s=e.pstyle("source-endpoint"),l=e.pstyle("target-endpoint"),u=s.units!=null&&l.units!=null,h=o(function(C,k,E,A){var N=A-k,P=E-C,I=Math.sqrt(P*P+N*N);return{x:-N/I,y:P/I}},"recalcVectorNormInverse"),d=e.pstyle("edge-distances").value;switch(d){case"node-position":a=r;break;case"intersection":a=i;break;case"endpoints":{if(u){var f=this.manualEndptToPx(e.source()[0],s),p=Dn(f,2),m=p[0],g=p[1],y=this.manualEndptToPx(e.target()[0],l),v=Dn(y,2),x=v[0],b=v[1],T={x1:m,y1:g,x2:x,y2:b};n=h(m,g,x,b),a=T}else di("Edge ".concat(e.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint.  Falling back on edge-distances:intersection (default).")),a=i;break}}return{midptPts:a,vectorNormInverse:n}};Ba.findHaystackPoints=function(e){for(var t=0;t<e.length;t++){var r=e[t],i=r._private,n=i.rscratch;if(!n.haystack){var a=Math.random()*2*Math.PI;n.source={x:Math.cos(a),y:Math.sin(a)},a=Math.random()*2*Math.PI,n.target={x:Math.cos(a),y:Math.sin(a)}}var s=i.source,l=i.target,u=s.position(),h=l.position(),d=s.width(),f=l.width(),p=s.height(),m=l.height(),g=r.pstyle("haystack-radius").value,y=g/2;n.haystackPts=n.allpts=[n.source.x*d*y+u.x,n.source.y*p*y+u.y,n.target.x*f*y+h.x,n.target.y*m*y+h.y],n.midX=(n.allpts[0]+n.allpts[2])/2,n.midY=(n.allpts[1]+n.allpts[3])/2,n.edgeType="haystack",n.haystack=!0,this.storeEdgeProjections(r),this.calculateArrowAngles(r),this.recalculateEdgeLabelProjections(r),this.calculateLabelAngles(r)}};Ba.findSegmentsPoints=function(e,t){var r=e._private.rscratch,i=e.pstyle("segment-weights"),n=e.pstyle("segment-distances"),a=e.pstyle("segment-radii"),s=e.pstyle("radius-type"),l=Math.min(i.pfValue.length,n.pfValue.length),u=a.pfValue[a.pfValue.length-1],h=s.pfValue[s.pfValue.length-1];r.edgeType="segments",r.segpts=[],r.radii=[],r.isArcRadius=[];for(var d=0;d<l;d++){var f=i.pfValue[d],p=n.pfValue[d],m=1-f,g=f,y=this.findMidptPtsEtc(e,t),v=y.midptPts,x=y.vectorNormInverse,b={x:v.x1*m+v.x2*g,y:v.y1*m+v.y2*g};r.segpts.push(b.x+x.x*p,b.y+x.y*p),r.radii.push(a.pfValue[d]!==void 0?a.pfValue[d]:u),r.isArcRadius.push((s.pfValue[d]!==void 0?s.pfValue[d]:h)==="arc-radius")}};Ba.findLoopPoints=function(e,t,r,i){var n=e._private.rscratch,a=t.dirCounts,s=t.srcPos,l=e.pstyle("control-point-distances"),u=l?l.pfValue[0]:void 0,h=e.pstyle("loop-direction").pfValue,d=e.pstyle("loop-sweep").pfValue,f=e.pstyle("control-point-step-size").pfValue;n.edgeType="self";var p=r,m=f;i&&(p=0,m=u);var g=h-Math.PI/2,y=g-d/2,v=g+d/2,x=h+"_"+d;p=a[x]===void 0?a[x]=0:++a[x],n.ctrlpts=[s.x+Math.cos(y)*1.4*m*(p/3+1),s.y+Math.sin(y)*1.4*m*(p/3+1),s.x+Math.cos(v)*1.4*m*(p/3+1),s.y+Math.sin(v)*1.4*m*(p/3+1)]};Ba.findCompoundLoopPoints=function(e,t,r,i){var n=e._private.rscratch;n.edgeType="compound";var a=t.srcPos,s=t.tgtPos,l=t.srcW,u=t.srcH,h=t.tgtW,d=t.tgtH,f=e.pstyle("control-point-step-size").pfValue,p=e.pstyle("control-point-distances"),m=p?p.pfValue[0]:void 0,g=r,y=f;i&&(g=0,y=m);var v=50,x={x:a.x-l/2,y:a.y-u/2},b={x:s.x-h/2,y:s.y-d/2},T={x:Math.min(x.x,b.x),y:Math.min(x.y,b.y)},w=.5,C=Math.max(w,Math.log(l*t2)),k=Math.max(w,Math.log(h*t2));n.ctrlpts=[T.x,T.y-(1+Math.pow(v,1.12)/100)*y*(g/3+1)*C,T.x-(1+Math.pow(v,1.12)/100)*y*(g/3+1)*k,T.y]};Ba.findStraightEdgePoints=function(e){e._private.rscratch.edgeType="straight"};Ba.findBezierPoints=function(e,t,r,i,n){var a=e._private.rscratch,s=e.pstyle("control-point-step-size").pfValue,l=e.pstyle("control-point-distances"),u=e.pstyle("control-point-weights"),h=l&&u?Math.min(l.value.length,u.value.length):1,d=l?l.pfValue[0]:void 0,f=u.value[0],p=i;a.edgeType=p?"multibezier":"bezier",a.ctrlpts=[];for(var m=0;m<h;m++){var g=(.5-t.eles.length/2+r)*s*(n?-1:1),y=void 0,v=vP(g);p&&(d=l?l.pfValue[m]:s,f=u.value[m]),i?y=d:y=d!==void 0?v*d:void 0;var x=y!==void 0?y:g,b=1-f,T=f,w=this.findMidptPtsEtc(e,t),C=w.midptPts,k=w.vectorNormInverse,E={x:C.x1*b+C.x2*T,y:C.y1*b+C.y2*T};a.ctrlpts.push(E.x+k.x*x,E.y+k.y*x)}};Ba.findTaxiPoints=function(e,t){var r=e._private.rscratch;r.edgeType="segments";var i="vertical",n="horizontal",a="leftward",s="rightward",l="downward",u="upward",h="auto",d=t.posPts,f=t.srcW,p=t.srcH,m=t.tgtW,g=t.tgtH,y=e.pstyle("edge-distances").value,v=y!=="node-position",x=e.pstyle("taxi-direction").value,b=x,T=e.pstyle("taxi-turn"),w=T.units==="%",C=T.pfValue,k=C<0,E=e.pstyle("taxi-turn-min-distance").pfValue,A=v?(f+m)/2:0,N=v?(p+g)/2:0,P=d.x2-d.x1,I=d.y2-d.y1,D=o(function(W,ce){return W>0?Math.max(W-ce,0):Math.min(W+ce,0)},"subDWH"),_=D(P,A),R=D(I,N),M=!1;b===h?x=Math.abs(_)>Math.abs(R)?n:i:b===u||b===l?(x=i,M=!0):(b===a||b===s)&&(x=n,M=!0);var L=x===i,B=L?R:_,O=L?I:P,$=vP(O),G=!1;!(M&&(w||k))&&(b===l&&O<0||b===u&&O>0||b===a&&O>0||b===s&&O<0)&&($*=-1,B=$*Math.abs(B),G=!0);var F;if(w){var V=C<0?1+C:C;F=V*B}else{var H=C<0?B:0;F=H+C*$}var j=o(function(W){return Math.abs(W)<E||Math.abs(W)>=Math.abs(B)},"getIsTooClose"),U=j(F),Q=j(Math.abs(B)-Math.abs(F)),Y=U||Q;if(Y&&!G)if(L){var ae=Math.abs(O)<=p/2,J=Math.abs(P)<=m/2;if(ae){var te=(d.x1+d.x2)/2,re=d.y1,ee=d.y2;r.segpts=[te,re,te,ee]}else if(J){var Te=(d.y1+d.y2)/2,ue=d.x1,De=d.x2;r.segpts=[ue,Te,De,Te]}else r.segpts=[d.x1,d.y2]}else{var Ie=Math.abs(O)<=f/2,Ee=Math.abs(I)<=g/2;if(Ie){var we=(d.y1+d.y2)/2,Me=d.x1,$e=d.x2;r.segpts=[Me,we,$e,we]}else if(Ee){var de=(d.x1+d.x2)/2,rt=d.y1,ke=d.y2;r.segpts=[de,rt,de,ke]}else r.segpts=[d.x2,d.y1]}else if(L){var Fe=d.y1+F+(v?p/2*$:0),He=d.x1,at=d.x2;r.segpts=[He,Fe,at,Fe]}else{var qe=d.x1+F+(v?f/2*$:0),Ue=d.y1,ye=d.y2;r.segpts=[qe,Ue,qe,ye]}if(r.isRound){var ve=e.pstyle("taxi-radius").value,ie=e.pstyle("radius-type").value[0]==="arc-radius";r.radii=new Array(r.segpts.length/2).fill(ve),r.isArcRadius=new Array(r.segpts.length/2).fill(ie)}};Ba.tryToCorrectInvalidPoints=function(e,t){var r=e._private.rscratch;if(r.edgeType==="bezier"){var i=t.srcPos,n=t.tgtPos,a=t.srcW,s=t.srcH,l=t.tgtW,u=t.tgtH,h=t.srcShape,d=t.tgtShape,f=t.srcCornerRadius,p=t.tgtCornerRadius,m=t.srcRs,g=t.tgtRs,y=!Ot(r.startX)||!Ot(r.startY),v=!Ot(r.arrowStartX)||!Ot(r.arrowStartY),x=!Ot(r.endX)||!Ot(r.endY),b=!Ot(r.arrowEndX)||!Ot(r.arrowEndY),T=3,w=this.getArrowWidth(e.pstyle("width").pfValue,e.pstyle("arrow-scale").value)*this.arrowShapeWidth,C=T*w,k=e0({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.startX,y:r.startY}),E=k<C,A=e0({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.endX,y:r.endY}),N=A<C,P=!1;if(y||v||E){P=!0;var I={x:r.ctrlpts[0]-i.x,y:r.ctrlpts[1]-i.y},D=Math.sqrt(I.x*I.x+I.y*I.y),_={x:I.x/D,y:I.y/D},R=Math.max(a,s),M={x:r.ctrlpts[0]+_.x*2*R,y:r.ctrlpts[1]+_.y*2*R},L=h.intersectLine(i.x,i.y,a,s,M.x,M.y,0,f,m);E?(r.ctrlpts[0]=r.ctrlpts[0]+_.x*(C-k),r.ctrlpts[1]=r.ctrlpts[1]+_.y*(C-k)):(r.ctrlpts[0]=L[0]+_.x*C,r.ctrlpts[1]=L[1]+_.y*C)}if(x||b||N){P=!0;var B={x:r.ctrlpts[0]-n.x,y:r.ctrlpts[1]-n.y},O=Math.sqrt(B.x*B.x+B.y*B.y),$={x:B.x/O,y:B.y/O},G=Math.max(a,s),F={x:r.ctrlpts[0]+$.x*2*G,y:r.ctrlpts[1]+$.y*2*G},V=d.intersectLine(n.x,n.y,l,u,F.x,F.y,0,p,g);N?(r.ctrlpts[0]=r.ctrlpts[0]+$.x*(C-A),r.ctrlpts[1]=r.ctrlpts[1]+$.y*(C-A)):(r.ctrlpts[0]=V[0]+$.x*C,r.ctrlpts[1]=V[1]+$.y*C)}P&&this.findEndpoints(e)}};Ba.storeAllpts=function(e){var t=e._private.rscratch;if(t.edgeType==="multibezier"||t.edgeType==="bezier"||t.edgeType==="self"||t.edgeType==="compound"){t.allpts=[],t.allpts.push(t.startX,t.startY);for(var r=0;r+1<t.ctrlpts.length;r+=2)t.allpts.push(t.ctrlpts[r],t.ctrlpts[r+1]),r+3<t.ctrlpts.length&&t.allpts.push((t.ctrlpts[r]+t.ctrlpts[r+2])/2,(t.ctrlpts[r+1]+t.ctrlpts[r+3])/2);t.allpts.push(t.endX,t.endY);var i,n;t.ctrlpts.length/2%2===0?(i=t.allpts.length/2-1,t.midX=t.allpts[i],t.midY=t.allpts[i+1]):(i=t.allpts.length/2-3,n=.5,t.midX=na(t.allpts[i],t.allpts[i+2],t.allpts[i+4],n),t.midY=na(t.allpts[i+1],t.allpts[i+3],t.allpts[i+5],n))}else if(t.edgeType==="straight")t.allpts=[t.startX,t.startY,t.endX,t.endY],t.midX=(t.startX+t.endX+t.arrowStartX+t.arrowEndX)/4,t.midY=(t.startY+t.endY+t.arrowStartY+t.arrowEndY)/4;else if(t.edgeType==="segments"){if(t.allpts=[],t.allpts.push(t.startX,t.startY),t.allpts.push.apply(t.allpts,t.segpts),t.allpts.push(t.endX,t.endY),t.isRound){t.roundCorners=[];for(var a=2;a+3<t.allpts.length;a+=2){var s=t.radii[a/2-1],l=t.isArcRadius[a/2-1];t.roundCorners.push(LP({x:t.allpts[a-2],y:t.allpts[a-1]},{x:t.allpts[a],y:t.allpts[a+1],radius:s},{x:t.allpts[a+2],y:t.allpts[a+3]},s,l))}}if(t.segpts.length%4===0){var u=t.segpts.length/2,h=u-2;t.midX=(t.segpts[h]+t.segpts[u])/2,t.midY=(t.segpts[h+1]+t.segpts[u+1])/2}else{var d=t.segpts.length/2-1;if(!t.isRound)t.midX=t.segpts[d],t.midY=t.segpts[d+1];else{var f={x:t.segpts[d],y:t.segpts[d+1]},p=t.roundCorners[d/2];if(p.radius===0){var m={x:t.segpts[d+2],y:t.segpts[d+3]};t.midX=f.x,t.midY=f.y,t.midVector=[f.y-m.y,m.x-f.x]}else{var g=[f.x-p.cx,f.y-p.cy],y=p.radius/Math.sqrt(Math.pow(g[0],2)+Math.pow(g[1],2));g=g.map(function(v){return v*y}),t.midX=p.cx+g[0],t.midY=p.cy+g[1],t.midVector=g}}}}};Ba.checkForInvalidEdgeWarning=function(e){var t=e[0]._private.rscratch;t.nodesOverlap||Ot(t.startX)&&Ot(t.startY)&&Ot(t.endX)&&Ot(t.endY)?t.loggedErr=!1:t.loggedErr||(t.loggedErr=!0,di("Edge `"+e.id()+"` has invalid endpoints and so it is impossible to draw.  Adjust your edge style (e.g. control points) accordingly or use an alternative edge type.  This is expected behaviour when the source node and the target node overlap."))};Ba.findEdgeControlPoints=function(e){var t=this;if(!(!e||e.length===0)){for(var r=this,i=r.cy,n=i.hasCompoundNodes(),a=new Ku,s=o(function(N,P){return[].concat(N3(N),[P?1:0]).join("-")},"getKey"),l=[],u=[],h=0;h<e.length;h++){var d=e[h],f=d._private,p=d.pstyle("curve-style").value;if(!(d.removed()||!d.takesUpSpace())){if(p==="haystack"){u.push(d);continue}var m=p==="unbundled-bezier"||$d(p,"segments")||p==="straight"||p==="straight-triangle"||$d(p,"taxi"),g=p==="unbundled-bezier"||p==="bezier",y=f.source,v=f.target,x=y.poolIndex(),b=v.poolIndex(),T=[x,b].sort(),w=s(T,m),C=a.get(w);C==null&&(C={eles:[]},l.push({pairId:T,edgeIsUnbundled:m}),a.set(w,C)),C.eles.push(d),m&&(C.hasUnbundled=!0),g&&(C.hasBezier=!0)}}for(var k=o(function(){var N=l[E],P=N.pairId,I=N.edgeIsUnbundled,D=s(P,I),_=a.get(D),R;if(!_.hasUnbundled){var M=_.eles[0].parallelEdges().filter(function(ie){return ie.isBundledBezier()});gP(_.eles),M.forEach(function(ie){return _.eles.push(ie)}),_.eles.sort(function(ie,fe){return ie.poolIndex()-fe.poolIndex()})}var L=_.eles[0],B=L.source(),O=L.target();if(B.poolIndex()>O.poolIndex()){var $=B;B=O,O=$}var G=_.srcPos=B.position(),F=_.tgtPos=O.position(),V=_.srcW=B.outerWidth(),H=_.srcH=B.outerHeight(),j=_.tgtW=O.outerWidth(),U=_.tgtH=O.outerHeight(),Q=_.srcShape=r.nodeShapes[t.getNodeShape(B)],Y=_.tgtShape=r.nodeShapes[t.getNodeShape(O)],ae=_.srcCornerRadius=B.pstyle("corner-radius").value==="auto"?"auto":B.pstyle("corner-radius").pfValue,J=_.tgtCornerRadius=O.pstyle("corner-radius").value==="auto"?"auto":O.pstyle("corner-radius").pfValue,te=_.tgtRs=O._private.rscratch,re=_.srcRs=B._private.rscratch;_.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var ee=0;ee<_.eles.length;ee++){var Te=_.eles[ee],ue=Te[0]._private.rscratch,De=Te.pstyle("curve-style").value,Ie=De==="unbundled-bezier"||$d(De,"segments")||$d(De,"taxi"),Ee=!B.same(Te.source());if(!_.calculatedIntersection&&B!==O&&(_.hasBezier||_.hasUnbundled)){_.calculatedIntersection=!0;var we=Q.intersectLine(G.x,G.y,V,H,F.x,F.y,0,ae,re),Me=_.srcIntn=we,$e=Y.intersectLine(F.x,F.y,j,U,G.x,G.y,0,J,te),de=_.tgtIntn=$e,rt=_.intersectionPts={x1:we[0],x2:$e[0],y1:we[1],y2:$e[1]},ke=_.posPts={x1:G.x,x2:F.x,y1:G.y,y2:F.y},Fe=$e[1]-we[1],He=$e[0]-we[0],at=Math.sqrt(He*He+Fe*Fe);Ot(at)&&at>=hst||(at=Math.sqrt(Math.max(He*He,t2)+Math.max(Fe*Fe,t2)));var qe=_.vector={x:He,y:Fe},Ue=_.vectorNorm={x:qe.x/at,y:qe.y/at},ye={x:-Ue.y,y:Ue.x};_.nodesOverlap=!Ot(at)||Y.checkPoint(we[0],we[1],0,j,U,F.x,F.y,J,te)||Q.checkPoint($e[0],$e[1],0,V,H,G.x,G.y,ae,re),_.vectorNormInverse=ye,R={nodesOverlap:_.nodesOverlap,dirCounts:_.dirCounts,calculatedIntersection:!0,hasBezier:_.hasBezier,hasUnbundled:_.hasUnbundled,eles:_.eles,srcPos:F,srcRs:te,tgtPos:G,tgtRs:re,srcW:j,srcH:U,tgtW:V,tgtH:H,srcIntn:de,tgtIntn:Me,srcShape:Y,tgtShape:Q,posPts:{x1:ke.x2,y1:ke.y2,x2:ke.x1,y2:ke.y1},intersectionPts:{x1:rt.x2,y1:rt.y2,x2:rt.x1,y2:rt.y1},vector:{x:-qe.x,y:-qe.y},vectorNorm:{x:-Ue.x,y:-Ue.y},vectorNormInverse:{x:-ye.x,y:-ye.y}}}var ve=Ee?R:_;ue.nodesOverlap=ve.nodesOverlap,ue.srcIntn=ve.srcIntn,ue.tgtIntn=ve.tgtIntn,ue.isRound=De.startsWith("round"),n&&(B.isParent()||B.isChild()||O.isParent()||O.isChild())&&(B.parents().anySame(O)||O.parents().anySame(B)||B.same(O)&&B.isParent())?t.findCompoundLoopPoints(Te,ve,ee,Ie):B===O?t.findLoopPoints(Te,ve,ee,Ie):De.endsWith("segments")?t.findSegmentsPoints(Te,ve):De.endsWith("taxi")?t.findTaxiPoints(Te,ve):De==="straight"||!Ie&&_.eles.length%2===1&&ee===Math.floor(_.eles.length/2)?t.findStraightEdgePoints(Te):t.findBezierPoints(Te,ve,ee,Ie,Ee),t.findEndpoints(Te),t.tryToCorrectInvalidPoints(Te,ve),t.checkForInvalidEdgeWarning(Te),t.storeAllpts(Te),t.storeEdgeProjections(Te),t.calculateArrowAngles(Te),t.recalculateEdgeLabelProjections(Te),t.calculateLabelAngles(Te)}},"_loop"),E=0;E<l.length;E++)k();this.findHaystackPoints(u)}};o(lge,"getPts");Ba.getSegmentPoints=function(e){var t=e[0]._private.rscratch;this.recalculateRenderedStyle(e);var r=t.edgeType;if(r==="segments")return lge(t.segpts)};Ba.getControlPoints=function(e){var t=e[0]._private.rscratch;this.recalculateRenderedStyle(e);var r=t.edgeType;if(r==="bezier"||r==="multibezier"||r==="self"||r==="compound")return lge(t.ctrlpts)};Ba.getEdgeMidpoint=function(e){var t=e[0]._private.rscratch;return this.recalculateRenderedStyle(e),{x:t.midX,y:t.midY}};h2={};h2.manualEndptToPx=function(e,t){var r=this,i=e.position(),n=e.outerWidth(),a=e.outerHeight(),s=e._private.rscratch;if(t.value.length===2){var l=[t.pfValue[0],t.pfValue[1]];return t.units[0]==="%"&&(l[0]=l[0]*n),t.units[1]==="%"&&(l[1]=l[1]*a),l[0]+=i.x,l[1]+=i.y,l}else{var u=t.pfValue[0];u=-Math.PI/2+u;var h=2*Math.max(n,a),d=[i.x+Math.cos(u)*h,i.y+Math.sin(u)*h];return r.nodeShapes[this.getNodeShape(e)].intersectLine(i.x,i.y,n,a,d[0],d[1],0,e.pstyle("corner-radius").value==="auto"?"auto":e.pstyle("corner-radius").pfValue,s)}};h2.findEndpoints=function(e){var t,r,i,n,a=this,s,l=e.source()[0],u=e.target()[0],h=l.position(),d=u.position(),f=e.pstyle("target-arrow-shape").value,p=e.pstyle("source-arrow-shape").value,m=e.pstyle("target-distance-from-node").pfValue,g=e.pstyle("source-distance-from-node").pfValue,y=l._private.rscratch,v=u._private.rscratch,x=e.pstyle("curve-style").value,b=e._private.rscratch,T=b.edgeType,w=$d(x,"taxi"),C=T==="self"||T==="compound",k=T==="bezier"||T==="multibezier"||C,E=T!=="bezier",A=T==="straight"||T==="segments",N=T==="segments",P=k||E||A,I=C||w,D=e.pstyle("source-endpoint"),_=I?"outside-to-node":D.value,R=l.pstyle("corner-radius").value==="auto"?"auto":l.pstyle("corner-radius").pfValue,M=e.pstyle("target-endpoint"),L=I?"outside-to-node":M.value,B=u.pstyle("corner-radius").value==="auto"?"auto":u.pstyle("corner-radius").pfValue;b.srcManEndpt=D,b.tgtManEndpt=M;var O,$,G,F,V=(t=(M==null||(r=M.pfValue)===null||r===void 0?void 0:r.length)===2?M.pfValue:null)!==null&&t!==void 0?t:[0,0],H=(i=(D==null||(n=D.pfValue)===null||n===void 0?void 0:n.length)===2?D.pfValue:null)!==null&&i!==void 0?i:[0,0];if(k){var j=[b.ctrlpts[0],b.ctrlpts[1]],U=E?[b.ctrlpts[b.ctrlpts.length-2],b.ctrlpts[b.ctrlpts.length-1]]:j;O=U,$=j}else if(A){var Q=N?b.segpts.slice(0,2):[d.x+V[0],d.y+V[1]],Y=N?b.segpts.slice(b.segpts.length-2):[h.x+H[0],h.y+H[1]];O=Y,$=Q}if(L==="inside-to-node")s=[d.x,d.y];else if(M.units)s=this.manualEndptToPx(u,M);else if(L==="outside-to-line")s=b.tgtIntn;else if(L==="outside-to-node"||L==="outside-to-node-or-label"?G=O:(L==="outside-to-line"||L==="outside-to-line-or-label")&&(G=[h.x,h.y]),s=a.nodeShapes[this.getNodeShape(u)].intersectLine(d.x,d.y,u.outerWidth(),u.outerHeight(),G[0],G[1],0,B,v),L==="outside-to-node-or-label"||L==="outside-to-line-or-label"){var ae=u._private.rscratch,J=ae.labelWidth,te=ae.labelHeight,re=ae.labelX,ee=ae.labelY,Te=J/2,ue=te/2,De=u.pstyle("text-valign").value;De==="top"?ee-=ue:De==="bottom"&&(ee+=ue);var Ie=u.pstyle("text-halign").value;Ie==="left"?re-=Te:Ie==="right"&&(re+=Te);var Ee=Kb(G[0],G[1],[re-Te,ee-ue,re+Te,ee-ue,re+Te,ee+ue,re-Te,ee+ue],d.x,d.y);if(Ee.length>0){var we=h,Me=jp(we,o1(s)),$e=jp(we,o1(Ee)),de=Me;if($e<Me&&(s=Ee,de=$e),Ee.length>2){var rt=jp(we,{x:Ee[2],y:Ee[3]});rt<de&&(s=[Ee[2],Ee[3]])}}}var ke=d3(s,O,a.arrowShapes[f].spacing(e)+m),Fe=d3(s,O,a.arrowShapes[f].gap(e)+m);if(b.endX=Fe[0],b.endY=Fe[1],b.arrowEndX=ke[0],b.arrowEndY=ke[1],_==="inside-to-node")s=[h.x,h.y];else if(D.units)s=this.manualEndptToPx(l,D);else if(_==="outside-to-line")s=b.srcIntn;else if(_==="outside-to-node"||_==="outside-to-node-or-label"?F=$:(_==="outside-to-line"||_==="outside-to-line-or-label")&&(F=[d.x,d.y]),s=a.nodeShapes[this.getNodeShape(l)].intersectLine(h.x,h.y,l.outerWidth(),l.outerHeight(),F[0],F[1],0,R,y),_==="outside-to-node-or-label"||_==="outside-to-line-or-label"){var He=l._private.rscratch,at=He.labelWidth,qe=He.labelHeight,Ue=He.labelX,ye=He.labelY,ve=at/2,ie=qe/2,fe=l.pstyle("text-valign").value;fe==="top"?ye-=ie:fe==="bottom"&&(ye+=ie);var W=l.pstyle("text-halign").value;W==="left"?Ue-=ve:W==="right"&&(Ue+=ve);var ce=Kb(F[0],F[1],[Ue-ve,ye-ie,Ue+ve,ye-ie,Ue+ve,ye+ie,Ue-ve,ye+ie],h.x,h.y);if(ce.length>0){var K=d,Re=jp(K,o1(s)),xe=jp(K,o1(ce)),Oe=Re;if(xe<Re&&(s=[ce[0],ce[1]],Oe=xe),ce.length>2){var be=jp(K,{x:ce[2],y:ce[3]});be<Oe&&(s=[ce[2],ce[3]])}}}var Be=d3(s,$,a.arrowShapes[p].spacing(e)+g),Ae=d3(s,$,a.arrowShapes[p].gap(e)+g);b.startX=Ae[0],b.startY=Ae[1],b.arrowStartX=Be[0],b.arrowStartY=Be[1],P&&(!Ot(b.startX)||!Ot(b.startY)||!Ot(b.endX)||!Ot(b.endY)?b.badLine=!0:b.badLine=!1)};h2.getSourceEndpoint=function(e){var t=e[0]._private.rscratch;switch(this.recalculateRenderedStyle(e),t.edgeType){case"haystack":return{x:t.haystackPts[0],y:t.haystackPts[1]};default:return{x:t.arrowStartX,y:t.arrowStartY}}};h2.getTargetEndpoint=function(e){var t=e[0]._private.rscratch;switch(this.recalculateRenderedStyle(e),t.edgeType){case"haystack":return{x:t.haystackPts[2],y:t.haystackPts[3]};default:return{x:t.arrowEndX,y:t.arrowEndY}}};MP={};o(dst,"pushBezierPts");MP.storeEdgeProjections=function(e){var t=e._private,r=t.rscratch,i=r.edgeType;if(t.rstyle.bezierPts=null,t.rstyle.linePts=null,t.rstyle.haystackPts=null,i==="multibezier"||i==="bezier"||i==="self"||i==="compound"){t.rstyle.bezierPts=[];for(var n=0;n+5<r.allpts.length;n+=4)dst(this,e,r.allpts.slice(n,n+6))}else if(i==="segments")for(var a=t.rstyle.linePts=[],n=0;n+1<r.allpts.length;n+=2)a.push({x:r.allpts[n],y:r.allpts[n+1]});else if(i==="haystack"){var s=r.haystackPts;t.rstyle.haystackPts=[{x:s[0],y:s[1]},{x:s[2],y:s[3]}]}t.rstyle.arrowWidth=this.getArrowWidth(e.pstyle("width").pfValue,e.pstyle("arrow-scale").value)*this.arrowShapeWidth};MP.recalculateEdgeProjections=function(e){this.findEdgeControlPoints(e)};$c={};$c.recalculateNodeLabelProjection=function(e){var t=e.pstyle("label").strValue;if(!qd(t)){var r,i,n=e._private,a=e.width(),s=e.height(),l=e.padding(),u=e.position(),h=e.pstyle("text-halign").strValue,d=e.pstyle("text-valign").strValue,f=n.rscratch,p=n.rstyle;switch(h){case"left":r=u.x-a/2-l;break;case"right":r=u.x+a/2+l;break;default:r=u.x}switch(d){case"top":i=u.y-s/2-l;break;case"bottom":i=u.y+s/2+l;break;default:i=u.y}f.labelX=r,f.labelY=i,p.labelX=r,p.labelY=i,this.calculateLabelAngles(e),this.applyLabelDimensions(e)}};cge=o(function(t,r){var i=Math.atan(r/t);return t===0&&i<0&&(i=i*-1),i},"lineAngleFromDelta"),uge=o(function(t,r){var i=r.x-t.x,n=r.y-t.y;return cge(i,n)},"lineAngle"),fst=o(function(t,r,i,n){var a=Xb(0,n-.001,1),s=Xb(0,n+.001,1),l=u1(t,r,i,a),u=u1(t,r,i,s);return uge(l,u)},"bezierAngle");$c.recalculateEdgeLabelProjections=function(e){var t,r=e._private,i=r.rscratch,n=this,a={mid:e.pstyle("label").strValue,source:e.pstyle("source-label").strValue,target:e.pstyle("target-label").strValue};if(a.mid||a.source||a.target){t={x:i.midX,y:i.midY};var s=o(function(f,p,m){Xu(r.rscratch,f,p,m),Xu(r.rstyle,f,p,m)},"setRs");s("labelX",null,t.x),s("labelY",null,t.y);var l=cge(i.midDispX,i.midDispY);s("labelAutoAngle",null,l);var u=o(function(){if(u.cache)return u.cache;for(var f=[],p=0;p+5<i.allpts.length;p+=4){var m={x:i.allpts[p],y:i.allpts[p+1]},g={x:i.allpts[p+2],y:i.allpts[p+3]},y={x:i.allpts[p+4],y:i.allpts[p+5]};f.push({p0:m,p1:g,p2:y,startDist:0,length:0,segments:[]})}var v=r.rstyle.bezierPts,x=n.bezierProjPcts.length;function b(E,A,N,P,I){var D=e0(A,N),_=E.segments[E.segments.length-1],R={p0:A,p1:N,t0:P,t1:I,startDist:_?_.startDist+_.length:0,length:D};E.segments.push(R),E.length+=D}o(b,"addSegment");for(var T=0;T<f.length;T++){var w=f[T],C=f[T-1];C&&(w.startDist=C.startDist+C.length),b(w,w.p0,v[T*x],0,n.bezierProjPcts[0]);for(var k=0;k<x-1;k++)b(w,v[T*x+k],v[T*x+k+1],n.bezierProjPcts[k],n.bezierProjPcts[k+1]);b(w,v[T*x+x-1],w.p2,n.bezierProjPcts[x-1],1)}return u.cache=f},"createControlPointInfo"),h=o(function(f){var p,m=f==="source";if(a[f]){var g=e.pstyle(f+"-text-offset").pfValue;switch(i.edgeType){case"self":case"compound":case"bezier":case"multibezier":{for(var y=u(),v,x=0,b=0,T=0;T<y.length;T++){for(var w=y[m?T:y.length-1-T],C=0;C<w.segments.length;C++){var k=w.segments[m?C:w.segments.length-1-C],E=T===y.length-1&&C===w.segments.length-1;if(x=b,b+=k.length,b>=g||E){v={cp:w,segment:k};break}}if(v)break}var A=v.cp,N=v.segment,P=(g-x)/N.length,I=N.t1-N.t0,D=m?N.t0+I*P:N.t1-I*P;D=Xb(0,D,1),t=u1(A.p0,A.p1,A.p2,D),p=fst(A.p0,A.p1,A.p2,D);break}case"straight":case"segments":case"haystack":{for(var _=0,R,M,L,B,O=i.allpts.length,$=0;$+3<O&&(m?(L={x:i.allpts[$],y:i.allpts[$+1]},B={x:i.allpts[$+2],y:i.allpts[$+3]}):(L={x:i.allpts[O-2-$],y:i.allpts[O-1-$]},B={x:i.allpts[O-4-$],y:i.allpts[O-3-$]}),R=e0(L,B),M=_,_+=R,!(_>=g));$+=2);var G=g-M,F=G/R;F=Xb(0,F,1),t=Ort(L,B,F),p=uge(L,B);break}}s("labelX",f,t.x),s("labelY",f,t.y),s("labelAutoAngle",f,p)}},"calculateEndProjection");h("source"),h("target"),this.applyLabelDimensions(e)}};$c.applyLabelDimensions=function(e){this.applyPrefixedLabelDimensions(e),e.isEdge()&&(this.applyPrefixedLabelDimensions(e,"source"),this.applyPrefixedLabelDimensions(e,"target"))};$c.applyPrefixedLabelDimensions=function(e,t){var r=e._private,i=this.getLabelText(e,t),n=Jp(i,e._private.labelDimsKey);if(Ps(r.rscratch,"prefixedLabelDimsKey",t)!==n){Xu(r.rscratch,"prefixedLabelDimsKey",t,n);var a=this.calculateLabelDimensions(e,i),s=e.pstyle("line-height").pfValue,l=e.pstyle("text-wrap").strValue,u=Ps(r.rscratch,"labelWrapCachedLines",t)||[],h=l!=="wrap"?1:Math.max(u.length,1),d=a.height/h,f=d*s,p=a.width,m=a.height+(h-1)*(s-1)*d;Xu(r.rstyle,"labelWidth",t,p),Xu(r.rscratch,"labelWidth",t,p),Xu(r.rstyle,"labelHeight",t,m),Xu(r.rscratch,"labelHeight",t,m),Xu(r.rscratch,"labelLineHeight",t,f)}};$c.getLabelText=function(e,t){var r=e._private,i=t?t+"-":"",n=e.pstyle(i+"label").strValue,a=e.pstyle("text-transform").value,s=o(function(H,j){return j?(Xu(r.rscratch,H,t,j),j):Ps(r.rscratch,H,t)},"rscratch");if(!n)return"";a=="none"||(a=="uppercase"?n=n.toUpperCase():a=="lowercase"&&(n=n.toLowerCase()));var l=e.pstyle("text-wrap").value;if(l==="wrap"){var u=s("labelKey");if(u!=null&&s("labelWrapKey")===u)return s("labelWrapCachedText");for(var h="\u200B",d=n.split(`
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Pe=Ve("cxttapend");if(Be?Be.emit(Pe):K.emit(Pe),!e.hoverData.cxtDragged){var Ye=Ve("cxttap");Be?Be.emit(Ye):K.emit(Ye)}e.hoverData.cxtDragged=!1,e.hoverData.which=null}else 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le=K.collection(e.getAllInBox(xe[0],xe[1],xe[2],xe[3]));e.redrawHint("select",!0),le.length>0&&e.redrawHint("eles",!0),K.emit(Ve("boxend"));var st=o(function(kt){return kt.selectable()&&!kt.selected()},"eleWouldBeSelected");K.selectionType()==="additive"||Ae||K.$(r).unmerge(le).unselect(),le.emit(Ve("box")).stdFilter(st).select().emit(Ve("boxselect")),e.redraw()}if(e.hoverData.dragging&&(e.hoverData.dragging=!1,e.redrawHint("select",!0),e.redrawHint("eles",!0),e.redraw()),!xe[4]){e.redrawHint("drag",!0),e.redrawHint("eles",!0);var me=Be&&Be.grabbed();x(be),me&&(Be.emit(Ve("freeon")),be.emit(Ve("free")),e.dragData.didDrag&&(Be.emit(Ve("dragfreeon")),be.emit(Ve("dragfree"))))}}xe[4]=0,e.hoverData.down=null,e.hoverData.cxtStarted=!1,e.hoverData.draggingEles=!1,e.hoverData.selecting=!1,e.hoverData.isOverThresholdDrag=!1,e.dragData.didDrag=!1,e.hoverData.dragged=!1,e.hoverData.dragDelta=[],e.hoverData.mdownPos=null,e.hoverData.mdownGPos=null,e.hoverData.which=null}}},"mouseupHandler"),!1);var M=[],L=4,B,O=1e5,$=o(function(W,ce){for(var K=0;K<W.length;K++)if(W[K]%ce!==0)return!1;return!0},"allAreDivisibleBy"),G=o(function(W){for(var ce=Math.abs(W[0]),K=1;K<W.length;K++)if(Math.abs(W[K])!==ce)return!1;return!0},"allAreSameMagnitude"),F=o(function(W){var ce=!1,K=W.deltaY;if(K==null&&(W.wheelDeltaY!=null?K=W.wheelDeltaY/4:W.wheelDelta!=null&&(K=W.wheelDelta/4)),K!==0){if(B==null)if(M.length>=L){var Re=M;if(B=$(Re,5),!B){var xe=Math.abs(Re[0]);B=G(Re)&&xe>5}if(B)for(var Oe=0;Oe<Re.length;Oe++)O=Math.min(Math.abs(Re[Oe]),O)}else M.push(K),ce=!0;else B&&(O=Math.min(Math.abs(K),O));if(!e.scrollingPage){var be=e.cy,Be=be.zoom(),Ae=be.pan(),Ve=e.projectIntoViewport(W.clientX,W.clientY),Pe=[Ve[0]*Be+Ae.x,Ve[1]*Be+Ae.y];if(e.hoverData.draggingEles||e.hoverData.dragging||e.hoverData.cxtStarted||N()){W.preventDefault();return}if(be.panningEnabled()&&be.userPanningEnabled()&&be.zoomingEnabled()&&be.userZoomingEnabled()){W.preventDefault(),e.data.wheelZooming=!0,clearTimeout(e.data.wheelTimeout),e.data.wheelTimeout=setTimeout(function(){e.data.wheelZooming=!1,e.redrawHint("eles",!0),e.redraw()},150);var Ye;ce&&Math.abs(K)>5&&(K=vP(K)*5),Ye=K/-250,B&&(Ye/=O,Ye*=3),Ye=Ye*e.wheelSensitivity;var le=W.deltaMode===1;le&&(Ye*=33);var st=be.zoom()*Math.pow(10,Ye);W.type==="gesturechange"&&(st=e.gestureStartZoom*W.scale),be.zoom({level:st,renderedPosition:{x:Pe[0],y:Pe[1]}}),be.emit({type:W.type==="gesturechange"?"pinchzoom":"scrollzoom",originalEvent:W,position:{x:Ve[0],y:Ve[1]}})}}}},"wheelHandler");e.registerBinding(e.container,"wheel",F,!0),e.registerBinding(t,"scroll",o(function(W){e.scrollingPage=!0,clearTimeout(e.scrollingPageTimeout),e.scrollingPageTimeout=setTimeout(function(){e.scrollingPage=!1},250)},"scrollHandler"),!0),e.registerBinding(e.container,"gesturestart",o(function(W){e.gestureStartZoom=e.cy.zoom(),e.hasTouchStarted||W.preventDefault()},"gestureStartHandler"),!0),e.registerBinding(e.container,"gesturechange",function(fe){e.hasTouchStarted||F(fe)},!0),e.registerBinding(e.container,"mouseout",o(function(W){var ce=e.projectIntoViewport(W.clientX,W.clientY);e.cy.emit({originalEvent:W,type:"mouseout",position:{x:ce[0],y:ce[1]}})},"mouseOutHandler"),!1),e.registerBinding(e.container,"mouseover",o(function(W){var ce=e.projectIntoViewport(W.clientX,W.clientY);e.cy.emit({originalEvent:W,type:"mouseover",position:{x:ce[0],y:ce[1]}})},"mouseOverHandler"),!1);var V,H,j,U,Q,Y,ae,J,te,re,ee,Te,ue,De=o(function(W,ce,K,Re){return Math.sqrt((K-W)*(K-W)+(Re-ce)*(Re-ce))},"distance"),Ie=o(function(W,ce,K,Re){return(K-W)*(K-W)+(Re-ce)*(Re-ce)},"distanceSq"),Ee;e.registerBinding(e.container,"touchstart",Ee=o(function(W){if(e.hasTouchStarted=!0,!!P(W)){T(),e.touchData.capture=!0,e.data.bgActivePosistion=void 0;var ce=e.cy,K=e.touchData.now,Re=e.touchData.earlier;if(W.touches[0]){var xe=e.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);K[0]=xe[0],K[1]=xe[1]}if(W.touches[1]){var xe=e.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);K[2]=xe[0],K[3]=xe[1]}if(W.touches[2]){var xe=e.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);K[4]=xe[0],K[5]=xe[1]}var Oe=o(function(oe){return{originalEvent:W,type:oe,position:{x:K[0],y:K[1]}}},"makeEvent");if(W.touches[1]){e.touchData.singleTouchMoved=!0,x(e.dragData.touchDragEles);var be=e.findContainerClientCoords();te=be[0],re=be[1],ee=be[2],Te=be[3],V=W.touches[0].clientX-te,H=W.touches[0].clientY-re,j=W.touches[1].clientX-te,U=W.touches[1].clientY-re,ue=0<=V&&V<=ee&&0<=j&&j<=ee&&0<=H&&H<=Te&&0<=U&&U<=Te;var Be=ce.pan(),Ae=ce.zoom();Q=De(V,H,j,U),Y=Ie(V,H,j,U),ae=[(V+j)/2,(H+U)/2],J=[(ae[0]-Be.x)/Ae,(ae[1]-Be.y)/Ae];var Ve=200,Pe=Ve*Ve;if(Y<Pe&&!W.touches[2]){var Ye=e.findNearestElement(K[0],K[1],!0,!0),le=e.findNearestElement(K[2],K[3],!0,!0);Ye&&Ye.isNode()?(Ye.activate().emit(Oe("cxttapstart")),e.touchData.start=Ye):le&&le.isNode()?(le.activate().emit(Oe("cxttapstart")),e.touchData.start=le):ce.emit(Oe("cxttapstart")),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!0,e.touchData.cxtDragged=!1,e.data.bgActivePosistion=void 0,e.redraw();return}}if(W.touches[2])ce.boxSelectionEnabled()&&W.preventDefault();else if(!W.touches[1]){if(W.touches[0]){var st=e.findNearestElements(K[0],K[1],!0,!0),me=st[0];if(me!=null&&(me.activate(),e.touchData.start=me,e.touchData.starts=st,e.nodeIsGrabbable(me))){var ot=e.dragData.touchDragEles=ce.collection(),kt=null;e.redrawHint("eles",!0),e.redrawHint("drag",!0),me.selected()?(kt=ce.$(function(yt){return yt.selected()&&e.nodeIsGrabbable(yt)}),y(kt,{addToList:ot})):v(me,{addToList:ot}),f(me),me.emit(Oe("grabon")),kt?kt.forEach(function(yt){yt.emit(Oe("grab"))}):me.emit(Oe("grab"))}n(me,["touchstart","tapstart","vmousedown"],W,{x:K[0],y:K[1]}),me==null&&(e.data.bgActivePosistion={x:xe[0],y:xe[1]},e.redrawHint("select",!0),e.redraw()),e.touchData.singleTouchMoved=!1,e.touchData.singleTouchStartTime=+new Date,clearTimeout(e.touchData.tapholdTimeout),e.touchData.tapholdTimeout=setTimeout(function(){e.touchData.singleTouchMoved===!1&&!e.pinching&&!e.touchData.selecting&&n(e.touchData.start,["taphold"],W,{x:K[0],y:K[1]})},e.tapholdDuration)}}if(W.touches.length>=1){for(var Gt=e.touchData.startPosition=[null,null,null,null,null,null],Tt=0;Tt<K.length;Tt++)Gt[Tt]=Re[Tt]=K[Tt];var Et=W.touches[0];e.touchData.startGPosition=[Et.clientX,Et.clientY]}}},"touchstartHandler"),!1);var we;e.registerBinding(t,"touchmove",we=o(function(W){var ce=e.touchData.capture;if(!(!ce&&!P(W))){var K=e.selection,Re=e.cy,xe=e.touchData.now,Oe=e.touchData.earlier,be=Re.zoom();if(W.touches[0]){var Be=e.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);xe[0]=Be[0],xe[1]=Be[1]}if(W.touches[1]){var Be=e.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);xe[2]=Be[0],xe[3]=Be[1]}if(W.touches[2]){var Be=e.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);xe[4]=Be[0],xe[5]=Be[1]}var Ae=o(function(xC){return{originalEvent:W,type:xC,position:{x:xe[0],y:xe[1]}}},"makeEvent"),Ve=e.touchData.startGPosition,Pe;if(ce&&W.touches[0]&&Ve){for(var Ye=[],le=0;le<xe.length;le++)Ye[le]=xe[le]-Oe[le];var st=W.touches[0].clientX-Ve[0],me=st*st,ot=W.touches[0].clientY-Ve[1],kt=ot*ot,Gt=me+kt;Pe=Gt>=e.touchTapThreshold2}if(ce&&e.touchData.cxt){W.preventDefault();var Tt=W.touches[0].clientX-te,Et=W.touches[0].clientY-re,yt=W.touches[1].clientX-te,oe=W.touches[1].clientY-re,ht=Ie(Tt,Et,yt,oe),gt=ht/Y,et=150,pe=et*et,Qe=1.5,nt=Qe*Qe;if(gt>=nt||ht>=pe){e.touchData.cxt=!1,e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var dt=Ae("cxttapend");e.touchData.start?(e.touchData.start.unactivate().emit(dt),e.touchData.start=null):Re.emit(dt)}}if(ce&&e.touchData.cxt){var dt=Ae("cxtdrag");e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.touchData.start?e.touchData.start.emit(dt):Re.emit(dt),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxtDragged=!0;var Ft=e.findNearestElement(xe[0],xe[1],!0,!0);(!e.touchData.cxtOver||Ft!==e.touchData.cxtOver)&&(e.touchData.cxtOver&&e.touchData.cxtOver.emit(Ae("cxtdragout")),e.touchData.cxtOver=Ft,Ft&&Ft.emit(Ae("cxtdragover")))}else if(ce&&W.touches[2]&&Re.boxSelectionEnabled())W.preventDefault(),e.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,e.touchData.selecting||Re.emit(Ae("boxstart")),e.touchData.selecting=!0,e.touchData.didSelect=!0,K[4]=1,!K||K.length===0||K[0]===void 0?(K[0]=(xe[0]+xe[2]+xe[4])/3,K[1]=(xe[1]+xe[3]+xe[5])/3,K[2]=(xe[0]+xe[2]+xe[4])/3+1,K[3]=(xe[1]+xe[3]+xe[5])/3+1):(K[2]=(xe[0]+xe[2]+xe[4])/3,K[3]=(xe[1]+xe[3]+xe[5])/3),e.redrawHint("select",!0),e.redraw();else if(ce&&W.touches[1]&&!e.touchData.didSelect&&Re.zoomingEnabled()&&Re.panningEnabled()&&Re.userZoomingEnabled()&&Re.userPanningEnabled()){W.preventDefault(),e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var Rt=e.dragData.touchDragEles;if(Rt){e.redrawHint("drag",!0);for(var $t=0;$t<Rt.length;$t++){var lr=Rt[$t]._private;lr.grabbed=!1,lr.rscratch.inDragLayer=!1}}var pt=e.touchData.start,Tt=W.touches[0].clientX-te,Et=W.touches[0].clientY-re,yt=W.touches[1].clientX-te,oe=W.touches[1].clientY-re,Se=De(Tt,Et,yt,oe),it=Se/Q;if(ue){var xt=Tt-V,rr=Et-H,_r=yt-j,Ur=oe-U,Dr=(xt+_r)/2,Xi=(rr+Ur)/2,In=Re.zoom(),pi=In*it,zt=Re.pan(),Mt=J[0]*In+zt.x,qt=J[1]*In+zt.y,Qt={x:-pi/In*(Mt-zt.x-Dr)+Mt,y:-pi/In*(qt-zt.y-Xi)+qt};if(pt&&pt.active()){var Rt=e.dragData.touchDragEles;x(Rt),e.redrawHint("drag",!0),e.redrawHint("eles",!0),pt.unactivate().emit(Ae("freeon")),Rt.emit(Ae("free")),e.dragData.didDrag&&(pt.emit(Ae("dragfreeon")),Rt.emit(Ae("dragfree")))}Re.viewport({zoom:pi,pan:Qt,cancelOnFailedZoom:!0}),Re.emit(Ae("pinchzoom")),Q=Se,V=Tt,H=Et,j=yt,U=oe,e.pinching=!0}if(W.touches[0]){var Be=e.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);xe[0]=Be[0],xe[1]=Be[1]}if(W.touches[1]){var Be=e.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);xe[2]=Be[0],xe[3]=Be[1]}if(W.touches[2]){var Be=e.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);xe[4]=Be[0],xe[5]=Be[1]}}else if(W.touches[0]&&!e.touchData.didSelect){var _t=e.touchData.start,oi=e.touchData.last,Ft;if(!e.hoverData.draggingEles&&!e.swipePanning&&(Ft=e.findNearestElement(xe[0],xe[1],!0,!0)),ce&&_t!=null&&W.preventDefault(),ce&&_t!=null&&e.nodeIsDraggable(_t))if(Pe){var Rt=e.dragData.touchDragEles,Ce=!e.dragData.didDrag;Ce&&y(Rt,{inDragLayer:!0}),e.dragData.didDrag=!0;var Zs={x:0,y:0};if(Ot(Ye[0])&&Ot(Ye[1])&&(Zs.x+=Ye[0],Zs.y+=Ye[1],Ce)){e.redrawHint("eles",!0);var _e=e.touchData.dragDelta;_e&&Ot(_e[0])&&Ot(_e[1])&&(Zs.x+=_e[0],Zs.y+=_e[1])}e.hoverData.draggingEles=!0,Rt.silentShift(Zs).emit(Ae("position")).emit(Ae("drag")),e.redrawHint("drag",!0),e.touchData.startPosition[0]==Oe[0]&&e.touchData.startPosition[1]==Oe[1]&&e.redrawHint("eles",!0),e.redraw()}else{var _e=e.touchData.dragDelta=e.touchData.dragDelta||[];_e.length===0?(_e.push(Ye[0]),_e.push(Ye[1])):(_e[0]+=Ye[0],_e[1]+=Ye[1])}if(n(_t||Ft,["touchmove","tapdrag","vmousemove"],W,{x:xe[0],y:xe[1]}),(!_t||!_t.grabbed())&&Ft!=oi&&(oi&&oi.emit(Ae("tapdragout")),Ft&&Ft.emit(Ae("tapdragover"))),e.touchData.last=Ft,ce)for(var $t=0;$t<xe.length;$t++)xe[$t]&&e.touchData.startPosition[$t]&&Pe&&(e.touchData.singleTouchMoved=!0);if(ce&&(_t==null||_t.pannable())&&Re.panningEnabled()&&Re.userPanningEnabled()){var an=s(_t,e.touchData.starts);an&&(W.preventDefault(),e.data.bgActivePosistion||(e.data.bgActivePosistion=o1(e.touchData.startPosition)),e.swipePanning?(Re.panBy({x:Ye[0]*be,y:Ye[1]*be}),Re.emit(Ae("dragpan"))):Pe&&(e.swipePanning=!0,Re.panBy({x:st*be,y:ot*be}),Re.emit(Ae("dragpan")),_t&&(_t.unactivate(),e.redrawHint("select",!0),e.touchData.start=null)));var Be=e.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);xe[0]=Be[0],xe[1]=Be[1]}}for(var le=0;le<xe.length;le++)Oe[le]=xe[le];ce&&W.touches.length>0&&!e.hoverData.draggingEles&&!e.swipePanning&&e.data.bgActivePosistion!=null&&(e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.redraw())}},"touchmoveHandler"),!1);var Me;e.registerBinding(t,"touchcancel",Me=o(function(W){var ce=e.touchData.start;e.touchData.capture=!1,ce&&ce.unactivate()},"touchcancelHandler"));var $e,de,rt,ke;if(e.registerBinding(t,"touchend",$e=o(function(W){var ce=e.touchData.start,K=e.touchData.capture;if(K)W.touches.length===0&&(e.touchData.capture=!1),W.preventDefault();else return;var Re=e.selection;e.swipePanning=!1,e.hoverData.draggingEles=!1;var xe=e.cy,Oe=xe.zoom(),be=e.touchData.now,Be=e.touchData.earlier;if(W.touches[0]){var Ae=e.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);be[0]=Ae[0],be[1]=Ae[1]}if(W.touches[1]){var Ae=e.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);be[2]=Ae[0],be[3]=Ae[1]}if(W.touches[2]){var Ae=e.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);be[4]=Ae[0],be[5]=Ae[1]}var Ve=o(function(pe){return{originalEvent:W,type:pe,position:{x:be[0],y:be[1]}}},"makeEvent");ce&&ce.unactivate();var Pe;if(e.touchData.cxt){if(Pe=Ve("cxttapend"),ce?ce.emit(Pe):xe.emit(Pe),!e.touchData.cxtDragged){var Ye=Ve("cxttap");ce?ce.emit(Ye):xe.emit(Ye)}e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!1,e.touchData.start=null,e.redraw();return}if(!W.touches[2]&&xe.boxSelectionEnabled()&&e.touchData.selecting){e.touchData.selecting=!1;var le=xe.collection(e.getAllInBox(Re[0],Re[1],Re[2],Re[3]));Re[0]=void 0,Re[1]=void 0,Re[2]=void 0,Re[3]=void 0,Re[4]=0,e.redrawHint("select",!0),xe.emit(Ve("boxend"));var st=o(function(pe){return pe.selectable()&&!pe.selected()},"eleWouldBeSelected");le.emit(Ve("box")).stdFilter(st).select().emit(Ve("boxselect")),le.nonempty()&&e.redrawHint("eles",!0),e.redraw()}if(ce?.unactivate(),W.touches[2])e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);else if(!W.touches[1]){if(!W.touches[0]){if(!W.touches[0]){e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var me=e.dragData.touchDragEles;if(ce!=null){var ot=ce._private.grabbed;x(me),e.redrawHint("drag",!0),e.redrawHint("eles",!0),ot&&(ce.emit(Ve("freeon")),me.emit(Ve("free")),e.dragData.didDrag&&(ce.emit(Ve("dragfreeon")),me.emit(Ve("dragfree")))),n(ce,["touchend","tapend","vmouseup","tapdragout"],W,{x:be[0],y:be[1]}),ce.unactivate(),e.touchData.start=null}else{var kt=e.findNearestElement(be[0],be[1],!0,!0);n(kt,["touchend","tapend","vmouseup","tapdragout"],W,{x:be[0],y:be[1]})}var Gt=e.touchData.startPosition[0]-be[0],Tt=Gt*Gt,Et=e.touchData.startPosition[1]-be[1],yt=Et*Et,oe=Tt+yt,ht=oe*Oe*Oe;e.touchData.singleTouchMoved||(ce||xe.$(":selected").unselect(["tapunselect"]),n(ce,["tap","vclick"],W,{x:be[0],y:be[1]}),de=!1,W.timeStamp-ke<=xe.multiClickDebounceTime()?(rt&&clearTimeout(rt),de=!0,ke=null,n(ce,["dbltap","vdblclick"],W,{x:be[0],y:be[1]})):(rt=setTimeout(function(){de||n(ce,["onetap","voneclick"],W,{x:be[0],y:be[1]})},xe.multiClickDebounceTime()),ke=W.timeStamp)),ce!=null&&!e.dragData.didDrag&&ce._private.selectable&&ht<e.touchTapThreshold2&&!e.pinching&&(xe.selectionType()==="single"?(xe.$(r).unmerge(ce).unselect(["tapunselect"]),ce.select(["tapselect"])):ce.selected()?ce.unselect(["tapunselect"]):ce.select(["tapselect"]),e.redrawHint("eles",!0)),e.touchData.singleTouchMoved=!0}}}for(var gt=0;gt<be.length;gt++)Be[gt]=be[gt];e.dragData.didDrag=!1,W.touches.length===0&&(e.touchData.dragDelta=[],e.touchData.startPosition=[null,null,null,null,null,null],e.touchData.startGPosition=null,e.touchData.didSelect=!1),W.touches.length<2&&(W.touches.length===1&&(e.touchData.startGPosition=[W.touches[0].clientX,W.touches[0].clientY]),e.pinching=!1,e.redrawHint("eles",!0),e.redraw())},"touchendHandler"),!1),typeof TouchEvent>"u"){var Fe=[],He=o(function(W){return{clientX:W.clientX,clientY:W.clientY,force:1,identifier:W.pointerId,pageX:W.pageX,pageY:W.pageY,radiusX:W.width/2,radiusY:W.height/2,screenX:W.screenX,screenY:W.screenY,target:W.target}},"makeTouch"),at=o(function(W){return{event:W,touch:He(W)}},"makePointer"),qe=o(function(W){Fe.push(at(W))},"addPointer"),Ue=o(function(W){for(var ce=0;ce<Fe.length;ce++){var K=Fe[ce];if(K.event.pointerId===W.pointerId){Fe.splice(ce,1);return}}},"removePointer"),ye=o(function(W){var ce=Fe.filter(function(K){return K.event.pointerId===W.pointerId})[0];ce.event=W,ce.touch=He(W)},"updatePointer"),ve=o(function(W){W.touches=Fe.map(function(ce){return ce.touch})},"addTouchesToEvent"),ie=o(function(W){return W.pointerType==="mouse"||W.pointerType===4},"pointerIsMouse");e.registerBinding(e.container,"pointerdown",function(fe){ie(fe)||(fe.preventDefault(),qe(fe),ve(fe),Ee(fe))}),e.registerBinding(e.container,"pointerup",function(fe){ie(fe)||(Ue(fe),ve(fe),$e(fe))}),e.registerBinding(e.container,"pointercancel",function(fe){ie(fe)||(Ue(fe),ve(fe),Me(fe))}),e.registerBinding(e.container,"pointermove",function(fe){ie(fe)||(fe.preventDefault(),ye(fe),ve(fe),we(fe))})}};Ju={};Ju.generatePolygon=function(e,t){return this.nodeShapes[e]={renderer:this,name:e,points:t,draw:o(function(i,n,a,s,l,u){this.renderer.nodeShapeImpl("polygon",i,n,a,s,l,this.points)},"draw"),intersectLine:o(function(i,n,a,s,l,u,h,d){return Kb(l,u,this.points,i,n,a/2,s/2,h)},"intersectLine"),checkPoint:o(function(i,n,a,s,l,u,h,d){return Qu(i,n,this.points,u,h,s,l,[0,-1],a)},"checkPoint"),hasMiterBounds:e!=="rectangle",miterBounds:o(function(i,n,a,s,l,u){return Grt(this.points,i,n,a,s,l)},"miterBounds")}};Ju.generateEllipse=function(){return this.nodeShapes.ellipse={renderer:this,name:"ellipse",draw:o(function(t,r,i,n,a,s){this.renderer.nodeShapeImpl(this.name,t,r,i,n,a)},"draw"),intersectLine:o(function(t,r,i,n,a,s,l,u){return Xrt(a,s,t,r,i/2+l,n/2+l)},"intersectLine"),checkPoint:o(function(t,r,i,n,a,s,l,u){return Qp(t,r,n,a,s,l,i)},"checkPoint")}};Ju.generateRoundPolygon=function(e,t){return this.nodeShapes[e]={renderer:this,name:e,points:t,getOrCreateCorners:o(function(i,n,a,s,l,u,h){if(u[h]!==void 0&&u[h+"-cx"]===i&&u[h+"-cy"]===n)return u[h];u[h]=new Array(t.length/2),u[h+"-cx"]=i,u[h+"-cy"]=n;var d=a/2,f=s/2;l=l==="auto"?pme(a,s):l;for(var p=new Array(t.length/2),m=0;m<t.length/2;m++)p[m]={x:i+d*t[m*2],y:n+f*t[m*2+1]};var g,y,v,x,b=p.length;for(y=p[b-1],g=0;g<b;g++)v=p[g%b],x=p[(g+1)%b],u[h][g]=LP(y,v,x,l),y=v,v=x;return u[h]},"getOrCreateCorners"),draw:o(function(i,n,a,s,l,u,h){this.renderer.nodeShapeImpl("round-polygon",i,n,a,s,l,this.points,this.getOrCreateCorners(n,a,s,l,u,h,"drawCorners"))},"draw"),intersectLine:o(function(i,n,a,s,l,u,h,d,f){return Zrt(l,u,this.points,i,n,a,s,h,this.getOrCreateCorners(i,n,a,s,d,f,"corners"))},"intersectLine"),checkPoint:o(function(i,n,a,s,l,u,h,d,f){return jrt(i,n,this.points,u,h,s,l,this.getOrCreateCorners(u,h,s,l,d,f,"corners"))},"checkPoint")}};Ju.generateRoundRectangle=function(){return this.nodeShapes["round-rectangle"]=this.nodeShapes.roundrectangle={renderer:this,name:"round-rectangle",points:rs(4,0),draw:o(function(t,r,i,n,a,s){this.renderer.nodeShapeImpl(this.name,t,r,i,n,a,this.points,s)},"draw"),intersectLine:o(function(t,r,i,n,a,s,l,u){return dme(a,s,t,r,i,n,l,u)},"intersectLine"),checkPoint:o(function(t,r,i,n,a,s,l,u){var h=n/2,d=a/2;u=u==="auto"?Hd(n,a):u,u=Math.min(h,d,u);var f=u*2;return!!(Qu(t,r,this.points,s,l,n,a-f,[0,-1],i)||Qu(t,r,this.points,s,l,n-f,a,[0,-1],i)||Qp(t,r,f,f,s-h+u,l-d+u,i)||Qp(t,r,f,f,s+h-u,l-d+u,i)||Qp(t,r,f,f,s+h-u,l+d-u,i)||Qp(t,r,f,f,s-h+u,l+d-u,i))},"checkPoint")}};Ju.generateCutRectangle=function(){return this.nodeShapes["cut-rectangle"]=this.nodeShapes.cutrectangle={renderer:this,name:"cut-rectangle",cornerLength:bP(),points:rs(4,0),draw:o(function(t,r,i,n,a,s){this.renderer.nodeShapeImpl(this.name,t,r,i,n,a,null,s)},"draw"),generateCutTrianglePts:o(function(t,r,i,n,a){var s=a==="auto"?this.cornerLength:a,l=r/2,u=t/2,h=i-u,d=i+u,f=n-l,p=n+l;return{topLeft:[h,f+s,h+s,f,h+s,f+s],topRight:[d-s,f,d,f+s,d-s,f+s],bottomRight:[d,p-s,d-s,p,d-s,p-s],bottomLeft:[h+s,p,h,p-s,h+s,p-s]}},"generateCutTrianglePts"),intersectLine:o(function(t,r,i,n,a,s,l,u){var h=this.generateCutTrianglePts(i+2*l,n+2*l,t,r,u),d=[].concat.apply([],[h.topLeft.splice(0,4),h.topRight.splice(0,4),h.bottomRight.splice(0,4),h.bottomLeft.splice(0,4)]);return Kb(a,s,d,t,r)},"intersectLine"),checkPoint:o(function(t,r,i,n,a,s,l,u){var h=u==="auto"?this.cornerLength:u;if(Qu(t,r,this.points,s,l,n,a-2*h,[0,-1],i)||Qu(t,r,this.points,s,l,n-2*h,a,[0,-1],i))return!0;var d=this.generateCutTrianglePts(n,a,s,l);return Bs(t,r,d.topLeft)||Bs(t,r,d.topRight)||Bs(t,r,d.bottomRight)||Bs(t,r,d.bottomLeft)},"checkPoint")}};Ju.generateBarrel=function(){return this.nodeShapes.barrel={renderer:this,name:"barrel",points:rs(4,0),draw:o(function(t,r,i,n,a,s){this.renderer.nodeShapeImpl(this.name,t,r,i,n,a)},"draw"),intersectLine:o(function(t,r,i,n,a,s,l,u){var h=.15,d=.5,f=.85,p=this.generateBarrelBezierPts(i+2*l,n+2*l,t,r),m=o(function(v){var x=u1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},h),b=u1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},d),T=u1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},f);return[v[0],v[1],x.x,x.y,b.x,b.y,T.x,T.y,v[4],v[5]]},"approximateBarrelCurvePts"),g=[].concat(m(p.topLeft),m(p.topRight),m(p.bottomRight),m(p.bottomLeft));return Kb(a,s,g,t,r)},"intersectLine"),generateBarrelBezierPts:o(function(t,r,i,n){var a=r/2,s=t/2,l=i-s,u=i+s,h=n-a,d=n+a,f=HO(t,r),p=f.heightOffset,m=f.widthOffset,g=f.ctrlPtOffsetPct*t,y={topLeft:[l,h+p,l+g,h,l+m,h],topRight:[u-m,h,u-g,h,u,h+p],bottomRight:[u,d-p,u-g,d,u-m,d],bottomLeft:[l+m,d,l+g,d,l,d-p]};return y.topLeft.isTop=!0,y.topRight.isTop=!0,y.bottomLeft.isBottom=!0,y.bottomRight.isBottom=!0,y},"generateBarrelBezierPts"),checkPoint:o(function(t,r,i,n,a,s,l,u){var h=HO(n,a),d=h.heightOffset,f=h.widthOffset;if(Qu(t,r,this.points,s,l,n,a-2*d,[0,-1],i)||Qu(t,r,this.points,s,l,n-2*f,a,[0,-1],i))return!0;for(var p=this.generateBarrelBezierPts(n,a,s,l),m=o(function(A,N,P){var I=P[4],D=P[2],_=P[0],R=P[5],M=P[1],L=Math.min(I,_),B=Math.max(I,_),O=Math.min(R,M),$=Math.max(R,M);if(L<=A&&A<=B&&O<=N&&N<=$){var G=Qrt(I,D,_),F=qrt(G[0],G[1],G[2],A),V=F.filter(function(H){return 0<=H&&H<=1});if(V.length>0)return V[0]}return null},"getCurveT"),g=Object.keys(p),y=0;y<g.length;y++){var v=g[y],x=p[v],b=m(t,r,x);if(b!=null){var T=x[5],w=x[3],C=x[1],k=na(T,w,C,b);if(x.isTop&&k<=r||x.isBottom&&r<=k)return!0}}return!1},"checkPoint")}};Ju.generateBottomRoundrectangle=function(){return this.nodeShapes["bottom-round-rectangle"]=this.nodeShapes.bottomroundrectangle={renderer:this,name:"bottom-round-rectangle",points:rs(4,0),draw:o(function(t,r,i,n,a,s){this.renderer.nodeShapeImpl(this.name,t,r,i,n,a,this.points,s)},"draw"),intersectLine:o(function(t,r,i,n,a,s,l,u){var h=t-(i/2+l),d=r-(n/2+l),f=d,p=t+(i/2+l),m=Gd(a,s,t,r,h,d,p,f,!1);return m.length>0?m:dme(a,s,t,r,i,n,l,u)},"intersectLine"),checkPoint:o(function(t,r,i,n,a,s,l,u){u=u==="auto"?Hd(n,a):u;var h=2*u;if(Qu(t,r,this.points,s,l,n,a-h,[0,-1],i)||Qu(t,r,this.points,s,l,n-h,a,[0,-1],i))return!0;var d=n/2+2*i,f=a/2+2*i,p=[s-d,l-f,s-d,l,s+d,l,s+d,l-f];return!!(Bs(t,r,p)||Qp(t,r,h,h,s+n/2-u,l+a/2-u,i)||Qp(t,r,h,h,s-n/2+u,l+a/2-u,i))},"checkPoint")}};Ju.registerNodeShapes=function(){var e=this.nodeShapes={},t=this;this.generateEllipse(),this.generatePolygon("triangle",rs(3,0)),this.generateRoundPolygon("round-triangle",rs(3,0)),this.generatePolygon("rectangle",rs(4,0)),e.square=e.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var r=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",r),this.generateRoundPolygon("round-diamond",r)}this.generatePolygon("pentagon",rs(5,0)),this.generateRoundPolygon("round-pentagon",rs(5,0)),this.generatePolygon("hexagon",rs(6,0)),this.generateRoundPolygon("round-hexagon",rs(6,0)),this.generatePolygon("heptagon",rs(7,0)),this.generateRoundPolygon("round-heptagon",rs(7,0)),this.generatePolygon("octagon",rs(8,0)),this.generateRoundPolygon("round-octagon",rs(8,0));var i=new Array(20);{var n=UO(5,0),a=UO(5,Math.PI/5),s=.5*(3-Math.sqrt(5));s*=1.57;for(var l=0;l<a.length/2;l++)a[l*2]*=s,a[l*2+1]*=s;for(var l=0;l<20/4;l++)i[l*4]=n[l*2],i[l*4+1]=n[l*2+1],i[l*4+2]=a[l*2],i[l*4+3]=a[l*2+1]}i=fme(i),this.generatePolygon("star",i),this.generatePolygon("vee",[-1,-1,0,-.333,1,-1,0,1]),this.generatePolygon("rhomboid",[-1,-1,.333,-1,1,1,-.333,1]),this.generatePolygon("right-rhomboid",[-.333,-1,1,-1,.333,1,-1,1]),this.nodeShapes.concavehexagon=this.generatePolygon("concave-hexagon",[-1,-.95,-.75,0,-1,.95,1,.95,.75,0,1,-.95]);{var u=[-1,-1,.25,-1,1,0,.25,1,-1,1];this.generatePolygon("tag",u),this.generateRoundPolygon("round-tag",u)}e.makePolygon=function(h){var d=h.join("$"),f="polygon-"+d,p;return(p=this[f])?p:t.generatePolygon(f,h)}};d2={};d2.timeToRender=function(){return this.redrawTotalTime/this.redrawCount};d2.redraw=function(e){e=e||lme();var t=this;t.averageRedrawTime===void 0&&(t.averageRedrawTime=0),t.lastRedrawTime===void 0&&(t.lastRedrawTime=0),t.lastDrawTime===void 0&&(t.lastDrawTime=0),t.requestedFrame=!0,t.renderOptions=e};d2.beforeRender=function(e,t){if(!this.destroyed){t==null&&Yi("Priority is not optional for beforeRender");var r=this.beforeRenderCallbacks;r.push({fn:e,priority:t}),r.sort(function(i,n){return n.priority-i.priority})}};A0e=o(function(t,r,i){for(var n=t.beforeRenderCallbacks,a=0;a<n.length;a++)n[a].fn(r,i)},"beforeRenderCallbacks");d2.startRenderLoop=function(){var e=this,t=e.cy;if(!e.renderLoopStarted){e.renderLoopStarted=!0;var r=o(function(n){if(!e.destroyed){if(!t.batching())if(e.requestedFrame&&!e.skipFrame){A0e(e,!0,n);var a=Zu();e.render(e.renderOptions);var s=e.lastDrawTime=Zu();e.averageRedrawTime===void 0&&(e.averageRedrawTime=s-a),e.redrawCount===void 0&&(e.redrawCount=0),e.redrawCount++,e.redrawTotalTime===void 0&&(e.redrawTotalTime=0);var l=s-a;e.redrawTotalTime+=l,e.lastRedrawTime=l,e.averageRedrawTime=e.averageRedrawTime/2+l/2,e.requestedFrame=!1}else A0e(e,!1,n);e.skipFrame=!1,O3(r)}},"renderFn");O3(r)}};pst=o(function(t){this.init(t)},"BaseRenderer"),pge=pst,C1=pge.prototype;C1.clientFunctions=["redrawHint","render","renderTo","matchCanvasSize","nodeShapeImpl","arrowShapeImpl"];C1.init=function(e){var t=this;t.options=e,t.cy=e.cy;var r=t.container=e.cy.container(),i=t.cy.window();if(i){var n=i.document,a=n.head,s="__________cytoscape_stylesheet",l="__________cytoscape_container",u=n.getElementById(s)!=null;if(r.className.indexOf(l)<0&&(r.className=(r.className||"")+" "+l),!u){var h=n.createElement("style");h.id=s,h.textContent="."+l+" { position: relative; }",a.insertBefore(h,a.children[0])}var d=i.getComputedStyle(r),f=d.getPropertyValue("position");f==="static"&&di("A Cytoscape container has style position:static and so can not use UI extensions properly")}t.selection=[void 0,void 0,void 0,void 0,0],t.bezierProjPcts=[.05,.225,.4,.5,.6,.775,.95],t.hoverData={down:null,last:null,downTime:null,triggerMode:null,dragging:!1,initialPan:[null,null],capture:!1},t.dragData={possibleDragElements:[]},t.touchData={start:null,capture:!1,startPosition:[null,null,null,null,null,null],singleTouchStartTime:null,singleTouchMoved:!0,now:[null,null,null,null,null,null],earlier:[null,null,null,null,null,null]},t.redraws=0,t.showFps=e.showFps,t.debug=e.debug,t.webgl=e.webgl,t.hideEdgesOnViewport=e.hideEdgesOnViewport,t.textureOnViewport=e.textureOnViewport,t.wheelSensitivity=e.wheelSensitivity,t.motionBlurEnabled=e.motionBlur,t.forcedPixelRatio=Ot(e.pixelRatio)?e.pixelRatio:null,t.motionBlur=e.motionBlur,t.motionBlurOpacity=e.motionBlurOpacity,t.motionBlurTransparency=1-t.motionBlurOpacity,t.motionBlurPxRatio=1,t.mbPxRBlurry=1,t.minMbLowQualFrames=4,t.fullQualityMb=!1,t.clearedForMotionBlur=[],t.desktopTapThreshold=e.desktopTapThreshold,t.desktopTapThreshold2=e.desktopTapThreshold*e.desktopTapThreshold,t.touchTapThreshold=e.touchTapThreshold,t.touchTapThreshold2=e.touchTapThreshold*e.touchTapThreshold,t.tapholdDuration=500,t.bindings=[],t.beforeRenderCallbacks=[],t.beforeRenderPriorities={animations:400,eleCalcs:300,eleTxrDeq:200,lyrTxrDeq:150,lyrTxrSkip:100},t.registerNodeShapes(),t.registerArrowShapes(),t.registerCalculationListeners()};C1.notify=function(e,t){var r=this,i=r.cy;if(!this.destroyed){if(e==="init"){r.load();return}if(e==="destroy"){r.destroy();return}(e==="add"||e==="remove"||e==="move"&&i.hasCompoundNodes()||e==="load"||e==="zorder"||e==="mount")&&r.invalidateCachedZSortedEles(),e==="viewport"&&r.redrawHint("select",!0),e==="gc"&&r.redrawHint("gc",!0),(e==="load"||e==="resize"||e==="mount")&&(r.invalidateContainerClientCoordsCache(),r.matchCanvasSize(r.container)),r.redrawHint("eles",!0),r.redrawHint("drag",!0),this.startRenderLoop(),this.redraw()}};C1.destroy=function(){var e=this;e.destroyed=!0,e.cy.stopAnimationLoop();for(var t=0;t<e.bindings.length;t++){var r=e.bindings[t],i=r,n=i.target;(n.off||n.removeEventListener).apply(n,i.args)}if(e.bindings=[],e.beforeRenderCallbacks=[],e.onUpdateEleCalcsFns=[],e.removeObserver&&e.removeObserver.disconnect(),e.styleObserver&&e.styleObserver.disconnect(),e.resizeObserver&&e.resizeObserver.disconnect(),e.labelCalcDiv)try{document.body.removeChild(e.labelCalcDiv)}catch{}};C1.isHeadless=function(){return!1};[RP,dge,fge,T1,Ju,d2].forEach(function(e){hr(C1,e)});PO=1e3/60,mge={setupDequeueing:o(function(t){return o(function(){var i=this,n=this.renderer;if(!i.dequeueingSetup){i.dequeueingSetup=!0;var a=o2(function(){n.redrawHint("eles",!0),n.redrawHint("drag",!0),n.redraw()},t.deqRedrawThreshold),s=o(function(h,d){var f=Zu(),p=n.averageRedrawTime,m=n.lastRedrawTime,g=[],y=n.cy.extent(),v=n.getPixelRatio();for(h||n.flushRenderedStyleQueue();;){var x=Zu(),b=x-f,T=x-d;if(m<PO){var w=PO-(h?p:0);if(T>=t.deqFastCost*w)break}else if(h){if(b>=t.deqCost*m||b>=t.deqAvgCost*p)break}else if(T>=t.deqNoDrawCost*PO)break;var C=t.deq(i,v,y);if(C.length>0)for(var k=0;k<C.length;k++)g.push(C[k]);else break}g.length>0&&(t.onDeqd(i,g),!h&&t.shouldRedraw(i,g,v,y)&&a())},"dequeue"),l=t.priority||mP;n.beforeRender(s,l(i))}},"setupDequeueingImpl")},"setupDequeueing")},mst=(function(){function e(t){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:P3;Kd(this,e),this.idsByKey=new Ku,this.keyForId=new Ku,this.cachesByLvl=new Ku,this.lvls=[],this.getKey=t,this.doesEleInvalidateKey=r}return o(e,"ElementTextureCacheLookup"),Zd(e,[{key:"getIdsFor",value:o(function(r){r==null&&Yi("Can not get id list for null key");var i=this.idsByKey,n=this.idsByKey.get(r);return n||(n=new y1,i.set(r,n)),n},"getIdsFor")},{key:"addIdForKey",value:o(function(r,i){r!=null&&this.getIdsFor(r).add(i)},"addIdForKey")},{key:"deleteIdForKey",value:o(function(r,i){r!=null&&this.getIdsFor(r).delete(i)},"deleteIdForKey")},{key:"getNumberOfIdsForKey",value:o(function(r){return r==null?0:this.getIdsFor(r).size},"getNumberOfIdsForKey")},{key:"updateKeyMappingFor",value:o(function(r){var i=r.id(),n=this.keyForId.get(i),a=this.getKey(r);this.deleteIdForKey(n,i),this.addIdForKey(a,i),this.keyForId.set(i,a)},"updateKeyMappingFor")},{key:"deleteKeyMappingFor",value:o(function(r){var i=r.id(),n=this.keyForId.get(i);this.deleteIdForKey(n,i),this.keyForId.delete(i)},"deleteKeyMappingFor")},{key:"keyHasChangedFor",value:o(function(r){var i=r.id(),n=this.keyForId.get(i),a=this.getKey(r);return n!==a},"keyHasChangedFor")},{key:"isInvalid",value:o(function(r){return this.keyHasChangedFor(r)||this.doesEleInvalidateKey(r)},"isInvalid")},{key:"getCachesAt",value:o(function(r){var i=this.cachesByLvl,n=this.lvls,a=i.get(r);return a||(a=new Ku,i.set(r,a),n.push(r)),a},"getCachesAt")},{key:"getCache",value:o(function(r,i){return this.getCachesAt(i).get(r)},"getCache")},{key:"get",value:o(function(r,i){var n=this.getKey(r),a=this.getCache(n,i);return a!=null&&this.updateKeyMappingFor(r),a},"get")},{key:"getForCachedKey",value:o(function(r,i){var n=this.keyForId.get(r.id()),a=this.getCache(n,i);return a},"getForCachedKey")},{key:"hasCache",value:o(function(r,i){return this.getCachesAt(i).has(r)},"hasCache")},{key:"has",value:o(function(r,i){var n=this.getKey(r);return this.hasCache(n,i)},"has")},{key:"setCache",value:o(function(r,i,n){n.key=r,this.getCachesAt(i).set(r,n)},"setCache")},{key:"set",value:o(function(r,i,n){var a=this.getKey(r);this.setCache(a,i,n),this.updateKeyMappingFor(r)},"set")},{key:"deleteCache",value:o(function(r,i){this.getCachesAt(i).delete(r)},"deleteCache")},{key:"delete",value:o(function(r,i){var n=this.getKey(r);this.deleteCache(n,i)},"_delete")},{key:"invalidateKey",value:o(function(r){var i=this;this.lvls.forEach(function(n){return i.deleteCache(r,n)})},"invalidateKey")},{key:"invalidate",value:o(function(r){var i=r.id(),n=this.keyForId.get(i);this.deleteKeyMappingFor(r);var a=this.doesEleInvalidateKey(r);return a&&this.invalidateKey(n),a||this.getNumberOfIdsForKey(n)===0},"invalidate")}])})(),_0e=25,b3=50,L3=-4,aP=3,gge=7.99,gst=8,yst=1024,vst=1024,xst=1024,bst=.2,Tst=.8,Cst=10,kst=.15,wst=.1,Sst=.9,Est=.9,Ast=100,_st=1,c1={dequeue:"dequeue",downscale:"downscale",highQuality:"highQuality"},Dst=va({getKey:null,doesEleInvalidateKey:P3,drawElement:null,getBoundingBox:null,getRotationPoint:null,getRotationOffset:null,isVisible:ame,allowEdgeTxrCaching:!0,allowParentTxrCaching:!0}),Vb=o(function(t,r){var i=this;i.renderer=t,i.onDequeues=[];var n=Dst(r);hr(i,n),i.lookup=new mst(n.getKey,n.doesEleInvalidateKey),i.setupDequeueing()},"ElementTextureCache"),qn=Vb.prototype;qn.reasons=c1;qn.getTextureQueue=function(e){var t=this;return t.eleImgCaches=t.eleImgCaches||{},t.eleImgCaches[e]=t.eleImgCaches[e]||[]};qn.getRetiredTextureQueue=function(e){var t=this,r=t.eleImgCaches.retired=t.eleImgCaches.retired||{},i=r[e]=r[e]||[];return i};qn.getElementQueue=function(){var e=this,t=e.eleCacheQueue=e.eleCacheQueue||new l2(function(r,i){return i.reqs-r.reqs});return t};qn.getElementKeyToQueue=function(){var e=this,t=e.eleKeyToCacheQueue=e.eleKeyToCacheQueue||{};return t};qn.getElement=function(e,t,r,i,n){var a=this,s=this.renderer,l=s.cy.zoom(),u=this.lookup;if(!t||t.w===0||t.h===0||isNaN(t.w)||isNaN(t.h)||!e.visible()||e.removed()||!a.allowEdgeTxrCaching&&e.isEdge()||!a.allowParentTxrCaching&&e.isParent())return null;if(i==null&&(i=Math.ceil(yP(l*r))),i<L3)i=L3;else if(l>=gge||i>aP)return null;var h=Math.pow(2,i),d=t.h*h,f=t.w*h,p=s.eleTextBiggerThanMin(e,h);if(!this.isVisible(e,p))return null;var m=u.get(e,i);if(m&&m.invalidated&&(m.invalidated=!1,m.texture.invalidatedWidth-=m.width),m)return m;var g;if(d<=_0e?g=_0e:d<=b3?g=b3:g=Math.ceil(d/b3)*b3,d>xst||f>vst)return null;var y=a.getTextureQueue(g),v=y[y.length-2],x=o(function(){return a.recycleTexture(g,f)||a.addTexture(g,f)},"addNewTxr");v||(v=y[y.length-1]),v||(v=x()),v.width-v.usedWidth<f&&(v=x());for(var b=o(function(L){return L&&L.scaledLabelShown===p},"scalableFrom"),T=n&&n===c1.dequeue,w=n&&n===c1.highQuality,C=n&&n===c1.downscale,k,E=i+1;E<=aP;E++){var A=u.get(e,E);if(A){k=A;break}}var N=k&&k.level===i+1?k:null,P=o(function(){v.context.drawImage(N.texture.canvas,N.x,0,N.width,N.height,v.usedWidth,0,f,d)},"downscale");if(v.context.setTransform(1,0,0,1,0,0),v.context.clearRect(v.usedWidth,0,f,g),b(N))P();else if(b(k))if(w){for(var I=k.level;I>i;I--)N=a.getElement(e,t,r,I,c1.downscale);P()}else return a.queueElement(e,k.level-1),k;else{var D;if(!T&&!w&&!C)for(var _=i-1;_>=L3;_--){var R=u.get(e,_);if(R){D=R;break}}if(b(D))return a.queueElement(e,i),D;v.context.translate(v.usedWidth,0),v.context.scale(h,h),this.drawElement(v.context,e,t,p,!1),v.context.scale(1/h,1/h),v.context.translate(-v.usedWidth,0)}return m={x:v.usedWidth,texture:v,level:i,scale:h,width:f,height:d,scaledLabelShown:p},v.usedWidth+=Math.ceil(f+gst),v.eleCaches.push(m),u.set(e,i,m),a.checkTextureFullness(v),m};qn.invalidateElements=function(e){for(var t=0;t<e.length;t++)this.invalidateElement(e[t])};qn.invalidateElement=function(e){var t=this,r=t.lookup,i=[],n=r.isInvalid(e);if(n){for(var a=L3;a<=aP;a++){var s=r.getForCachedKey(e,a);s&&i.push(s)}var l=r.invalidate(e);if(l)for(var u=0;u<i.length;u++){var h=i[u],d=h.texture;d.invalidatedWidth+=h.width,h.invalidated=!0,t.checkTextureUtility(d)}t.removeFromQueue(e)}};qn.checkTextureUtility=function(e){e.invalidatedWidth>=bst*e.width&&this.retireTexture(e)};qn.checkTextureFullness=function(e){var t=this,r=t.getTextureQueue(e.height);e.usedWidth/e.width>Tst&&e.fullnessChecks>=Cst?Ud(r,e):e.fullnessChecks++};qn.retireTexture=function(e){var t=this,r=e.height,i=t.getTextureQueue(r),n=this.lookup;Ud(i,e),e.retired=!0;for(var a=e.eleCaches,s=0;s<a.length;s++){var l=a[s];n.deleteCache(l.key,l.level)}gP(a);var u=t.getRetiredTextureQueue(r);u.push(e)};qn.addTexture=function(e,t){var r=this,i=r.getTextureQueue(e),n={};return i.push(n),n.eleCaches=[],n.height=e,n.width=Math.max(yst,t),n.usedWidth=0,n.invalidatedWidth=0,n.fullnessChecks=0,n.canvas=r.renderer.makeOffscreenCanvas(n.width,n.height),n.context=n.canvas.getContext("2d"),n};qn.recycleTexture=function(e,t){for(var r=this,i=r.getTextureQueue(e),n=r.getRetiredTextureQueue(e),a=0;a<n.length;a++){var s=n[a];if(s.width>=t)return s.retired=!1,s.usedWidth=0,s.invalidatedWidth=0,s.fullnessChecks=0,gP(s.eleCaches),s.context.setTransform(1,0,0,1,0,0),s.context.clearRect(0,0,s.width,s.height),Ud(n,s),i.push(s),s}};qn.queueElement=function(e,t){var r=this,i=r.getElementQueue(),n=r.getElementKeyToQueue(),a=this.getKey(e),s=n[a];if(s)s.level=Math.max(s.level,t),s.eles.merge(e),s.reqs++,i.updateItem(s);else{var l={eles:e.spawn().merge(e),level:t,reqs:1,key:a};i.push(l),n[a]=l}};qn.dequeue=function(e){for(var t=this,r=t.getElementQueue(),i=t.getElementKeyToQueue(),n=[],a=t.lookup,s=0;s<_st&&r.size()>0;s++){var l=r.pop(),u=l.key,h=l.eles[0],d=a.hasCache(h,l.level);if(i[u]=null,d)continue;n.push(l);var f=t.getBoundingBox(h);t.getElement(h,f,e,l.level,c1.dequeue)}return n};qn.removeFromQueue=function(e){var t=this,r=t.getElementQueue(),i=t.getElementKeyToQueue(),n=this.getKey(e),a=i[n];a!=null&&(a.eles.length===1?(a.reqs=pP,r.updateItem(a),r.pop(),i[n]=null):a.eles.unmerge(e))};qn.onDequeue=function(e){this.onDequeues.push(e)};qn.offDequeue=function(e){Ud(this.onDequeues,e)};qn.setupDequeueing=mge.setupDequeueing({deqRedrawThreshold:Ast,deqCost:kst,deqAvgCost:wst,deqNoDrawCost:Sst,deqFastCost:Est,deq:o(function(t,r,i){return t.dequeue(r,i)},"deq"),onDeqd:o(function(t,r){for(var i=0;i<t.onDequeues.length;i++){var n=t.onDequeues[i];n(r)}},"onDeqd"),shouldRedraw:o(function(t,r,i,n){for(var a=0;a<r.length;a++)for(var s=r[a].eles,l=0;l<s.length;l++){var u=s[l].boundingBox();if(xP(u,n))return!0}return!1},"shouldRedraw"),priority:o(function(t){return t.renderer.beforeRenderPriorities.eleTxrDeq},"priority")});Rst=1,qb=-4,q3=2,Lst=3.99,Mst=50,Nst=50,Ist=.15,Ost=.1,Pst=.9,Bst=.9,Fst=1,D0e=250,$st=4e3*4e3,R0e=32767,zst=!0,yge=o(function(t){var r=this,i=r.renderer=t,n=i.cy;r.layersByLevel={},r.firstGet=!0,r.lastInvalidationTime=Zu()-2*D0e,r.skipping=!1,r.eleTxrDeqs=n.collection(),r.scheduleElementRefinement=o2(function(){r.refineElementTextures(r.eleTxrDeqs),r.eleTxrDeqs.unmerge(r.eleTxrDeqs)},Nst),i.beforeRender(function(s,l){l-r.lastInvalidationTime<=D0e?r.skipping=!0:r.skipping=!1},i.beforeRenderPriorities.lyrTxrSkip);var a=o(function(l,u){return u.reqs-l.reqs},"qSort");r.layersQueue=new l2(a),r.setupDequeueing()},"LayeredTextureCache"),xa=yge.prototype,L0e=0,Gst=Math.pow(2,53)-1;xa.makeLayer=function(e,t){var r=Math.pow(2,t),i=Math.ceil(e.w*r),n=Math.ceil(e.h*r),a=this.renderer.makeOffscreenCanvas(i,n),s={id:L0e=++L0e%Gst,bb:e,level:t,width:i,height:n,canvas:a,context:a.getContext("2d"),eles:[],elesQueue:[],reqs:0},l=s.context,u=-s.bb.x1,h=-s.bb.y1;return l.scale(r,r),l.translate(u,h),s};xa.getLayers=function(e,t,r){var i=this,n=i.renderer,a=n.cy,s=a.zoom(),l=i.firstGet;if(i.firstGet=!1,r==null){if(r=Math.ceil(yP(s*t)),r<qb)r=qb;else if(s>=Lst||r>q3)return null}i.validateLayersElesOrdering(r,e);var u=i.layersByLevel,h=Math.pow(2,r),d=u[r]=u[r]||[],f,p=i.levelIsComplete(r,e),m,g=o(function(){var P=o(function(M){if(i.validateLayersElesOrdering(M,e),i.levelIsComplete(M,e))return m=u[M],!0},"canUseAsTmpLvl"),I=o(function(M){if(!m)for(var L=r+M;qb<=L&&L<=q3&&!P(L);L+=M);},"checkLvls");I(1),I(-1);for(var D=d.length-1;D>=0;D--){var _=d[D];_.invalid&&Ud(d,_)}},"checkTempLevels");if(!p)g();else return d;var y=o(function(){if(!f){f=is();for(var P=0;P<e.length;P++)Frt(f,e[P].boundingBox())}return f},"getBb"),v=o(function(P){P=P||{};var I=P.after;y();var D=Math.ceil(f.w*h),_=Math.ceil(f.h*h);if(D>R0e||_>R0e)return null;var R=D*_;if(R>$st)return null;var M=i.makeLayer(f,r);if(I!=null){var L=d.indexOf(I)+1;d.splice(L,0,M)}else(P.insert===void 0||P.insert)&&d.unshift(M);return M},"makeLayer");if(i.skipping&&!l)return null;for(var x=null,b=e.length/Rst,T=!l,w=0;w<e.length;w++){var C=e[w],k=C._private.rscratch,E=k.imgLayerCaches=k.imgLayerCaches||{},A=E[r];if(A){x=A;continue}if((!x||x.eles.length>=b||!hme(x.bb,C.boundingBox()))&&(x=v({insert:!0,after:x}),!x))return null;m||T?i.queueLayer(x,C):i.drawEleInLayer(x,C,r,t),x.eles.push(C),E[r]=x}return m||(T?null:d)};xa.getEleLevelForLayerLevel=function(e,t){return e};xa.drawEleInLayer=function(e,t,r,i){var n=this,a=this.renderer,s=e.context,l=t.boundingBox();l.w===0||l.h===0||!t.visible()||(r=n.getEleLevelForLayerLevel(r,i),a.setImgSmoothing(s,!1),a.drawCachedElement(s,t,null,null,r,zst),a.setImgSmoothing(s,!0))};xa.levelIsComplete=function(e,t){var r=this,i=r.layersByLevel[e];if(!i||i.length===0)return!1;for(var n=0,a=0;a<i.length;a++){var s=i[a];if(s.reqs>0||s.invalid)return!1;n+=s.eles.length}return n===t.length};xa.validateLayersElesOrdering=function(e,t){var r=this.layersByLevel[e];if(r)for(var i=0;i<r.length;i++){for(var n=r[i],a=-1,s=0;s<t.length;s++)if(n.eles[0]===t[s]){a=s;break}if(a<0){this.invalidateLayer(n);continue}for(var l=a,s=0;s<n.eles.length;s++)if(n.eles[s]!==t[l+s]){this.invalidateLayer(n);break}}};xa.updateElementsInLayers=function(e,t){for(var r=this,i=i2(e[0]),n=0;n<e.length;n++)for(var a=i?null:e[n],s=i?e[n]:e[n].ele,l=s._private.rscratch,u=l.imgLayerCaches=l.imgLayerCaches||{},h=qb;h<=q3;h++){var d=u[h];d&&(a&&r.getEleLevelForLayerLevel(d.level)!==a.level||t(d,s,a))}};xa.haveLayers=function(){for(var e=this,t=!1,r=qb;r<=q3;r++){var i=e.layersByLevel[r];if(i&&i.length>0){t=!0;break}}return t};xa.invalidateElements=function(e){var t=this;e.length!==0&&(t.lastInvalidationTime=Zu(),!(e.length===0||!t.haveLayers())&&t.updateElementsInLayers(e,o(function(i,n,a){t.invalidateLayer(i)},"invalAssocLayers")))};xa.invalidateLayer=function(e){if(this.lastInvalidationTime=Zu(),!e.invalid){var t=e.level,r=e.eles,i=this.layersByLevel[t];Ud(i,e),e.elesQueue=[],e.invalid=!0,e.replacement&&(e.replacement.invalid=!0);for(var n=0;n<r.length;n++){var a=r[n]._private.rscratch.imgLayerCaches;a&&(a[t]=null)}}};xa.refineElementTextures=function(e){var t=this;t.updateElementsInLayers(e,o(function(i,n,a){var s=i.replacement;if(s||(s=i.replacement=t.makeLayer(i.bb,i.level),s.replaces=i,s.eles=i.eles),!s.reqs)for(var l=0;l<s.eles.length;l++)t.queueLayer(s,s.eles[l])},"refineEachEle"))};xa.enqueueElementRefinement=function(e){this.eleTxrDeqs.merge(e),this.scheduleElementRefinement()};xa.queueLayer=function(e,t){var r=this,i=r.layersQueue,n=e.elesQueue,a=n.hasId=n.hasId||{};if(!e.replacement){if(t){if(a[t.id()])return;n.push(t),a[t.id()]=!0}e.reqs?(e.reqs++,i.updateItem(e)):(e.reqs=1,i.push(e))}};xa.dequeue=function(e){for(var t=this,r=t.layersQueue,i=[],n=0;n<Fst&&r.size()!==0;){var a=r.peek();if(a.replacement){r.pop();continue}if(a.replaces&&a!==a.replaces.replacement){r.pop();continue}if(a.invalid){r.pop();continue}var s=a.elesQueue.shift();s&&(t.drawEleInLayer(a,s,a.level,e),n++),i.length===0&&i.push(!0),a.elesQueue.length===0&&(r.pop(),a.reqs=0,a.replaces&&t.applyLayerReplacement(a),t.requestRedraw())}return i};xa.applyLayerReplacement=function(e){var t=this,r=t.layersByLevel[e.level],i=e.replaces,n=r.indexOf(i);if(!(n<0||i.invalid)){r[n]=e;for(var a=0;a<e.eles.length;a++){var s=e.eles[a]._private,l=s.imgLayerCaches=s.imgLayerCaches||{};l&&(l[e.level]=e)}t.requestRedraw()}};xa.requestRedraw=o2(function(){var e=this.renderer;e.redrawHint("eles",!0),e.redrawHint("drag",!0),e.redraw()},100);xa.setupDequeueing=mge.setupDequeueing({deqRedrawThreshold:Mst,deqCost:Ist,deqAvgCost:Ost,deqNoDrawCost:Pst,deqFastCost:Bst,deq:o(function(t,r){return t.dequeue(r)},"deq"),onDeqd:mP,shouldRedraw:ame,priority:o(function(t){return t.renderer.beforeRenderPriorities.lyrTxrDeq},"priority")});vge={};o(Vst,"polygon");o(Wst,"triangleBackcurve");o(N0e,"triangleTee");o(qst,"circleTriangle");o(Ust,"circle$1");vge.arrowShapeImpl=function(e){return(M0e||(M0e={polygon:Vst,"triangle-backcurve":Wst,"triangle-tee":N0e,"circle-triangle":qst,"triangle-cross":N0e,circle:Ust}))[e]};zc={};zc.drawElement=function(e,t,r,i,n,a){var s=this;t.isNode()?s.drawNode(e,t,r,i,n,a):s.drawEdge(e,t,r,i,n,a)};zc.drawElementOverlay=function(e,t){var r=this;t.isNode()?r.drawNodeOverlay(e,t):r.drawEdgeOverlay(e,t)};zc.drawElementUnderlay=function(e,t){var r=this;t.isNode()?r.drawNodeUnderlay(e,t):r.drawEdgeUnderlay(e,t)};zc.drawCachedElementPortion=function(e,t,r,i,n,a,s,l){var u=this,h=r.getBoundingBox(t);if(!(h.w===0||h.h===0)){var d=r.getElement(t,h,i,n,a);if(d!=null){var f=l(u,t);if(f===0)return;var p=s(u,t),m=h.x1,g=h.y1,y=h.w,v=h.h,x,b,T,w,C;if(p!==0){var k=r.getRotationPoint(t);T=k.x,w=k.y,e.translate(T,w),e.rotate(p),C=u.getImgSmoothing(e),C||u.setImgSmoothing(e,!0);var E=r.getRotationOffset(t);x=E.x,b=E.y}else x=m,b=g;var A;f!==1&&(A=e.globalAlpha,e.globalAlpha=A*f),e.drawImage(d.texture.canvas,d.x,0,d.width,d.height,x,b,y,v),f!==1&&(e.globalAlpha=A),p!==0&&(e.rotate(-p),e.translate(-T,-w),C||u.setImgSmoothing(e,!1))}else r.drawElement(e,t)}};Hst=o(function(){return 0},"getZeroRotation"),Yst=o(function(t,r){return t.getTextAngle(r,null)},"getLabelRotation"),jst=o(function(t,r){return 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Re=t.cy().style().getIndexedStyle(t,"background-image-containment","value",K);if(fe&&Re==="over"||!fe&&Re==="inside"){ce++;continue}w[K]&&C[K].complete&&!C[K].error&&(ce++,s.drawInscribedImage(e,C[K],t,K,ie))}h.backgrounding=ce!==k,W!==h.backgrounding&&t.updateStyle(!1)},"drawImages"),Ee=o(function(){var ie=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!1,fe=arguments.length>1&&arguments[1]!==void 0?arguments[1]:p;s.hasPie(t)&&(s.drawPie(e,t,fe),ie&&(m||s.nodeShapes[s.getNodeShape(t)].draw(e,f.x,f.y,l,u,Y,d)))},"drawPie"),we=o(function(){var ie=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!1,fe=arguments.length>1&&arguments[1]!==void 0?arguments[1]:p;s.hasStripe(t)&&(e.save(),m?e.clip(d.pathCache):(s.nodeShapes[s.getNodeShape(t)].draw(e,f.x,f.y,l,u,Y,d),e.clip()),s.drawStripe(e,t,fe),e.restore(),ie&&(m||s.nodeShapes[s.getNodeShape(t)].draw(e,f.x,f.y,l,u,Y,d)))},"drawStripe"),Me=o(function(){var ie=arguments.length>0&&arguments[0]!==void 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x=p;i||(t.prevPxRatio!==u&&(t.invalidateContainerClientCoordsCache(),t.matchCanvasSize(t.container),t.redrawHint("eles",!0),t.redrawHint("drag",!0)),t.prevPxRatio=u),!i&&t.motionBlurTimeout&&clearTimeout(t.motionBlurTimeout),p&&(t.mbFrames==null&&(t.mbFrames=0),t.mbFrames++,t.mbFrames<3&&(x=!1),t.mbFrames>t.minMbLowQualFrames&&(t.motionBlurPxRatio=t.mbPxRBlurry)),t.clearingMotionBlur&&(t.motionBlurPxRatio=1),t.textureDrawLastFrame&&!f&&(d[t.NODE]=!0,d[t.SELECT_BOX]=!0);var b=r.style(),T=r.zoom(),w=s!==void 0?s:T,C=r.pan(),k={x:C.x,y:C.y},E={zoom:T,pan:{x:C.x,y:C.y}},A=t.prevViewport,N=A===void 0||E.zoom!==A.zoom||E.pan.x!==A.pan.x||E.pan.y!==A.pan.y;!N&&!(y&&!g)&&(t.motionBlurPxRatio=1),l&&(k=l),w*=u,k.x*=u,k.y*=u;var P=t.getCachedZSortedEles();function I(J,te,re,ee,Te){var ue=J.globalCompositeOperation;J.globalCompositeOperation="destination-out",t.colorFillStyle(J,255,255,255,t.motionBlurTransparency),J.fillRect(te,re,ee,Te),J.globalCompositeOperation=ue}o(I,"mbclear");function D(J,te){var re,ee,Te,ue;!t.clearingMotionBlur&&(J===h.bufferContexts[t.MOTIONBLUR_BUFFER_NODE]||J===h.bufferContexts[t.MOTIONBLUR_BUFFER_DRAG])?(re={x:C.x*m,y:C.y*m},ee=T*m,Te=t.canvasWidth*m,ue=t.canvasHeight*m):(re=k,ee=w,Te=t.canvasWidth,ue=t.canvasHeight),J.setTransform(1,0,0,1,0,0),te==="motionBlur"?I(J,0,0,Te,ue):!i&&(te===void 0||te)&&J.clearRect(0,0,Te,ue),n||(J.translate(re.x,re.y),J.scale(ee,ee)),l&&J.translate(l.x,l.y),s&&J.scale(s,s)}if(o(D,"setContextTransform"),f||(t.textureDrawLastFrame=!1),f){if(t.textureDrawLastFrame=!0,!t.textureCache){t.textureCache={},t.textureCache.bb=r.mutableElements().boundingBox(),t.textureCache.texture=t.data.bufferCanvases[t.TEXTURE_BUFFER];var _=t.data.bufferContexts[t.TEXTURE_BUFFER];_.setTransform(1,0,0,1,0,0),_.clearRect(0,0,t.canvasWidth*t.textureMult,t.canvasHeight*t.textureMult),t.render({forcedContext:_,drawOnlyNodeLayer:!0,forcedPxRatio:u*t.textureMult});var E=t.textureCache.viewport={zoom:r.zoom(),pan:r.pan(),width:t.canvasWidth,height:t.canvasHeight};E.mpan={x:(0-E.pan.x)/E.zoom,y:(0-E.pan.y)/E.zoom}}d[t.DRAG]=!1,d[t.NODE]=!1;var R=h.contexts[t.NODE],M=t.textureCache.texture,E=t.textureCache.viewport;R.setTransform(1,0,0,1,0,0),p?I(R,0,0,E.width,E.height):R.clearRect(0,0,E.width,E.height);var L=b.core("outside-texture-bg-color").value,B=b.core("outside-texture-bg-opacity").value;t.colorFillStyle(R,L[0],L[1],L[2],B),R.fillRect(0,0,E.width,E.height);var T=r.zoom();D(R,!1),R.clearRect(E.mpan.x,E.mpan.y,E.width/E.zoom/u,E.height/E.zoom/u),R.drawImage(M,E.mpan.x,E.mpan.y,E.width/E.zoom/u,E.height/E.zoom/u)}else t.textureOnViewport&&!i&&(t.textureCache=null);var O=r.extent(),$=t.pinching||t.hoverData.dragging||t.swipePanning||t.data.wheelZooming||t.hoverData.draggingEles||t.cy.animated(),G=t.hideEdgesOnViewport&&$,F=[];if(F[t.NODE]=!d[t.NODE]&&p&&!t.clearedForMotionBlur[t.NODE]||t.clearingMotionBlur,F[t.NODE]&&(t.clearedForMotionBlur[t.NODE]=!0),F[t.DRAG]=!d[t.DRAG]&&p&&!t.clearedForMotionBlur[t.DRAG]||t.clearingMotionBlur,F[t.DRAG]&&(t.clearedForMotionBlur[t.DRAG]=!0),d[t.NODE]||n||a||F[t.NODE]){var V=p&&!F[t.NODE]&&m!==1,R=i||(V?t.data.bufferContexts[t.MOTIONBLUR_BUFFER_NODE]:h.contexts[t.NODE]),H=p&&!V?"motionBlur":void 0;D(R,H),G?t.drawCachedNodes(R,P.nondrag,u,O):t.drawLayeredElements(R,P.nondrag,u,O),t.debug&&t.drawDebugPoints(R,P.nondrag),!n&&!p&&(d[t.NODE]=!1)}if(!a&&(d[t.DRAG]||n||F[t.DRAG])){var V=p&&!F[t.DRAG]&&m!==1,R=i||(V?t.data.bufferContexts[t.MOTIONBLUR_BUFFER_DRAG]:h.contexts[t.DRAG]);D(R,p&&!V?"motionBlur":void 0),G?t.drawCachedNodes(R,P.drag,u,O):t.drawCachedElements(R,P.drag,u,O),t.debug&&t.drawDebugPoints(R,P.drag),!n&&!p&&(d[t.DRAG]=!1)}if(this.drawSelectionRectangle(e,D),p&&m!==1){var j=h.contexts[t.NODE],U=t.data.bufferCanvases[t.MOTIONBLUR_BUFFER_NODE],Q=h.contexts[t.DRAG],Y=t.data.bufferCanvases[t.MOTIONBLUR_BUFFER_DRAG],ae=o(function(te,re,ee){te.setTransform(1,0,0,1,0,0),ee||!x?te.clearRect(0,0,t.canvasWidth,t.canvasHeight):I(te,0,0,t.canvasWidth,t.canvasHeight);var Te=m;te.drawImage(re,0,0,t.canvasWidth*Te,t.canvasHeight*Te,0,0,t.canvasWidth,t.canvasHeight)},"drawMotionBlur");(d[t.NODE]||F[t.NODE])&&(ae(j,U,F[t.NODE]),d[t.NODE]=!1),(d[t.DRAG]||F[t.DRAG])&&(ae(Q,Y,F[t.DRAG]),d[t.DRAG]=!1)}t.prevViewport=E,t.clearingMotionBlur&&(t.clearingMotionBlur=!1,t.motionBlurCleared=!0,t.motionBlur=!0),p&&(t.motionBlurTimeout=setTimeout(function(){t.motionBlurTimeout=null,t.clearedForMotionBlur[t.NODE]=!1,t.clearedForMotionBlur[t.DRAG]=!1,t.motionBlur=!1,t.clearingMotionBlur=!f,t.mbFrames=0,d[t.NODE]=!0,d[t.DRAG]=!0,t.redraw()},Qst)),i||r.emit("render")};ns.drawSelectionRectangle=function(e,t){var r=this,i=r.cy,n=r.data,a=i.style(),s=e.drawOnlyNodeLayer,l=e.drawAllLayers,u=n.canvasNeedsRedraw,h=e.forcedContext;if(r.showFps||!s&&u[r.SELECT_BOX]&&!l){var d=h||n.contexts[r.SELECT_BOX];if(t(d),r.selection[4]==1&&(r.hoverData.selecting||r.touchData.selecting)){var f=r.cy.zoom(),p=a.core("selection-box-border-width").value/f;d.lineWidth=p,d.fillStyle="rgba("+a.core("selection-box-color").value[0]+","+a.core("selection-box-color").value[1]+","+a.core("selection-box-color").value[2]+","+a.core("selection-box-opacity").value+")",d.fillRect(r.selection[0],r.selection[1],r.selection[2]-r.selection[0],r.selection[3]-r.selection[1]),p>0&&(d.strokeStyle="rgba("+a.core("selection-box-border-color").value[0]+","+a.core("selection-box-border-color").value[1]+","+a.core("selection-box-border-color").value[2]+","+a.core("selection-box-opacity").value+")",d.strokeRect(r.selection[0],r.selection[1],r.selection[2]-r.selection[0],r.selection[3]-r.selection[1]))}if(n.bgActivePosistion&&!r.hoverData.selecting){var f=r.cy.zoom(),m=n.bgActivePosistion;d.fillStyle="rgba("+a.core("active-bg-color").value[0]+","+a.core("active-bg-color").value[1]+","+a.core("active-bg-color").value[2]+","+a.core("active-bg-opacity").value+")",d.beginPath(),d.arc(m.x,m.y,a.core("active-bg-size").pfValue/f,0,2*Math.PI),d.fill()}var g=r.lastRedrawTime;if(r.showFps&&g){g=Math.round(g);var y=Math.round(1e3/g),v="1 frame = "+g+" ms = "+y+" fps";if(d.setTransform(1,0,0,1,0,0),d.fillStyle="rgba(255, 0, 0, 0.75)",d.strokeStyle="rgba(255, 0, 0, 0.75)",d.font="30px Arial",!Bb){var x=d.measureText(v);Bb=x.actualBoundingBoxAscent}d.fillText(v,0,Bb);var b=60;d.strokeRect(0,Bb+10,250,20),d.fillRect(0,Bb+10,250*Math.min(y/b,1),20)}l||(u[r.SELECT_BOX]=!1)}};o(O0e,"compileShader");o(Jst,"createProgram");o(eot,"createTextureCanvas");o(IP,"getEffectivePanZoom");o(tot,"getEffectiveZoom");o(rot,"modelToRenderedPosition");o(iot,"isSimpleShape");o(not,"arrayEqual");o(Hp,"toWebGLColor");o(n1,"indexToVec4");o(aot,"vec4ToIndex");o(sot,"createTexture");o(Tge,"getTypeInfo");o(Cge,"createTypedArray");o(oot,"createTypedArrayView");o(lot,"createBufferStaticDraw");o(Oc,"createBufferDynamicDraw");o(cot,"create3x3MatrixBufferDynamicDraw");o(uot,"createPickingFrameBuffer");P0e=typeof Float32Array<"u"?Float32Array:Array;Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});o(FO,"create");o(B0e,"identity");o(hot,"multiply");o(M3,"translate");o(F0e,"rotate");o(sP,"scale");o(dot,"projection");fot=(function(){function e(t,r,i,n){Kd(this,e),this.debugID=Math.floor(Math.random()*1e4),this.r=t,this.texSize=r,this.texRows=i,this.texHeight=Math.floor(r/i),this.enableWrapping=!0,this.locked=!1,this.texture=null,this.needsBuffer=!0,this.freePointer={x:0,row:0},this.keyToLocation=new Map,this.canvas=n(t,r,r),this.scratch=n(t,r,this.texHeight,"scratch")}return o(e,"Atlas"),Zd(e,[{key:"lock",value:o(function(){this.locked=!0},"lock")},{key:"getKeys",value:o(function(){return new Set(this.keyToLocation.keys())},"getKeys")},{key:"getScale",value:o(function(r){var i=r.w,n=r.h,a=this.texHeight,s=this.texSize,l=a/n,u=i*l,h=n*l;return u>s&&(l=s/i,u=i*l,h=n*l),{scale:l,texW:u,texH:h}},"getScale")},{key:"draw",value:o(function(r,i,n){var a=this;if(this.locked)throw new Error("can't draw, atlas is locked");var s=this.texSize,l=this.texRows,u=this.texHeight,h=this.getScale(i),d=h.scale,f=h.texW,p=h.texH,m=o(function(T,w){if(n&&w){var C=w.context,k=T.x,E=T.row,A=k,N=u*E;C.save(),C.translate(A,N),C.scale(d,d),n(C,i),C.restore()}},"drawAt"),g=[null,null],y=o(function(){m(a.freePointer,a.canvas),g[0]={x:a.freePointer.x,y:a.freePointer.row*u,w:f,h:p},g[1]={x:a.freePointer.x+f,y:a.freePointer.row*u,w:0,h:p},a.freePointer.x+=f,a.freePointer.x==s&&(a.freePointer.x=0,a.freePointer.row++)},"drawNormal"),v=o(function(){var T=a.scratch,w=a.canvas;T.clear(),m({x:0,row:0},T);var C=s-a.freePointer.x,k=f-C,E=u;{var A=a.freePointer.x,N=a.freePointer.row*u,P=C;w.context.drawImage(T,0,0,P,E,A,N,P,E),g[0]={x:A,y:N,w:P,h:p}}{var I=C,D=(a.freePointer.row+1)*u,_=k;w&&w.context.drawImage(T,I,0,_,E,0,D,_,E),g[1]={x:0,y:D,w:_,h:p}}a.freePointer.x=k,a.freePointer.row++},"drawWrapped"),x=o(function(){a.freePointer.x=0,a.freePointer.row++},"moveToStartOfNextRow");if(this.freePointer.x+f<=s)y();else{if(this.freePointer.row>=l-1)return!1;this.freePointer.x===s?(x(),y()):this.enableWrapping?v():(x(),y())}return this.keyToLocation.set(r,g),this.needsBuffer=!0,g},"draw")},{key:"getOffsets",value:o(function(r){return this.keyToLocation.get(r)},"getOffsets")},{key:"isEmpty",value:o(function(){return this.freePointer.x===0&&this.freePointer.row===0},"isEmpty")},{key:"canFit",value:o(function(r){if(this.locked)return!1;var i=this.texSize,n=this.texRows,a=this.getScale(r),s=a.texW;return this.freePointer.x+s>i?this.freePointer.row<n-1:!0},"canFit")},{key:"bufferIfNeeded",value:o(function(r){this.texture||(this.texture=sot(r,this.debugID)),this.needsBuffer&&(this.texture.buffer(this.canvas),this.needsBuffer=!1,this.locked&&(this.canvas=null,this.scratch=null))},"bufferIfNeeded")},{key:"dispose",value:o(function(){this.texture&&(this.texture.deleteTexture(),this.texture=null),this.canvas=null,this.scratch=null,this.locked=!0},"dispose")}])})(),pot=(function(){function e(t,r,i,n){Kd(this,e),this.r=t,this.texSize=r,this.texRows=i,this.createTextureCanvas=n,this.atlases=[],this.styleKeyToAtlas=new Map,this.markedKeys=new Set}return o(e,"AtlasCollection"),Zd(e,[{key:"getKeys",value:o(function(){return new Set(this.styleKeyToAtlas.keys())},"getKeys")},{key:"_createAtlas",value:o(function(){var r=this.r,i=this.texSize,n=this.texRows,a=this.createTextureCanvas;return new fot(r,i,n,a)},"_createAtlas")},{key:"_getScratchCanvas",value:o(function(){if(!this.scratch){var r=this.r,i=this.texSize,n=this.texRows,a=this.createTextureCanvas,s=Math.floor(i/n);this.scratch=a(r,i,s,"scratch")}return this.scratch},"_getScratchCanvas")},{key:"draw",value:o(function(r,i,n){var a=this.styleKeyToAtlas.get(r);return a||(a=this.atlases[this.atlases.length-1],(!a||!a.canFit(i))&&(a&&a.lock(),a=this._createAtlas(),this.atlases.push(a)),a.draw(r,i,n),this.styleKeyToAtlas.set(r,a)),a},"draw")},{key:"getAtlas",value:o(function(r){return this.styleKeyToAtlas.get(r)},"getAtlas")},{key:"hasAtlas",value:o(function(r){return this.styleKeyToAtlas.has(r)},"hasAtlas")},{key:"markKeyForGC",value:o(function(r){this.markedKeys.add(r)},"markKeyForGC")},{key:"gc",value:o(function(){var r=this,i=this.markedKeys;if(i.size===0){console.log("nothing to garbage collect");return}var n=[],a=new Map,s=null,l=Fs(this.atlases),u;try{var h=o(function(){var f=u.value,p=f.getKeys(),m=mot(i,p);if(m.size===0)return n.push(f),p.forEach(function(C){return a.set(C,f)}),1;s||(s=r._createAtlas(),n.push(s));var g=Fs(p),y;try{for(g.s();!(y=g.n()).done;){var v=y.value;if(!m.has(v)){var x=f.getOffsets(v),b=Dn(x,2),T=b[0],w=b[1];s.canFit({w:T.w+w.w,h:T.h})||(s.lock(),s=r._createAtlas(),n.push(s)),f.canvas&&(r._copyTextureToNewAtlas(v,f,s),a.set(v,s))}}}catch(C){g.e(C)}finally{g.f()}f.dispose()},"_loop");for(l.s();!(u=l.n()).done;)h()}catch(d){l.e(d)}finally{l.f()}this.atlases=n,this.styleKeyToAtlas=a,this.markedKeys=new Set},"gc")},{key:"_copyTextureToNewAtlas",value:o(function(r,i,n){var a=i.getOffsets(r),s=Dn(a,2),l=s[0],u=s[1];if(u.w===0)n.draw(r,l,function(p){p.drawImage(i.canvas,l.x,l.y,l.w,l.h,0,0,l.w,l.h)});else{var h=this._getScratchCanvas();h.clear(),h.context.drawImage(i.canvas,l.x,l.y,l.w,l.h,0,0,l.w,l.h),h.context.drawImage(i.canvas,u.x,u.y,u.w,u.h,l.w,0,u.w,u.h);var d=l.w+u.w,f=l.h;n.draw(r,{w:d,h:f},function(p){p.drawImage(h,0,0,d,f,0,0,d,f)})}},"_copyTextureToNewAtlas")},{key:"getCounts",value:o(function(){return{keyCount:this.styleKeyToAtlas.size,atlasCount:new Set(this.styleKeyToAtlas.values()).size}},"getCounts")}])})();o(mot,"intersection");got=(function(){function e(t,r){Kd(this,e),this.r=t,this.globalOptions=r,this.atlasSize=r.webglTexSize,this.maxAtlasesPerBatch=r.webglTexPerBatch,this.renderTypes=new Map,this.collections=new Map,this.typeAndIdToKey=new Map}return o(e,"AtlasManager"),Zd(e,[{key:"getAtlasSize",value:o(function(){return this.atlasSize},"getAtlasSize")},{key:"addAtlasCollection",value:o(function(r,i){var n=this.globalOptions,a=n.webglTexSize,s=n.createTextureCanvas,l=i.texRows,u=this._cacheScratchCanvas(s),h=new pot(this.r,a,l,u);this.collections.set(r,h)},"addAtlasCollection")},{key:"addRenderType",value:o(function(r,i){var n=i.collection;if(!this.collections.has(n))throw new Error("invalid atlas collection name '".concat(n,"'"));var a=this.collections.get(n),s=hr({type:r,atlasCollection:a},i);this.renderTypes.set(r,s)},"addRenderType")},{key:"getRenderTypeOpts",value:o(function(r){return this.renderTypes.get(r)},"getRenderTypeOpts")},{key:"getAtlasCollection",value:o(function(r){return this.collections.get(r)},"getAtlasCollection")},{key:"_cacheScratchCanvas",value:o(function(r){var i=-1,n=-1,a=null;return function(s,l,u,h){return h?((!a||l!=i||u!=n)&&(i=l,n=u,a=r(s,l,u)),a):r(s,l,u)}},"_cacheScratchCanvas")},{key:"_key",value:o(function(r,i){return"".concat(r,"-").concat(i)},"_key")},{key:"invalidate",value:o(function(r){var i=this,n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},a=n.forceRedraw,s=a===void 0?!1:a,l=n.filterEle,u=l===void 0?function(){return!0}:l,h=n.filterType,d=h===void 0?function(){return!0}:h,f=!1,p=!1,m=Fs(r),g;try{for(m.s();!(g=m.n()).done;){var y=g.value;if(u(y)){var v=Fs(this.renderTypes.values()),x;try{var b=o(function(){var w=x.value,C=w.type;if(d(C)){var k=i.collections.get(w.collection),E=w.getKey(y),A=Array.isArray(E)?E:[E];if(s)A.forEach(function(D){return k.markKeyForGC(D)}),p=!0;else{var N=w.getID?w.getID(y):y.id(),P=i._key(C,N),I=i.typeAndIdToKey.get(P);I!==void 0&&!not(A,I)&&(f=!0,i.typeAndIdToKey.delete(P),I.forEach(function(D){return k.markKeyForGC(D)}))}}},"_loop2");for(v.s();!(x=v.n()).done;)b()}catch(T){v.e(T)}finally{v.f()}}}}catch(T){m.e(T)}finally{m.f()}return p&&(this.gc(),f=!1),f},"invalidate")},{key:"gc",value:o(function(){var r=Fs(this.collections.values()),i;try{for(r.s();!(i=r.n()).done;){var n=i.value;n.gc()}}catch(a){r.e(a)}finally{r.f()}},"gc")},{key:"getOrCreateAtlas",value:o(function(r,i,n,a){var s=this.renderTypes.get(i),l=this.collections.get(s.collection),u=!1,h=l.draw(a,n,function(p){s.drawClipped?(p.save(),p.beginPath(),p.rect(0,0,n.w,n.h),p.clip(),s.drawElement(p,r,n,!0,!0),p.restore()):s.drawElement(p,r,n,!0,!0),u=!0});if(u){var d=s.getID?s.getID(r):r.id(),f=this._key(i,d);this.typeAndIdToKey.has(f)?this.typeAndIdToKey.get(f).push(a):this.typeAndIdToKey.set(f,[a])}return h},"getOrCreateAtlas")},{key:"getAtlasInfo",value:o(function(r,i){var n=this,a=this.renderTypes.get(i),s=a.getKey(r),l=Array.isArray(s)?s:[s];return l.map(function(u){var h=a.getBoundingBox(r,u),d=n.getOrCreateAtlas(r,i,h,u),f=d.getOffsets(u),p=Dn(f,2),m=p[0],g=p[1];return{atlas:d,tex:m,tex1:m,tex2:g,bb:h}})},"getAtlasInfo")},{key:"getDebugInfo",value:o(function(){var r=[],i=Fs(this.collections),n;try{for(i.s();!(n=i.n()).done;){var a=Dn(n.value,2),s=a[0],l=a[1],u=l.getCounts(),h=u.keyCount,d=u.atlasCount;r.push({type:s,keyCount:h,atlasCount:d})}}catch(f){i.e(f)}finally{i.f()}return r},"getDebugInfo")}])})(),yot=(function(){function e(t){Kd(this,e),this.globalOptions=t,this.atlasSize=t.webglTexSize,this.maxAtlasesPerBatch=t.webglTexPerBatch,this.batchAtlases=[]}return o(e,"AtlasBatchManager"),Zd(e,[{key:"getMaxAtlasesPerBatch",value:o(function(){return this.maxAtlasesPerBatch},"getMaxAtlasesPerBatch")},{key:"getAtlasSize",value:o(function(){return this.atlasSize},"getAtlasSize")},{key:"getIndexArray",value:o(function(){return Array.from({length:this.maxAtlasesPerBatch},function(r,i){return i})},"getIndexArray")},{key:"startBatch",value:o(function(){this.batchAtlases=[]},"startBatch")},{key:"getAtlasCount",value:o(function(){return this.batchAtlases.length},"getAtlasCount")},{key:"getAtlases",value:o(function(){return this.batchAtlases},"getAtlases")},{key:"canAddToCurrentBatch",value:o(function(r){return this.batchAtlases.length===this.maxAtlasesPerBatch?this.batchAtlases.includes(r):!0},"canAddToCurrentBatch")},{key:"getAtlasIndexForBatch",value:o(function(r){var i=this.batchAtlases.indexOf(r);if(i<0){if(this.batchAtlases.length===this.maxAtlasesPerBatch)throw new Error("cannot add more atlases to batch");this.batchAtlases.push(r),i=this.batchAtlases.length-1}return i},"getAtlasIndexForBatch")}])})(),vot=`
+  float circleSD(vec2 p, float r) {
+    return distance(vec2(0), p) - r; // signed distance
+  }
+`,xot=`
+  float rectangleSD(vec2 p, vec2 b) {
+    vec2 d = abs(p)-b;
+    return distance(vec2(0),max(d,0.0)) + min(max(d.x,d.y),0.0);
+  }
+`,bot=`
+  float roundRectangleSD(vec2 p, vec2 b, vec4 cr) {
+    cr.xy = (p.x > 0.0) ? cr.xy : cr.zw;
+    cr.x  = (p.y > 0.0) ? cr.x  : cr.y;
+    vec2 q = abs(p) - b + cr.x;
+    return min(max(q.x, q.y), 0.0) + distance(vec2(0), max(q, 0.0)) - cr.x;
+  }
+`,Tot=`
+  float ellipseSD(vec2 p, vec2 ab) {
+    p = abs( p ); // symmetry
+
+    // find root with Newton solver
+    vec2 q = ab*(p-ab);
+    float w = (q.x<q.y)? 1.570796327 : 0.0;
+    for( int i=0; i<5; i++ ) {
+      vec2 cs = vec2(cos(w),sin(w));
+      vec2 u = ab*vec2( cs.x,cs.y);
+      vec2 v = ab*vec2(-cs.y,cs.x);
+      w = w + dot(p-u,v)/(dot(p-u,u)+dot(v,v));
+    }
+    
+    // compute final point and distance
+    float d = length(p-ab*vec2(cos(w),sin(w)));
+    
+    // return signed distance
+    return (dot(p/ab,p/ab)>1.0) ? d : -d;
+  }
+`,Ub={SCREEN:{name:"screen",screen:!0},PICKING:{name:"picking",picking:!0}},U3={IGNORE:1,USE_BB:2},$O=0,$0e=1,z0e=2,zO=3,a1=4,T3=5,Fb=6,$b=7,Cot=(function(){function e(t,r,i){Kd(this,e),this.r=t,this.gl=r,this.maxInstances=i.webglBatchSize,this.atlasSize=i.webglTexSize,this.bgColor=i.bgColor,this.debug=i.webglDebug,this.batchDebugInfo=[],i.enableWrapping=!0,i.createTextureCanvas=eot,this.atlasManager=new got(t,i),this.batchManager=new yot(i),this.simpleShapeOptions=new Map,this.program=this._createShaderProgram(Ub.SCREEN),this.pickingProgram=this._createShaderProgram(Ub.PICKING),this.vao=this._createVAO()}return o(e,"ElementDrawingWebGL"),Zd(e,[{key:"addAtlasCollection",value:o(function(r,i){this.atlasManager.addAtlasCollection(r,i)},"addAtlasCollection")},{key:"addTextureAtlasRenderType",value:o(function(r,i){this.atlasManager.addRenderType(r,i)},"addTextureAtlasRenderType")},{key:"addSimpleShapeRenderType",value:o(function(r,i){this.simpleShapeOptions.set(r,i)},"addSimpleShapeRenderType")},{key:"invalidate",value:o(function(r){var i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},n=i.type,a=this.atlasManager;return n?a.invalidate(r,{filterType:o(function(l){return l===n},"filterType"),forceRedraw:!0}):a.invalidate(r)},"invalidate")},{key:"gc",value:o(function(){this.atlasManager.gc()},"gc")},{key:"_createShaderProgram",value:o(function(r){var i=this.gl,n=`#version 300 es
+      precision highp float;
+
+      uniform mat3 uPanZoomMatrix;
+      uniform int  uAtlasSize;
+      
+      // instanced
+      in vec2 aPosition; // a vertex from the unit square
+      
+      in mat3 aTransform; // used to transform verticies, eg into a bounding box
+      in int aVertType; // the type of thing we are rendering
+
+      // the z-index that is output when using picking mode
+      in vec4 aIndex;
+      
+      // For textures
+      in int aAtlasId; // which shader unit/atlas to use
+      in vec4 aTex; // x/y/w/h of texture in atlas
+
+      // for edges
+      in vec4 aPointAPointB;
+      in vec4 aPointCPointD;
+      in vec2 aLineWidth; // also used for node border width
+
+      // simple shapes
+      in vec4 aCornerRadius; // for round-rectangle [top-right, bottom-right, top-left, bottom-left]
+      in vec4 aColor; // also used for edges
+      in vec4 aBorderColor; // aLineWidth is used for border width
+
+      // output values passed to the fragment shader
+      out vec2 vTexCoord;
+      out vec4 vColor;
+      out vec2 vPosition;
+      // flat values are not interpolated
+      flat out int vAtlasId; 
+      flat out int vVertType;
+      flat out vec2 vTopRight;
+      flat out vec2 vBotLeft;
+      flat out vec4 vCornerRadius;
+      flat out vec4 vBorderColor;
+      flat out vec2 vBorderWidth;
+      flat out vec4 vIndex;
+      
+      void main(void) {
+        int vid = gl_VertexID;
+        vec2 position = aPosition; // TODO make this a vec3, simplifies some code below
+
+        if(aVertType == `.concat($O,`) {
+          float texX = aTex.x; // texture coordinates
+          float texY = aTex.y;
+          float texW = aTex.z;
+          float texH = aTex.w;
+
+          if(vid == 1 || vid == 2 || vid == 4) {
+            texX += texW;
+          }
+          if(vid == 2 || vid == 4 || vid == 5) {
+            texY += texH;
+          }
+
+          float d = float(uAtlasSize);
+          vTexCoord = vec2(texX / d, texY / d); // tex coords must be between 0 and 1
+
+          gl_Position = vec4(uPanZoomMatrix * aTransform * vec3(position, 1.0), 1.0);
+        }
+        else if(aVertType == `).concat(a1," || aVertType == ").concat($b,` 
+             || aVertType == `).concat(T3," || aVertType == ").concat(Fb,`) { // simple shapes
+
+          // the bounding box is needed by the fragment shader
+          vBotLeft  = (aTransform * vec3(0, 0, 1)).xy; // flat
+          vTopRight = (aTransform * vec3(1, 1, 1)).xy; // flat
+          vPosition = (aTransform * vec3(position, 1)).xy; // will be interpolated
+
+          // calculations are done in the fragment shader, just pass these along
+          vColor = aColor;
+          vCornerRadius = aCornerRadius;
+          vBorderColor = aBorderColor;
+          vBorderWidth = aLineWidth;
+
+          gl_Position = vec4(uPanZoomMatrix * aTransform * vec3(position, 1.0), 1.0);
+        }
+        else if(aVertType == `).concat($0e,`) {
+          vec2 source = aPointAPointB.xy;
+          vec2 target = aPointAPointB.zw;
+
+          // adjust the geometry so that the line is centered on the edge
+          position.y = position.y - 0.5;
+
+          // stretch the unit square into a long skinny rectangle
+          vec2 xBasis = target - source;
+          vec2 yBasis = normalize(vec2(-xBasis.y, xBasis.x));
+          vec2 point = source + xBasis * position.x + yBasis * aLineWidth[0] * position.y;
+
+          gl_Position = vec4(uPanZoomMatrix * vec3(point, 1.0), 1.0);
+          vColor = aColor;
+        } 
+        else if(aVertType == `).concat(z0e,`) {
+          vec2 pointA = aPointAPointB.xy;
+          vec2 pointB = aPointAPointB.zw;
+          vec2 pointC = aPointCPointD.xy;
+          vec2 pointD = aPointCPointD.zw;
+
+          // adjust the geometry so that the line is centered on the edge
+          position.y = position.y - 0.5;
+
+          vec2 p0, p1, p2, pos;
+          if(position.x == 0.0) { // The left side of the unit square
+            p0 = pointA;
+            p1 = pointB;
+            p2 = pointC;
+            pos = position;
+          } else { // The right side of the unit square, use same approach but flip the geometry upside down
+            p0 = pointD;
+            p1 = pointC;
+            p2 = pointB;
+            pos = vec2(0.0, -position.y);
+          }
+
+          vec2 p01 = p1 - p0;
+          vec2 p12 = p2 - p1;
+          vec2 p21 = p1 - p2;
+
+          // Find the normal vector.
+          vec2 tangent = normalize(normalize(p12) + normalize(p01));
+          vec2 normal = vec2(-tangent.y, tangent.x);
+
+          // Find the vector perpendicular to p0 -> p1.
+          vec2 p01Norm = normalize(vec2(-p01.y, p01.x));
+
+          // Determine the bend direction.
+          float sigma = sign(dot(p01 + p21, normal));
+          float width = aLineWidth[0];
+
+          if(sign(pos.y) == -sigma) {
+            // This is an intersecting vertex. Adjust the position so that there's no overlap.
+            vec2 point = 0.5 * width * normal * -sigma / dot(normal, p01Norm);
+            gl_Position = vec4(uPanZoomMatrix * vec3(p1 + point, 1.0), 1.0);
+          } else {
+            // This is a non-intersecting vertex. Treat it like a mitre join.
+            vec2 point = 0.5 * width * normal * sigma * dot(normal, p01Norm);
+            gl_Position = vec4(uPanZoomMatrix * vec3(p1 + point, 1.0), 1.0);
+          }
+
+          vColor = aColor;
+        } 
+        else if(aVertType == `).concat(zO,` && vid < 3) {
+          // massage the first triangle into an edge arrow
+          if(vid == 0)
+            position = vec2(-0.15, -0.3);
+          if(vid == 1)
+            position = vec2(  0.0,  0.0);
+          if(vid == 2)
+            position = vec2( 0.15, -0.3);
+
+          gl_Position = vec4(uPanZoomMatrix * aTransform * vec3(position, 1.0), 1.0);
+          vColor = aColor;
+        }
+        else {
+          gl_Position = vec4(2.0, 0.0, 0.0, 1.0); // discard vertex by putting it outside webgl clip space
+        }
+
+        vAtlasId = aAtlasId;
+        vVertType = aVertType;
+        vIndex = aIndex;
+      }
+    `),a=this.batchManager.getIndexArray(),s=`#version 300 es
+      precision highp float;
+
+      // declare texture unit for each texture atlas in the batch
+      `.concat(a.map(function(h){return"uniform sampler2D uTexture".concat(h,";")}).join(`
+	`),`
+
+      uniform vec4 uBGColor;
+      uniform float uZoom;
+
+      in vec2 vTexCoord;
+      in vec4 vColor;
+      in vec2 vPosition; // model coordinates
+
+      flat in int vAtlasId;
+      flat in vec4 vIndex;
+      flat in int vVertType;
+      flat in vec2 vTopRight;
+      flat in vec2 vBotLeft;
+      flat in vec4 vCornerRadius;
+      flat in vec4 vBorderColor;
+      flat in vec2 vBorderWidth;
+
+      out vec4 outColor;
+
+      `).concat(vot,`
+      `).concat(xot,`
+      `).concat(bot,`
+      `).concat(Tot,`
+
+      vec4 blend(vec4 top, vec4 bot) { // blend colors with premultiplied alpha
+        return vec4( 
+          top.rgb + (bot.rgb * (1.0 - top.a)),
+          top.a   + (bot.a   * (1.0 - top.a)) 
+        );
+      }
+
+      vec4 distInterp(vec4 cA, vec4 cB, float d) { // interpolate color using Signed Distance
+        // scale to the zoom level so that borders don't look blurry when zoomed in
+        // note 1.5 is an aribitrary value chosen because it looks good
+        return mix(cA, cB, 1.0 - smoothstep(0.0, 1.5 / uZoom, abs(d))); 
+      }
+
+      void main(void) {
+        if(vVertType == `).concat($O,`) {
+          // look up the texel from the texture unit
+          `).concat(a.map(function(h){return"if(vAtlasId == ".concat(h,") outColor = texture(uTexture").concat(h,", vTexCoord);")}).join(`
+	else `),`
+        } 
+        else if(vVertType == `).concat(zO,`) {
+          // mimics how canvas renderer uses context.globalCompositeOperation = 'destination-out';
+          outColor = blend(vColor, uBGColor);
+          outColor.a = 1.0; // make opaque, masks out line under arrow
+        }
+        else if(vVertType == `).concat(a1,` && vBorderWidth == vec2(0.0)) { // simple rectangle with no border
+          outColor = vColor; // unit square is already transformed to the rectangle, nothing else needs to be done
+        }
+        else if(vVertType == `).concat(a1," || vVertType == ").concat($b,` 
+          || vVertType == `).concat(T3," || vVertType == ").concat(Fb,`) { // use SDF
+
+          float outerBorder = vBorderWidth[0];
+          float innerBorder = vBorderWidth[1];
+          float borderPadding = outerBorder * 2.0;
+          float w = vTopRight.x - vBotLeft.x - borderPadding;
+          float h = vTopRight.y - vBotLeft.y - borderPadding;
+          vec2 b = vec2(w/2.0, h/2.0); // half width, half height
+          vec2 p = vPosition - vec2(vTopRight.x - b[0] - outerBorder, vTopRight.y - b[1] - outerBorder); // translate to center
+
+          float d; // signed distance
+          if(vVertType == `).concat(a1,`) {
+            d = rectangleSD(p, b);
+          } else if(vVertType == `).concat($b,` && w == h) {
+            d = circleSD(p, b.x); // faster than ellipse
+          } else if(vVertType == `).concat($b,`) {
+            d = ellipseSD(p, b);
+          } else {
+            d = roundRectangleSD(p, b, vCornerRadius.wzyx);
+          }
+
+          // use the distance to interpolate a color to smooth the edges of the shape, doesn't need multisampling
+          // we must smooth colors inwards, because we can't change pixels outside the shape's bounding box
+          if(d > 0.0) {
+            if(d > outerBorder) {
+              discard;
+            } else {
+              outColor = distInterp(vBorderColor, vec4(0), d - outerBorder);
+            }
+          } else {
+            if(d > innerBorder) {
+              vec4 outerColor = outerBorder == 0.0 ? vec4(0) : vBorderColor;
+              vec4 innerBorderColor = blend(vBorderColor, vColor);
+              outColor = distInterp(innerBorderColor, outerColor, d);
+            } 
+            else {
+              vec4 outerColor;
+              if(innerBorder == 0.0 && outerBorder == 0.0) {
+                outerColor = vec4(0);
+              } else if(innerBorder == 0.0) {
+                outerColor = vBorderColor;
+              } else {
+                outerColor = blend(vBorderColor, vColor);
+              }
+              outColor = distInterp(vColor, outerColor, d - innerBorder);
+            }
+          }
+        }
+        else {
+          outColor = vColor;
+        }
+
+        `).concat(r.picking?`if(outColor.a == 0.0) discard;
+             else outColor = vIndex;`:"",`
+      }
+    `),l=Jst(i,n,s);l.aPosition=i.getAttribLocation(l,"aPosition"),l.aIndex=i.getAttribLocation(l,"aIndex"),l.aVertType=i.getAttribLocation(l,"aVertType"),l.aTransform=i.getAttribLocation(l,"aTransform"),l.aAtlasId=i.getAttribLocation(l,"aAtlasId"),l.aTex=i.getAttribLocation(l,"aTex"),l.aPointAPointB=i.getAttribLocation(l,"aPointAPointB"),l.aPointCPointD=i.getAttribLocation(l,"aPointCPointD"),l.aLineWidth=i.getAttribLocation(l,"aLineWidth"),l.aColor=i.getAttribLocation(l,"aColor"),l.aCornerRadius=i.getAttribLocation(l,"aCornerRadius"),l.aBorderColor=i.getAttribLocation(l,"aBorderColor"),l.uPanZoomMatrix=i.getUniformLocation(l,"uPanZoomMatrix"),l.uAtlasSize=i.getUniformLocation(l,"uAtlasSize"),l.uBGColor=i.getUniformLocation(l,"uBGColor"),l.uZoom=i.getUniformLocation(l,"uZoom"),l.uTextures=[];for(var u=0;u<this.batchManager.getMaxAtlasesPerBatch();u++)l.uTextures.push(i.getUniformLocation(l,"uTexture".concat(u)));return l},"_createShaderProgram")},{key:"_createVAO",value:o(function(){var r=[0,0,1,0,1,1,0,0,1,1,0,1];this.vertexCount=r.length/2;var i=this.maxInstances,n=this.gl,a=this.program,s=n.createVertexArray();return n.bindVertexArray(s),lot(n,"vec2",a.aPosition,r),this.transformBuffer=cot(n,i,a.aTransform),this.indexBuffer=Oc(n,i,"vec4",a.aIndex),this.vertTypeBuffer=Oc(n,i,"int",a.aVertType),this.atlasIdBuffer=Oc(n,i,"int",a.aAtlasId),this.texBuffer=Oc(n,i,"vec4",a.aTex),this.pointAPointBBuffer=Oc(n,i,"vec4",a.aPointAPointB),this.pointCPointDBuffer=Oc(n,i,"vec4",a.aPointCPointD),this.lineWidthBuffer=Oc(n,i,"vec2",a.aLineWidth),this.colorBuffer=Oc(n,i,"vec4",a.aColor),this.cornerRadiusBuffer=Oc(n,i,"vec4",a.aCornerRadius),this.borderColorBuffer=Oc(n,i,"vec4",a.aBorderColor),n.bindVertexArray(null),s},"_createVAO")},{key:"buffers",get:o(function(){var r=this;return this._buffers||(this._buffers=Object.keys(this).filter(function(i){return $d(i,"Buffer")}).map(function(i){return r[i]})),this._buffers},"get")},{key:"startFrame",value:o(function(r){var i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:Ub.SCREEN;this.panZoomMatrix=r,this.renderTarget=i,this.batchDebugInfo=[],this.wrappedCount=0,this.simpleCount=0,this.startBatch()},"startFrame")},{key:"startBatch",value:o(function(){this.instanceCount=0,this.batchManager.startBatch()},"startBatch")},{key:"endFrame",value:o(function(){this.endBatch()},"endFrame")},{key:"_isVisible",value:o(function(r,i){return r.visible()?i&&i.isVisible?i.isVisible(r):!0:!1},"_isVisible")},{key:"drawTexture",value:o(function(r,i,n){var a=this.atlasManager,s=this.batchManager,l=a.getRenderTypeOpts(n);if(this._isVisible(r,l)&&!(r.isEdge()&&!this._isValidEdge(r))){if(this.renderTarget.picking&&l.getTexPickingMode){var u=l.getTexPickingMode(r);if(u===U3.IGNORE)return;if(u==U3.USE_BB){this.drawPickingRectangle(r,i,n);return}}var h=a.getAtlasInfo(r,n),d=Fs(h),f;try{for(d.s();!(f=d.n()).done;){var p=f.value,m=p.atlas,g=p.tex1,y=p.tex2;s.canAddToCurrentBatch(m)||this.endBatch();for(var v=s.getAtlasIndexForBatch(m),x=0,b=[[g,!0],[y,!1]];x<b.length;x++){var T=Dn(b[x],2),w=T[0],C=T[1];if(w.w!=0){var k=this.instanceCount;this.vertTypeBuffer.getView(k)[0]=$O;var E=this.indexBuffer.getView(k);n1(i,E);var A=this.atlasIdBuffer.getView(k);A[0]=v;var N=this.texBuffer.getView(k);N[0]=w.x,N[1]=w.y,N[2]=w.w,N[3]=w.h;var P=this.transformBuffer.getMatrixView(k);this.setTransformMatrix(r,P,l,p,C),this.instanceCount++,C||this.wrappedCount++,this.instanceCount>=this.maxInstances&&this.endBatch()}}}}catch(I){d.e(I)}finally{d.f()}}},"drawTexture")},{key:"setTransformMatrix",value:o(function(r,i,n,a){var s=arguments.length>4&&arguments[4]!==void 0?arguments[4]:!0,l=0;if(n.shapeProps&&n.shapeProps.padding&&(l=r.pstyle(n.shapeProps.padding).pfValue),a){var u=a.bb,h=a.tex1,d=a.tex2,f=h.w/(h.w+d.w);s||(f=1-f);var p=this._getAdjustedBB(u,l,s,f);this._applyTransformMatrix(i,p,n,r)}else{var m=n.getBoundingBox(r),g=this._getAdjustedBB(m,l,!0,1);this._applyTransformMatrix(i,g,n,r)}},"setTransformMatrix")},{key:"_applyTransformMatrix",value:o(function(r,i,n,a){var s,l;B0e(r);var u=n.getRotation?n.getRotation(a):0;if(u!==0){var h=n.getRotationPoint(a),d=h.x,f=h.y;M3(r,r,[d,f]),F0e(r,r,u);var p=n.getRotationOffset(a);s=p.x+(i.xOffset||0),l=p.y+(i.yOffset||0)}else s=i.x1,l=i.y1;M3(r,r,[s,l]),sP(r,r,[i.w,i.h])},"_applyTransformMatrix")},{key:"_getAdjustedBB",value:o(function(r,i,n,a){var s=r.x1,l=r.y1,u=r.w,h=r.h,d=r.yOffset;i&&(s-=i,l-=i,u+=2*i,h+=2*i);var f=0,p=u*a;return n&&a<1?u=p:!n&&a<1&&(f=u-p,s+=f,u=p),{x1:s,y1:l,w:u,h,xOffset:f,yOffset:d}},"_getAdjustedBB")},{key:"drawPickingRectangle",value:o(function(r,i,n){var a=this.atlasManager.getRenderTypeOpts(n),s=this.instanceCount;this.vertTypeBuffer.getView(s)[0]=a1;var l=this.indexBuffer.getView(s);n1(i,l);var u=this.colorBuffer.getView(s);Hp([0,0,0],1,u);var h=this.transformBuffer.getMatrixView(s);this.setTransformMatrix(r,h,a),this.simpleCount++,this.instanceCount++,this.instanceCount>=this.maxInstances&&this.endBatch()},"drawPickingRectangle")},{key:"drawNode",value:o(function(r,i,n){var a=this.simpleShapeOptions.get(n);if(this._isVisible(r,a)){var s=a.shapeProps,l=this._getVertTypeForShape(r,s.shape);if(l===void 0||a.isSimple&&!a.isSimple(r)){this.drawTexture(r,i,n);return}var u=this.instanceCount;if(this.vertTypeBuffer.getView(u)[0]=l,l===T3||l===Fb){var h=a.getBoundingBox(r),d=this._getCornerRadius(r,s.radius,h),f=this.cornerRadiusBuffer.getView(u);f[0]=d,f[1]=d,f[2]=d,f[3]=d,l===Fb&&(f[0]=0,f[2]=0)}var p=this.indexBuffer.getView(u);n1(i,p);var m=r.pstyle(s.color).value,g=r.pstyle(s.opacity).value,y=this.colorBuffer.getView(u);Hp(m,g,y);var v=this.lineWidthBuffer.getView(u);if(v[0]=0,v[1]=0,s.border){var x=r.pstyle("border-width").value;if(x>0){var b=r.pstyle("border-color").value,T=r.pstyle("border-opacity").value,w=this.borderColorBuffer.getView(u);Hp(b,T,w);var C=r.pstyle("border-position").value;if(C==="inside")v[0]=0,v[1]=-x;else if(C==="outside")v[0]=x,v[1]=0;else{var k=x/2;v[0]=k,v[1]=-k}}}var E=this.transformBuffer.getMatrixView(u);this.setTransformMatrix(r,E,a),this.simpleCount++,this.instanceCount++,this.instanceCount>=this.maxInstances&&this.endBatch()}},"drawNode")},{key:"_getVertTypeForShape",value:o(function(r,i){var n=r.pstyle(i).value;switch(n){case"rectangle":return a1;case"ellipse":return $b;case"roundrectangle":case"round-rectangle":return T3;case"bottom-round-rectangle":return Fb;default:return}},"_getVertTypeForShape")},{key:"_getCornerRadius",value:o(function(r,i,n){var a=n.w,s=n.h;if(r.pstyle(i).value==="auto")return Hd(a,s);var l=r.pstyle(i).pfValue,u=a/2,h=s/2;return Math.min(l,h,u)},"_getCornerRadius")},{key:"drawEdgeArrow",value:o(function(r,i,n){if(r.visible()){var a=r._private.rscratch,s,l,u;if(n==="source"?(s=a.arrowStartX,l=a.arrowStartY,u=a.srcArrowAngle):(s=a.arrowEndX,l=a.arrowEndY,u=a.tgtArrowAngle),!(isNaN(s)||s==null||isNaN(l)||l==null||isNaN(u)||u==null)){var h=r.pstyle(n+"-arrow-shape").value;if(h!=="none"){var d=r.pstyle(n+"-arrow-color").value,f=r.pstyle("opacity").value,p=r.pstyle("line-opacity").value,m=f*p,g=r.pstyle("width").pfValue,y=r.pstyle("arrow-scale").value,v=this.r.getArrowWidth(g,y),x=this.instanceCount,b=this.transformBuffer.getMatrixView(x);B0e(b),M3(b,b,[s,l]),sP(b,b,[v,v]),F0e(b,b,u),this.vertTypeBuffer.getView(x)[0]=zO;var T=this.indexBuffer.getView(x);n1(i,T);var w=this.colorBuffer.getView(x);Hp(d,m,w),this.instanceCount++,this.instanceCount>=this.maxInstances&&this.endBatch()}}}},"drawEdgeArrow")},{key:"drawEdgeLine",value:o(function(r,i){if(r.visible()){var n=this._getEdgePoints(r);if(n){var 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p=f.getRight()-f.getLeft(),m=f.getBottom()-f.getTop();this.labelWidth>p&&(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)),this.labelHeight>m&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-m)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-m),this.setHeight(this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==n.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(f){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=f.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return 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Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(i.prototype);for(var m in i)p[m]=i[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,T=0;T<b;T++)x=v[T],x.isInterGraph?this.graphManager.remove(x):x.source.owner.remove(x);var w=this.nodes.indexOf(y);if(w==-1)throw"Node not in owner node list!";this.nodes.splice(w,1)}else if(g instanceof u){var x=g;if(x==null)throw"Edge is null!";if(!(x.source!=null&&x.target!=null))throw"Source and/or target is null!";if(!(x.source.owner!=null&&x.target.owner!=null&&x.source.owner==this&&x.target.owner==this))throw"Source and/or target owner is invalid!";var C=x.source.edges.indexOf(x),k=x.target.edges.indexOf(x);if(!(C>-1&&k>-1))throw"Source and/or target doesn't 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h(y,x,v-y,b-x);y==n.MAX_VALUE&&(this.left=this.parent.getLeft(),this.right=this.parent.getRight(),this.top=this.parent.getTop(),this.bottom=this.parent.getBottom()),A[0].getParent().paddingLeft!=null?E=A[0].getParent().paddingLeft:E=this.margin,this.left=D.x-E,this.right=D.x+D.width+E,this.top=D.y-E,this.bottom=D.y+D.height+E},p.calculateBounds=function(g){for(var y=n.MAX_VALUE,v=-n.MAX_VALUE,x=n.MAX_VALUE,b=-n.MAX_VALUE,T,w,C,k,E=g.length,A=0;A<E;A++){var N=g[A];T=N.getLeft(),w=N.getRight(),C=N.getTop(),k=N.getBottom(),y>T&&(y=T),v<w&&(v=w),x>C&&(x=C),b<k&&(b=k)}var P=new h(y,x,v-y,b-x);return P},p.prototype.getInclusionTreeDepth=function(){return this==this.graphManager.getRoot()?1:this.parent.getInclusionTreeDepth()},p.prototype.getEstimatedSize=function(){if(this.estimatedSize==n.MIN_VALUE)throw"assert failed";return this.estimatedSize},p.prototype.calcEstimatedSize=function(){for(var g=0,y=this.nodes,v=y.length,x=0;x<v;x++){var b=y[x];g+=b.calcEstimatedSize()}return g==0?this.estimatedSize=a.EMPTY_COMPOUND_NODE_SIZE:this.estimatedSize=g/Math.sqrt(this.nodes.length),this.estimatedSize},p.prototype.updateConnected=function(){var g=this;if(this.nodes.length==0){this.isConnected=!0;return}var y=new f,v=new Set,x=this.nodes[0],b,T,w=x.withChildren();for(w.forEach(function(P){y.push(P),v.add(P)});y.length!==0;){x=y.shift(),b=x.getEdges();for(var C=b.length,k=0;k<C;k++){var E=b[k];if(T=E.getOtherEndInGraph(x,this),T!=null&&!v.has(T)){var A=T.withChildren();A.forEach(function(P){y.push(P),v.add(P)})}}}if(this.isConnected=!1,v.size>=this.nodes.length){var N=0;v.forEach(function(P){P.owner==g&&N++}),N==this.nodes.length&&(this.isConnected=!0)}},e.exports=p}),(function(e,t,r){"use strict";var i,n=r(1);function a(s){i=r(5),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,d){if(u==null&&h==null&&d==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{d=u,h=l,u=s;var f=h.getOwner(),p=d.getOwner();if(!(f!=null&&f.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(f==p)return u.isInterGraph=!1,f.add(u,h,d);if(u.isInterGraph=!0,u.source=h,u.target=d,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof i){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,d=u.length,f=0;f<d;f++)h=u[f],l.remove(h);var p=[];p=p.concat(l.getNodes());var m;d=p.length;for(var f=0;f<d;f++)m=p[f],l.remove(m);l==this.rootGraph&&this.setRootGraph(null);var g=this.graphs.indexOf(l);this.graphs.splice(g,1),l.parent=null}else if(s instanceof n){if(h=s,h==null)throw"Edge is null!";if(!h.isInterGraph)throw"Not an inter-graph edge!";if(!(h.source!=null&&h.target!=null))throw"Source and/or target is null!";if(!(h.source.edges.indexOf(h)!=-1&&h.target.edges.indexOf(h)!=-1))throw"Source and/or target doesn't know this edge!";var g=h.source.edges.indexOf(h);if(h.source.edges.splice(g,1),g=h.target.edges.indexOf(h),h.target.edges.splice(g,1),!(h.source.owner!=null&&h.source.owner.getGraphManager()!=null))throw"Edge owner graph or owner graph manager is null!";if(h.source.owner.getGraphManager().edges.indexOf(h)==-1)throw"Not in owner graph manager's edge list!";var g=h.source.owner.getGraphManager().edges.indexOf(h);h.source.owner.getGraphManager().edges.splice(g,1)}},a.prototype.updateBounds=function(){this.rootGraph.updateBounds(!0)},a.prototype.getGraphs=function(){return this.graphs},a.prototype.getAllNodes=function(){if(this.allNodes==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<u;h++)s=s.concat(l[h].getNodes());this.allNodes=s}return this.allNodes},a.prototype.resetAllNodes=function(){this.allNodes=null},a.prototype.resetAllEdges=function(){this.allEdges=null},a.prototype.resetAllNodesToApplyGravitation=function(){this.allNodesToApplyGravitation=null},a.prototype.getAllEdges=function(){if(this.allEdges==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<l.length;h++)s=s.concat(l[h].getEdges());s=s.concat(this.edges),this.allEdges=s}return this.allEdges},a.prototype.getAllNodesToApplyGravitation=function(){return this.allNodesToApplyGravitation},a.prototype.setAllNodesToApplyGravitation=function(s){if(this.allNodesToApplyGravitation!=null)throw"assert failed";this.allNodesToApplyGravitation=s},a.prototype.getRoot=function(){return this.rootGraph},a.prototype.setRootGraph=function(s){if(s.getGraphManager()!=this)throw"Root not in this graph mgr!";this.rootGraph=s,s.parent==null&&(s.parent=this.layout.newNode("Root node"))},a.prototype.getLayout=function(){return this.layout},a.prototype.isOneAncestorOfOther=function(s,l){if(!(s!=null&&l!=null))throw"assert failed";if(s==l)return!0;var u=s.getOwner(),h;do{if(h=u.getParent(),h==null)break;if(h==l)return!0;if(u=h.getOwner(),u==null)break}while(!0);u=l.getOwner();do{if(h=u.getParent(),h==null)break;if(h==s)return!0;if(u=h.getOwner(),u==null)break}while(!0);return!1},a.prototype.calcLowestCommonAncestors=function(){for(var s,l,u,h,d,f=this.getAllEdges(),p=f.length,m=0;m<p;m++){if(s=f[m],l=s.source,u=s.target,s.lca=null,s.sourceInLca=l,s.targetInLca=u,l==u){s.lca=l.getOwner();continue}for(h=l.getOwner();s.lca==null;){for(s.targetInLca=u,d=u.getOwner();s.lca==null;){if(d==h){s.lca=d;break}if(d==this.rootGraph)break;if(s.lca!=null)throw"assert failed";s.targetInLca=d.getParent(),d=s.targetInLca.getOwner()}if(h==this.rootGraph)break;s.lca==null&&(s.sourceInLca=h.getParent(),h=s.sourceInLca.getOwner())}if(s.lca==null)throw"assert failed"}},a.prototype.calcLowestCommonAncestor=function(s,l){if(s==l)return s.getOwner();var u=s.getOwner();do{if(u==null)break;var h=l.getOwner();do{if(h==null)break;if(h==u)return h;h=h.getParent().getOwner()}while(!0);u=u.getParent().getOwner()}while(!0);return u},a.prototype.calcInclusionTreeDepths=function(s,l){s==null&&l==null&&(s=this.rootGraph,l=1);for(var u,h=s.getNodes(),d=h.length,f=0;f<d;f++)u=h[f],u.inclusionTreeDepth=l,u.child!=null&&this.calcInclusionTreeDepths(u.child,l+1)},a.prototype.includesInvalidEdge=function(){for(var s,l=this.edges.length,u=0;u<l;u++)if(s=this.edges[u],this.isOneAncestorOfOther(s.source,s.target))return!0;return!1},e.exports=a}),(function(e,t,r){"use strict";var i=r(0);function n(){}o(n,"FDLayoutConstants");for(var a in 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g,y,v=m.getNodes(),x=0;x<v.length;x++)g=v[x],y=g.getChild(),y==null||y.getNodes().length==0?g.scatter():(this.positionNodesRandomly(y),g.updateBounds())},p.prototype.getFlatForest=function(){for(var m=[],g=!0,y=this.graphManager.getRoot().getNodes(),v=!0,x=0;x<y.length;x++)y[x].getChild()!=null&&(v=!1);if(!v)return m;var b=new Set,T=[],w=new Map,C=[];for(C=C.concat(y);C.length>0&&g;){for(T.push(C[0]);T.length>0&&g;){var k=T[0];T.splice(0,1),b.add(k);for(var E=k.getEdges(),x=0;x<E.length;x++){var A=E[x].getOtherEnd(k);if(w.get(k)!=A)if(!b.has(A))T.push(A),w.set(A,k);else{g=!1;break}}}if(!g)m=[];else{var N=[].concat(i(b));m.push(N);for(var x=0;x<N.length;x++){var P=N[x],I=C.indexOf(P);I>-1&&C.splice(I,1)}b=new Set,w=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x<m.bendpoints.length;x++){var b=this.newNode(null);b.setRect(new Point(0,0),new Dimension(1,1)),v.add(b);var T=this.newEdge(null);this.graphManager.add(T,y,b),g.add(b),y=b}var T=this.newEdge(null);return this.graphManager.add(T,y,m.target),this.edgeToDummyNodes.set(m,g),m.isInterGraph()?this.graphManager.remove(m):v.remove(m),g},p.prototype.createBendpointsFromDummyNodes=function(){var m=[];m=m.concat(this.graphManager.getAllEdges()),m=[].concat(i(this.edgeToDummyNodes.keys())).concat(m);for(var g=0;g<m.length;g++){var y=m[g];if(y.bendpoints.length>0){for(var v=this.edgeToDummyNodes.get(y),x=0;x<v.length;x++){var b=v[x],T=new h(b.getCenterX(),b.getCenterY()),w=y.bendpoints.get(x);w.x=T.x,w.y=T.y,b.getOwner().remove(b)}this.graphManager.add(y,y.source,y.target)}}},p.transform=function(m,g,y,v){if(y!=null&&v!=null){var x=g;if(m<=50){var b=g/y;x-=(g-b)/50*(50-m)}else{var T=g*v;x+=(T-g)/50*(m-50)}return x}else{var w,C;return m<=50?(w=9*g/500,C=g/10):(w=9*g/50,C=-8*g),w*m+C}},p.findCenterOfTree=function(m){var g=[];g=g.concat(m);var y=[],v=new Map,x=!1,b=null;(g.length==1||g.length==2)&&(x=!0,b=g[0]);for(var T=0;T<g.length;T++){var w=g[T],C=w.getNeighborsList().size;v.set(w,w.getNeighborsList().size),C==1&&y.push(w)}var k=[];for(k=k.concat(y);!x;){var E=[];E=E.concat(k),k=[];for(var T=0;T<g.length;T++){var w=g[T],A=g.indexOf(w);A>=0&&g.splice(A,1);var N=w.getNeighborsList();N.forEach(function(D){if(y.indexOf(D)<0){var _=v.get(D),R=_-1;R==1&&k.push(D),v.set(D,R)}})}y=y.concat(k),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},e.exports=p}),(function(e,t,r){"use strict";function i(){}o(i,"RandomSeed"),i.seed=1,i.x=0,i.nextDouble=function(){return i.x=Math.sin(i.seed++)*1e4,i.x-Math.floor(i.x)},e.exports=i}),(function(e,t,r){"use strict";var i=r(4);function n(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(n,"Transform"),n.prototype.getWorldOrgX=function(){return this.lworldOrgX},n.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},n.prototype.getWorldOrgY=function(){return this.lworldOrgY},n.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},n.prototype.getWorldExtX=function(){return this.lworldExtX},n.prototype.setWorldExtX=function(a){this.lworldExtX=a},n.prototype.getWorldExtY=function(){return this.lworldExtY},n.prototype.setWorldExtY=function(a){this.lworldExtY=a},n.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},n.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},n.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},n.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},n.prototype.getDeviceExtX=function(){return this.ldeviceExtX},n.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},n.prototype.getDeviceExtY=function(){return this.ldeviceExtY},n.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},n.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},n.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},n.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},n.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},n.prototype.inverseTransformPoint=function(a){var s=new i(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},e.exports=n}),(function(e,t,r){"use strict";function i(f){if(Array.isArray(f)){for(var p=0,m=Array(f.length);p<f.length;p++)m[p]=f[p];return m}else return Array.from(f)}o(i,"_toConsumableArray");var n=r(15),a=r(7),s=r(0),l=r(8),u=r(9);function h(){n.call(this),this.useSmartIdealEdgeLengthCalculation=a.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.idealEdgeLength=a.DEFAULT_EDGE_LENGTH,this.springConstant=a.DEFAULT_SPRING_STRENGTH,this.repulsionConstant=a.DEFAULT_REPULSION_STRENGTH,this.gravityConstant=a.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=a.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=a.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=a.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.coolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.initialCoolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.totalDisplacement=0,this.oldTotalDisplacement=0,this.maxIterations=a.MAX_ITERATIONS}o(h,"FDLayout"),h.prototype=Object.create(n.prototype);for(var d in n)h[d]=n[d];h.prototype.initParameters=function(){n.prototype.initParameters.call(this,arguments),this.totalIterations=0,this.notAnimatedIterations=0,this.useFRGridVariant=a.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION,this.grid=[]},h.prototype.calcIdealEdgeLengths=function(){for(var f,p,m,g,y,v,x=this.getGraphManager().getAllEdges(),b=0;b<x.length;b++)f=x[b],f.idealLength=this.idealEdgeLength,f.isInterGraph&&(m=f.getSource(),g=f.getTarget(),y=f.getSourceInLca().getEstimatedSize(),v=f.getTargetInLca().getEstimatedSize(),this.useSmartIdealEdgeLengthCalculation&&(f.idealLength+=y+v-2*s.SIMPLE_NODE_SIZE),p=f.getLca().getInclusionTreeDepth(),f.idealLength+=a.DEFAULT_EDGE_LENGTH*a.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR*(m.getInclusionTreeDepth()+g.getInclusionTreeDepth()-2*p))},h.prototype.initSpringEmbedder=function(){var f=this.getAllNodes().length;this.incremental?(f>a.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(f-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(f>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(f-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var f=this.getAllEdges(),p,m=0;m<f.length;m++)p=f[m],this.calcSpringForce(p,p.idealLength)},h.prototype.calcRepulsionForces=function(){var f=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&f&&this.updateGrid(),b=new Set,m=0;m<x.length;m++)y=x[m],this.calculateRepulsionForceOfANode(y,b,f,p),b.add(y);else for(m=0;m<x.length;m++)for(y=x[m],g=m+1;g<x.length;g++)v=x[g],y.getOwner()==v.getOwner()&&this.calcRepulsionForce(y,v)},h.prototype.calcGravitationalForces=function(){for(var f,p=this.getAllNodesToApplyGravitation(),m=0;m<p.length;m++)f=p[m],this.calcGravitationalForce(f)},h.prototype.moveNodes=function(){for(var f=this.getAllNodes(),p,m=0;m<f.length;m++)p=f[m],p.move()},h.prototype.calcSpringForce=function(f,p){var m=f.getSource(),g=f.getTarget(),y,v,x,b;if(this.uniformLeafNodeSizes&&m.getChild()==null&&g.getChild()==null)f.updateLengthSimple();else if(f.updateLength(),f.isOverlapingSourceAndTarget)return;y=f.getLength(),y!=0&&(v=this.springConstant*(y-p),x=v*(f.lengthX/y),b=v*(f.lengthY/y),m.springForceX+=x,m.springForceY+=b,g.springForceX-=x,g.springForceY-=b)},h.prototype.calcRepulsionForce=function(f,p){var m=f.getRect(),g=p.getRect(),y=new Array(2),v=new Array(4),x,b,T,w,C,k,E;if(m.intersects(g)){l.calcSeparationAmount(m,g,y,a.DEFAULT_EDGE_LENGTH/2),k=2*y[0],E=2*y[1];var A=f.noOfChildren*p.noOfChildren/(f.noOfChildren+p.noOfChildren);f.repulsionForceX-=A*k,f.repulsionForceY-=A*E,p.repulsionForceX+=A*k,p.repulsionForceY+=A*E}else this.uniformLeafNodeSizes&&f.getChild()==null&&p.getChild()==null?(x=g.getCenterX()-m.getCenterX(),b=g.getCenterY()-m.getCenterY()):(l.getIntersection(m,g,v),x=v[2]-v[0],b=v[3]-v[1]),Math.abs(x)<a.MIN_REPULSION_DIST&&(x=u.sign(x)*a.MIN_REPULSION_DIST),Math.abs(b)<a.MIN_REPULSION_DIST&&(b=u.sign(b)*a.MIN_REPULSION_DIST),T=x*x+b*b,w=Math.sqrt(T),C=this.repulsionConstant*f.noOfChildren*p.noOfChildren/T,k=C*x/w,E=C*b/w,f.repulsionForceX-=k,f.repulsionForceY-=E,p.repulsionForceX+=k,p.repulsionForceY+=E},h.prototype.calcGravitationalForce=function(f){var p,m,g,y,v,x,b,T;p=f.getOwner(),m=(p.getRight()+p.getLeft())/2,g=(p.getTop()+p.getBottom())/2,y=f.getCenterX()-m,v=f.getCenterY()-g,x=Math.abs(y)+f.getWidth()/2,b=Math.abs(v)+f.getHeight()/2,f.getOwner()==this.graphManager.getRoot()?(T=p.getEstimatedSize()*this.gravityRangeFactor,(x>T||b>T)&&(f.gravitationForceX=-this.gravityConstant*y,f.gravitationForceY=-this.gravityConstant*v)):(T=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>T||b>T)&&(f.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,f.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var f,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),f=this.totalDisplacement<this.totalDisplacementThreshold,this.oldTotalDisplacement=this.totalDisplacement,f||p},h.prototype.animate=function(){this.animationDuringLayout&&!this.isSubLayout&&(this.notAnimatedIterations==this.animationPeriod?(this.update(),this.notAnimatedIterations=0):this.notAnimatedIterations++)},h.prototype.calcNoOfChildrenForAllNodes=function(){for(var f,p=this.graphManager.getAllNodes(),m=0;m<p.length;m++)f=p[m],f.noOfChildren=f.getNoOfChildren()},h.prototype.calcGrid=function(f){var p=0,m=0;p=parseInt(Math.ceil((f.getRight()-f.getLeft())/this.repulsionRange)),m=parseInt(Math.ceil((f.getBottom()-f.getTop())/this.repulsionRange));for(var g=new Array(p),y=0;y<p;y++)g[y]=new Array(m);for(var y=0;y<p;y++)for(var v=0;v<m;v++)g[y][v]=new Array;return g},h.prototype.addNodeToGrid=function(f,p,m){var g=0,y=0,v=0,x=0;g=parseInt(Math.floor((f.getRect().x-p)/this.repulsionRange)),y=parseInt(Math.floor((f.getRect().width+f.getRect().x-p)/this.repulsionRange)),v=parseInt(Math.floor((f.getRect().y-m)/this.repulsionRange)),x=parseInt(Math.floor((f.getRect().height+f.getRect().y-m)/this.repulsionRange));for(var b=g;b<=y;b++)for(var T=v;T<=x;T++)this.grid[b][T].push(f),f.setGridCoordinates(g,y,v,x)},h.prototype.updateGrid=function(){var f,p,m=this.getAllNodes();for(this.grid=this.calcGrid(this.graphManager.getRoot()),f=0;f<m.length;f++)p=m[f],this.addNodeToGrid(p,this.graphManager.getRoot().getLeft(),this.graphManager.getRoot().getTop())},h.prototype.calculateRepulsionForceOfANode=function(f,p,m,g){if(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&m||g){var y=new Set;f.surrounding=new Array;for(var v,x=this.grid,b=f.startX-1;b<f.finishX+2;b++)for(var T=f.startY-1;T<f.finishY+2;T++)if(!(b<0||T<0||b>=x.length||T>=x[0].length)){for(var w=0;w<x[b][T].length;w++)if(v=x[b][T][w],!(f.getOwner()!=v.getOwner()||f==v)&&!p.has(v)&&!y.has(v)){var C=Math.abs(f.getCenterX()-v.getCenterX())-(f.getWidth()/2+v.getWidth()/2),k=Math.abs(f.getCenterY()-v.getCenterY())-(f.getHeight()/2+v.getHeight()/2);C<=this.repulsionRange&&k<=this.repulsionRange&&y.add(v)}}f.surrounding=[].concat(i(y))}for(b=0;b<f.surrounding.length;b++)this.calcRepulsionForce(f,f.surrounding[b])},h.prototype.calcRepulsionRange=function(){return 0},e.exports=h}),(function(e,t,r){"use strict";var i=r(1),n=r(7);function a(l,u,h){i.call(this,l,u,h),this.idealLength=n.DEFAULT_EDGE_LENGTH}o(a,"FDLayoutEdge"),a.prototype=Object.create(i.prototype);for(var s in i)a[s]=i[s];e.exports=a}),(function(e,t,r){"use strict";var i=r(3);function n(s,l,u,h){i.call(this,s,l,u,h),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0,this.startX=0,this.finishX=0,this.startY=0,this.finishY=0,this.surrounding=[]}o(n,"FDLayoutNode"),n.prototype=Object.create(i.prototype);for(var a in i)n[a]=i[a];n.prototype.setGridCoordinates=function(s,l,u,h){this.startX=s,this.finishX=l,this.startY=u,this.finishY=h},e.exports=n}),(function(e,t,r){"use strict";function i(n,a){this.width=0,this.height=0,n!==null&&a!==null&&(this.height=a,this.width=n)}o(i,"DimensionD"),i.prototype.getWidth=function(){return this.width},i.prototype.setWidth=function(n){this.width=n},i.prototype.getHeight=function(){return this.height},i.prototype.setHeight=function(n){this.height=n},e.exports=i}),(function(e,t,r){"use strict";var i=r(14);function n(){this.map={},this.keys=[]}o(n,"HashMap"),n.prototype.put=function(a,s){var l=i.createID(a);this.contains(l)||(this.map[l]=s,this.keys.push(a))},n.prototype.contains=function(a){var s=i.createID(a);return this.map[a]!=null},n.prototype.get=function(a){var s=i.createID(a);return this.map[s]},n.prototype.keySet=function(){return this.keys},e.exports=n}),(function(e,t,r){"use strict";var i=r(14);function n(){this.set={}}o(n,"HashSet"),n.prototype.add=function(a){var s=i.createID(a);this.contains(s)||(this.set[s]=a)},n.prototype.remove=function(a){delete this.set[i.createID(a)]},n.prototype.clear=function(){this.set={}},n.prototype.contains=function(a){return this.set[i.createID(a)]==a},n.prototype.isEmpty=function(){return this.size()===0},n.prototype.size=function(){return Object.keys(this.set).length},n.prototype.addAllTo=function(a){for(var s=Object.keys(this.set),l=s.length,u=0;u<l;u++)a.push(this.set[s[u]])},n.prototype.size=function(){return Object.keys(this.set).length},n.prototype.addAll=function(a){for(var s=a.length,l=0;l<s;l++){var u=a[l];this.add(u)}},e.exports=n}),(function(e,t,r){"use strict";var i=(function(){function l(u,h){for(var d=0;d<h.length;d++){var f=h[d];f.enumerable=f.enumerable||!1,f.configurable=!0,"value"in f&&(f.writable=!0),Object.defineProperty(u,f.key,f)}}return o(l,"defineProperties"),function(u,h,d){return h&&l(u.prototype,h),d&&l(u,d),u}})();function n(l,u){if(!(l instanceof u))throw new TypeError("Cannot call a class as a function")}o(n,"_classCallCheck");var a=r(11),s=(function(){function l(u,h){n(this,l),(h!==null||h!==void 0)&&(this.compareFunction=this._defaultCompareFunction);var d=void 0;u instanceof a?d=u.size():d=u.length,this._quicksort(u,0,d-1)}return o(l,"Quicksort"),i(l,[{key:"_quicksort",value:o(function(h,d,f){if(d<f){var p=this._partition(h,d,f);this._quicksort(h,d,p),this._quicksort(h,p+1,f)}},"_quicksort")},{key:"_partition",value:o(function(h,d,f){for(var p=this._get(h,d),m=d,g=f;;){for(;this.compareFunction(p,this._get(h,g));)g--;for(;this.compareFunction(this._get(h,m),p);)m++;if(m<g)this._swap(h,m,g),m++,g--;else return g}},"_partition")},{key:"_get",value:o(function(h,d){return h instanceof a?h.get_object_at(d):h[d]},"_get")},{key:"_set",value:o(function(h,d,f){h instanceof a?h.set_object_at(d,f):h[d]=f},"_set")},{key:"_swap",value:o(function(h,d,f){var p=this._get(h,d);this._set(h,d,this._get(h,f)),this._set(h,f,p)},"_swap")},{key:"_defaultCompareFunction",value:o(function(h,d){return d>h},"_defaultCompareFunction")}]),l})();e.exports=s}),(function(e,t,r){"use strict";var i=(function(){function s(l,u){for(var h=0;h<u.length;h++){var d=u[h];d.enumerable=d.enumerable||!1,d.configurable=!0,"value"in d&&(d.writable=!0),Object.defineProperty(l,d.key,d)}}return o(s,"defineProperties"),function(l,u,h){return u&&s(l.prototype,u),h&&s(l,h),l}})();function n(s,l){if(!(s instanceof l))throw new TypeError("Cannot call a class as a function")}o(n,"_classCallCheck");var a=(function(){function s(l,u){var h=arguments.length>2&&arguments[2]!==void 0?arguments[2]:1,d=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,f=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;n(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=d,this.gap_penalty=f,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p<this.iMax;p++){this.grid[p]=new Array(this.jMax);for(var m=0;m<this.jMax;m++)this.grid[p][m]=0}this.tracebackGrid=new Array(this.iMax);for(var g=0;g<this.iMax;g++){this.tracebackGrid[g]=new Array(this.jMax);for(var y=0;y<this.jMax;y++)this.tracebackGrid[g][y]=[null,null,null]}this.alignments=[],this.score=-1,this.computeGrids()}return o(s,"NeedlemanWunsch"),i(s,[{key:"getScore",value:o(function(){return this.score},"getScore")},{key:"getAlignments",value:o(function(){return this.alignments},"getAlignments")},{key:"computeGrids",value:o(function(){for(var u=1;u<this.jMax;u++)this.grid[0][u]=this.grid[0][u-1]+this.gap_penalty,this.tracebackGrid[0][u]=[!1,!1,!0];for(var h=1;h<this.iMax;h++)this.grid[h][0]=this.grid[h-1][0]+this.gap_penalty,this.tracebackGrid[h][0]=[!1,!0,!1];for(var d=1;d<this.iMax;d++)for(var f=1;f<this.jMax;f++){var p=void 0;this.sequence1[d-1]===this.sequence2[f-1]?p=this.grid[d-1][f-1]+this.match_score:p=this.grid[d-1][f-1]+this.mismatch_penalty;var m=this.grid[d-1][f]+this.gap_penalty,g=this.grid[d][f-1]+this.gap_penalty,y=[p,m,g],v=this.arrayAllMaxIndexes(y);this.grid[d][f]=y[v[0]],this.tracebackGrid[d][f]=[v.includes(0),v.includes(1),v.includes(2)]}this.score=this.grid[this.iMax-1][this.jMax-1]},"computeGrids")},{key:"alignmentTraceback",value:o(function(){var u=[];for(u.push({pos:[this.sequence1.length,this.sequence2.length],seq1:"",seq2:""});u[0];){var h=u[0],d=this.tracebackGrid[h.pos[0]][h.pos[1]];d[0]&&u.push({pos:[h.pos[0]-1,h.pos[1]-1],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),d[1]&&u.push({pos:[h.pos[0]-1,h.pos[1]],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:"-"+h.seq2}),d[2]&&u.push({pos:[h.pos[0],h.pos[1]-1],seq1:"-"+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),h.pos[0]===0&&h.pos[1]===0&&this.alignments.push({sequence1:h.seq1,sequence2:h.seq2}),u.shift()}return this.alignments},"alignmentTraceback")},{key:"getAllIndexes",value:o(function(u,h){for(var d=[],f=-1;(f=u.indexOf(h,f+1))!==-1;)d.push(f);return d},"getAllIndexes")},{key:"arrayAllMaxIndexes",value:o(function(u){return this.getAllIndexes(u,Math.max.apply(null,u))},"arrayAllMaxIndexes")}]),s})();e.exports=a}),(function(e,t,r){"use strict";var i=o(function(){},"layoutBase");i.FDLayout=r(18),i.FDLayoutConstants=r(7),i.FDLayoutEdge=r(19),i.FDLayoutNode=r(20),i.DimensionD=r(21),i.HashMap=r(22),i.HashSet=r(23),i.IGeometry=r(8),i.IMath=r(9),i.Integer=r(10),i.Point=r(12),i.PointD=r(4),i.RandomSeed=r(16),i.RectangleD=r(13),i.Transform=r(17),i.UniqueIDGeneretor=r(14),i.Quicksort=r(24),i.LinkedList=r(11),i.LGraphObject=r(2),i.LGraph=r(5),i.LEdge=r(1),i.LGraphManager=r(6),i.LNode=r(3),i.Layout=r(15),i.LayoutConstants=r(0),i.NeedlemanWunsch=r(25),e.exports=i}),(function(e,t,r){"use strict";function i(){this.listeners=[]}o(i,"Emitter");var n=i.prototype;n.addListener=function(a,s){this.listeners.push({event:a,callback:s})},n.removeListener=function(a,s){for(var l=this.listeners.length;l>=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},n.emit=function(a,s){for(var l=0;l<this.listeners.length;l++){var u=this.listeners[l];a===u.event&&u.callback(s)}},e.exports=i})])})});var $P=Js((m2,FP)=>{"use strict";o((function(t,r){typeof m2=="object"&&typeof FP=="object"?FP.exports=r(BP()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof m2=="object"?m2.coseBase=r(BP()):t.coseBase=r(t.layoutBase)}),"webpackUniversalModuleDefinition")(m2,function(e){return(function(t){var r={};function i(n){if(r[n])return r[n].exports;var a=r[n]={i:n,l:!1,exports:{}};return t[n].call(a.exports,a,a.exports,i),a.l=!0,a.exports}return o(i,"__webpack_require__"),i.m=t,i.c=r,i.i=function(n){return n},i.d=function(n,a,s){i.o(n,a)||Object.defineProperty(n,a,{configurable:!1,enumerable:!0,get:s})},i.n=function(n){var a=n&&n.__esModule?o(function(){return n.default},"getDefault"):o(function(){return n},"getModuleExports");return i.d(a,"a",a),a},i.o=function(n,a){return Object.prototype.hasOwnProperty.call(n,a)},i.p="",i(i.s=7)})([(function(t,r){t.exports=e}),(function(t,r,i){"use strict";var n=i(0).FDLayoutConstants;function a(){}o(a,"CoSEConstants");for(var s in n)a[s]=n[s];a.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,a.DEFAULT_RADIAL_SEPARATION=n.DEFAULT_EDGE_LENGTH,a.DEFAULT_COMPONENT_SEPERATION=60,a.TILE=!0,a.TILING_PADDING_VERTICAL=10,a.TILING_PADDING_HORIZONTAL=10,a.TREE_REDUCTION_ON_INCREMENTAL=!1,t.exports=a}),(function(t,r,i){"use strict";var n=i(0).FDLayoutEdge;function a(l,u,h){n.call(this,l,u,h)}o(a,"CoSEEdge"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];t.exports=a}),(function(t,r,i){"use strict";var n=i(0).LGraph;function a(l,u,h){n.call(this,l,u,h)}o(a,"CoSEGraph"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];t.exports=a}),(function(t,r,i){"use strict";var n=i(0).LGraphManager;function a(l){n.call(this,l)}o(a,"CoSEGraphManager"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];t.exports=a}),(function(t,r,i){"use strict";var n=i(0).FDLayoutNode,a=i(0).IMath;function s(u,h,d,f){n.call(this,u,h,d,f)}o(s,"CoSENode"),s.prototype=Object.create(n.prototype);for(var l in n)s[l]=n[l];s.prototype.move=function(){var u=this.graphManager.getLayout();this.displacementX=u.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY=u.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren,Math.abs(this.displacementX)>u.coolingFactor*u.maxNodeDisplacement&&(this.displacementX=u.coolingFactor*u.maxNodeDisplacement*a.sign(this.displacementX)),Math.abs(this.displacementY)>u.coolingFactor*u.maxNodeDisplacement&&(this.displacementY=u.coolingFactor*u.maxNodeDisplacement*a.sign(this.displacementY)),this.child==null?this.moveBy(this.displacementX,this.displacementY):this.child.getNodes().length==0?this.moveBy(this.displacementX,this.displacementY):this.propogateDisplacementToChildren(this.displacementX,this.displacementY),u.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},s.prototype.propogateDisplacementToChildren=function(u,h){for(var d=this.getChild().getNodes(),f,p=0;p<d.length;p++)f=d[p],f.getChild()==null?(f.moveBy(u,h),f.displacementX+=u,f.displacementY+=h):f.propogateDisplacementToChildren(u,h)},s.prototype.setPred1=function(u){this.pred1=u},s.prototype.getPred1=function(){return pred1},s.prototype.getPred2=function(){return pred2},s.prototype.setNext=function(u){this.next=u},s.prototype.getNext=function(){return next},s.prototype.setProcessed=function(u){this.processed=u},s.prototype.isProcessed=function(){return processed},t.exports=s}),(function(t,r,i){"use strict";var n=i(0).FDLayout,a=i(4),s=i(3),l=i(5),u=i(2),h=i(1),d=i(0).FDLayoutConstants,f=i(0).LayoutConstants,p=i(0).Point,m=i(0).PointD,g=i(0).Layout,y=i(0).Integer,v=i(0).IGeometry,x=i(0).LGraph,b=i(0).Transform;function T(){n.call(this),this.toBeTiled={}}o(T,"CoSELayout"),T.prototype=Object.create(n.prototype);for(var w in n)T[w]=n[w];T.prototype.newGraphManager=function(){var C=new a(this);return this.graphManager=C,C},T.prototype.newGraph=function(C){return new s(null,this.graphManager,C)},T.prototype.newNode=function(C){return new l(this.graphManager,C)},T.prototype.newEdge=function(C){return new u(null,null,C)},T.prototype.initParameters=function(){n.prototype.initParameters.call(this,arguments),this.isSubLayout||(h.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=h.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=h.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.springConstant=d.DEFAULT_SPRING_STRENGTH,this.repulsionConstant=d.DEFAULT_REPULSION_STRENGTH,this.gravityConstant=d.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=d.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=d.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=d.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1,this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/d.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=d.CONVERGENCE_CHECK_PERIOD/this.maxIterations,this.coolingAdjuster=1)},T.prototype.layout=function(){var C=f.DEFAULT_CREATE_BENDS_AS_NEEDED;return C&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},T.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(h.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),E=this.nodesWithGravity.filter(function(P){return k.has(P)});this.graphManager.setAllNodesToApplyGravitation(E)}}else{var C=this.getFlatForest();if(C.length>0)this.positionNodesRadially(C);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),E=this.nodesWithGravity.filter(function(A){return k.has(A)});this.graphManager.setAllNodesToApplyGravitation(E),this.positionNodesRandomly()}}return this.initSpringEmbedder(),this.runSpringEmbedder(),!0},T.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%d.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var C=new Set(this.getAllNodes()),k=this.nodesWithGravity.filter(function(N){return C.has(N)});this.graphManager.setAllNodesToApplyGravitation(k),this.graphManager.updateBounds(),this.updateGrid(),this.coolingFactor=d.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),this.coolingFactor=d.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var E=!this.isTreeGrowing&&!this.isGrowthFinished,A=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(E,A),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},T.prototype.getPositionsData=function(){for(var C=this.graphManager.getAllNodes(),k={},E=0;E<C.length;E++){var A=C[E].rect,N=C[E].id;k[N]={id:N,x:A.getCenterX(),y:A.getCenterY(),w:A.width,h:A.height}}return k},T.prototype.runSpringEmbedder=function(){this.initialAnimationPeriod=25,this.animationPeriod=this.initialAnimationPeriod;var C=!1;if(d.ANIMATE==="during")this.emit("layoutstarted");else{for(;!C;)C=this.tick();this.graphManager.updateBounds()}},T.prototype.calculateNodesToApplyGravitationTo=function(){var C=[],k,E=this.graphManager.getGraphs(),A=E.length,N;for(N=0;N<A;N++)k=E[N],k.updateConnected(),k.isConnected||(C=C.concat(k.getNodes()));return C},T.prototype.createBendpoints=function(){var C=[];C=C.concat(this.graphManager.getAllEdges());var k=new Set,E;for(E=0;E<C.length;E++){var A=C[E];if(!k.has(A)){var N=A.getSource(),P=A.getTarget();if(N==P)A.getBendpoints().push(new m),A.getBendpoints().push(new m),this.createDummyNodesForBendpoints(A),k.add(A);else{var I=[];if(I=I.concat(N.getEdgeListToNode(P)),I=I.concat(P.getEdgeListToNode(N)),!k.has(I[0])){if(I.length>1){var D;for(D=0;D<I.length;D++){var _=I[D];_.getBendpoints().push(new m),this.createDummyNodesForBendpoints(_)}}I.forEach(function(R){k.add(R)})}}}if(k.size==C.length)break}},T.prototype.positionNodesRadially=function(C){for(var k=new p(0,0),E=Math.ceil(Math.sqrt(C.length)),A=0,N=0,P=0,I=new m(0,0),D=0;D<C.length;D++){D%E==0&&(P=0,N=A,D!=0&&(N+=h.DEFAULT_COMPONENT_SEPERATION),A=0);var _=C[D],R=g.findCenterOfTree(_);k.x=P,k.y=N,I=T.radialLayout(_,R,k),I.y>A&&(A=Math.floor(I.y)),P=Math.floor(I.x+h.DEFAULT_COMPONENT_SEPERATION)}this.transform(new m(f.WORLD_CENTER_X-I.x/2,f.WORLD_CENTER_Y-I.y/2))},T.radialLayout=function(C,k,E){var A=Math.max(this.maxDiagonalInTree(C),h.DEFAULT_RADIAL_SEPARATION);T.branchRadialLayout(k,null,0,359,0,A);var N=x.calculateBounds(C),P=new b;P.setDeviceOrgX(N.getMinX()),P.setDeviceOrgY(N.getMinY()),P.setWorldOrgX(E.x),P.setWorldOrgY(E.y);for(var I=0;I<C.length;I++){var D=C[I];D.transform(P)}var _=new m(N.getMaxX(),N.getMaxY());return P.inverseTransformPoint(_)},T.branchRadialLayout=function(C,k,E,A,N,P){var I=(A-E+1)/2;I<0&&(I+=180);var D=(I+E)%360,_=D*v.TWO_PI/360,R=Math.cos(_),M=N*Math.cos(_),L=N*Math.sin(_);C.setCenter(M,L);var B=[];B=B.concat(C.getEdges());var O=B.length;k!=null&&O--;for(var $=0,G=B.length,F,V=C.getEdgesBetween(k);V.length>1;){var H=V[0];V.splice(0,1);var j=B.indexOf(H);j>=0&&B.splice(j,1),G--,O--}k!=null?F=(B.indexOf(V[0])+1)%G:F=0;for(var U=Math.abs(A-E)/O,Q=F;$!=O;Q=++Q%G){var Y=B[Q].getOtherEnd(C);if(Y!=k){var ae=(E+$*U)%360,J=(ae+U)%360;T.branchRadialLayout(Y,C,ae,J,N+P,P),$++}}},T.maxDiagonalInTree=function(C){for(var k=y.MIN_VALUE,E=0;E<C.length;E++){var A=C[E],N=A.getDiagonal();N>k&&(k=N)}return k},T.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},T.prototype.groupZeroDegreeMembers=function(){var C=this,k={};this.memberGroups={},this.idToDummyNode={};for(var E=[],A=this.graphManager.getAllNodes(),N=0;N<A.length;N++){var P=A[N],I=P.getParent();this.getNodeDegreeWithChildren(P)===0&&(I.id==null||!this.getToBeTiled(I))&&E.push(P)}for(var N=0;N<E.length;N++){var P=E[N],D=P.getParent().id;typeof k[D]>"u"&&(k[D]=[]),k[D]=k[D].concat(P)}Object.keys(k).forEach(function(_){if(k[_].length>1){var R="DummyCompound_"+_;C.memberGroups[R]=k[_];var M=k[_][0].getParent(),L=new l(C.graphManager);L.id=R,L.paddingLeft=M.paddingLeft||0,L.paddingRight=M.paddingRight||0,L.paddingBottom=M.paddingBottom||0,L.paddingTop=M.paddingTop||0,C.idToDummyNode[R]=L;var B=C.getGraphManager().add(C.newGraph(),L),O=M.getChild();O.add(L);for(var $=0;$<k[_].length;$++){var G=k[_][$];O.remove(G),B.add(G)}}})},T.prototype.clearCompounds=function(){var C={},k={};this.performDFSOnCompounds();for(var E=0;E<this.compoundOrder.length;E++)k[this.compoundOrder[E].id]=this.compoundOrder[E],C[this.compoundOrder[E].id]=[].concat(this.compoundOrder[E].getChild().getNodes()),this.graphManager.remove(this.compoundOrder[E].getChild()),this.compoundOrder[E].child=null;this.graphManager.resetAllNodes(),this.tileCompoundMembers(C,k)},T.prototype.clearZeroDegreeMembers=function(){var C=this,k=this.tiledZeroDegreePack=[];Object.keys(this.memberGroups).forEach(function(E){var A=C.idToDummyNode[E];k[E]=C.tileNodes(C.memberGroups[E],A.paddingLeft+A.paddingRight),A.rect.width=k[E].width,A.rect.height=k[E].height})},T.prototype.repopulateCompounds=function(){for(var C=this.compoundOrder.length-1;C>=0;C--){var k=this.compoundOrder[C],E=k.id,A=k.paddingLeft,N=k.paddingTop;this.adjustLocations(this.tiledMemberPack[E],k.rect.x,k.rect.y,A,N)}},T.prototype.repopulateZeroDegreeMembers=function(){var C=this,k=this.tiledZeroDegreePack;Object.keys(k).forEach(function(E){var A=C.idToDummyNode[E],N=A.paddingLeft,P=A.paddingTop;C.adjustLocations(k[E],A.rect.x,A.rect.y,N,P)})},T.prototype.getToBeTiled=function(C){var k=C.id;if(this.toBeTiled[k]!=null)return this.toBeTiled[k];var E=C.getChild();if(E==null)return this.toBeTiled[k]=!1,!1;for(var A=E.getNodes(),N=0;N<A.length;N++){var P=A[N];if(this.getNodeDegree(P)>0)return this.toBeTiled[k]=!1,!1;if(P.getChild()==null){this.toBeTiled[P.id]=!1;continue}if(!this.getToBeTiled(P))return this.toBeTiled[k]=!1,!1}return this.toBeTiled[k]=!0,!0},T.prototype.getNodeDegree=function(C){for(var k=C.id,E=C.getEdges(),A=0,N=0;N<E.length;N++){var P=E[N];P.getSource().id!==P.getTarget().id&&(A=A+1)}return A},T.prototype.getNodeDegreeWithChildren=function(C){var k=this.getNodeDegree(C);if(C.getChild()==null)return k;for(var E=C.getChild().getNodes(),A=0;A<E.length;A++){var N=E[A];k+=this.getNodeDegreeWithChildren(N)}return k},T.prototype.performDFSOnCompounds=function(){this.compoundOrder=[],this.fillCompexOrderByDFS(this.graphManager.getRoot().getNodes())},T.prototype.fillCompexOrderByDFS=function(C){for(var k=0;k<C.length;k++){var E=C[k];E.getChild()!=null&&this.fillCompexOrderByDFS(E.getChild().getNodes()),this.getToBeTiled(E)&&this.compoundOrder.push(E)}},T.prototype.adjustLocations=function(C,k,E,A,N){k+=A,E+=N;for(var P=k,I=0;I<C.rows.length;I++){var D=C.rows[I];k=P;for(var _=0,R=0;R<D.length;R++){var M=D[R];M.rect.x=k,M.rect.y=E,k+=M.rect.width+C.horizontalPadding,M.rect.height>_&&(_=M.rect.height)}E+=_+C.verticalPadding}},T.prototype.tileCompoundMembers=function(C,k){var E=this;this.tiledMemberPack=[],Object.keys(C).forEach(function(A){var N=k[A];E.tiledMemberPack[A]=E.tileNodes(C[A],N.paddingLeft+N.paddingRight),N.rect.width=E.tiledMemberPack[A].width,N.rect.height=E.tiledMemberPack[A].height})},T.prototype.tileNodes=function(C,k){var 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Va,X_,Pf,wo,l6t,K_,Am={},bC,hu,EU,TC;;){if(Pf=Qt[Qt.length-1],this.defaultActions[Pf]?wo=this.defaultActions[Pf]:((Va===null||typeof Va>"u")&&(Va=aNe()),wo=Zs[Pf]&&Zs[Pf][Va]),typeof wo>"u"||!wo.length||!wo[0]){var Z_="";TC=[];for(bC in Zs[Pf])this.terminals_[bC]&&bC>rNe&&TC.push("'"+this.terminals_[bC]+"'");Kn.showPosition?Z_="Parse error on line "+(an+1)+`:
+`+Kn.showPosition()+`
+Expecting `+TC.join(", ")+", got '"+(this.terminals_[Va]||Va)+"'":Z_="Parse error on line "+(an+1)+": Unexpected "+(Va==SU?"end of input":"'"+(this.terminals_[Va]||Va)+"'"),this.parseError(Z_,{text:Kn.match,token:this.terminals_[Va]||Va,line:Kn.yylineno,loc:j_,expected:TC})}if(wo[0]instanceof Array&&wo.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Pf+", token: "+Va);switch(wo[0]){case 1:Qt.push(Va),oi.push(Kn.yytext),Ce.push(Kn.yylloc),Qt.push(wo[1]),Va=null,X_?(Va=X_,X_=null):(H_=Kn.yyleng,_e=Kn.yytext,an=Kn.yylineno,j_=Kn.yylloc,xC>0&&xC--);break;case 2:if(hu=this.productions_[wo[1]][1],Am.$=oi[oi.length-hu],Am._$={first_line:Ce[Ce.length-(hu||1)].first_line,last_line:Ce[Ce.length-1].last_line,first_column:Ce[Ce.length-(hu||1)].first_column,last_column:Ce[Ce.length-1].last_column},nNe&&(Am._$.range=[Ce[Ce.length-(hu||1)].range[0],Ce[Ce.length-1].range[1]]),K_=this.performAction.apply(Am,[_e,H_,an,Of.yy,wo[1],oi,Ce].concat(iNe)),typeof K_<"u")return K_;hu&&(Qt=Qt.slice(0,-1*hu*2),oi=oi.slice(0,-1*hu),Ce=Ce.slice(0,-1*hu)),Qt.push(this.productions_[wo[1]][0]),oi.push(Am.$),Ce.push(Am._$),EU=Zs[Qt[Qt.length-2]][Qt[Qt.length-1]],Qt.push(EU);break;case 3:return!0}}return!0},"parse")},In=(function(){var zt={EOF:1,parseError:o(function(qt,Qt){if(this.yy.parser)this.yy.parser.parseError(qt,Qt);else throw new Error(qt)},"parseError"),setInput:o(function(Mt,qt){return this.yy=qt||this.yy||{},this._input=Mt,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var Mt=this._input[0];this.yytext+=Mt,this.yyleng++,this.offset++,this.match+=Mt,this.matched+=Mt;var qt=Mt.match(/(?:\r\n?|\n).*/g);return qt?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Mt},"input"),unput:o(function(Mt){var qt=Mt.length,Qt=Mt.split(/(?:\r\n?|\n)/g);this._input=Mt+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-qt),this.offset-=qt;var _t=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Qt.length-1&&(this.yylineno-=Qt.length-1);var oi=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Qt?(Qt.length===_t.length?this.yylloc.first_column:0)+_t[_t.length-Qt.length].length-Qt[0].length:this.yylloc.first_column-qt},this.options.ranges&&(this.yylloc.range=[oi[0],oi[0]+this.yyleng-qt]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(Mt){this.unput(this.match.slice(Mt))},"less"),pastInput:o(function(){var Mt=this.matched.substr(0,this.matched.length-this.match.length);return(Mt.length>20?"...":"")+Mt.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var Mt=this.match;return Mt.length<20&&(Mt+=this._input.substr(0,20-Mt.length)),(Mt.substr(0,20)+(Mt.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var Mt=this.pastInput(),qt=new Array(Mt.length+1).join("-");return Mt+this.upcomingInput()+`
+`+qt+"^"},"showPosition"),test_match:o(function(Mt,qt){var Qt,_t,oi;if(this.options.backtrack_lexer&&(oi={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(oi.yylloc.range=this.yylloc.range.slice(0))),_t=Mt[0].match(/(?:\r\n?|\n).*/g),_t&&(this.yylineno+=_t.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:_t?_t[_t.length-1].length-_t[_t.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+Mt[0].length},this.yytext+=Mt[0],this.match+=Mt[0],this.matches=Mt,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(Mt[0].length),this.matched+=Mt[0],Qt=this.performAction.call(this,this.yy,this,qt,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Qt)return Qt;if(this._backtrack){for(var Ce in oi)this[Ce]=oi[Ce];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var Mt,qt,Qt,_t;this._more||(this.yytext="",this.match="");for(var oi=this._currentRules(),Ce=0;Ce<oi.length;Ce++)if(Qt=this._input.match(this.rules[oi[Ce]]),Qt&&(!qt||Qt[0].length>qt[0].length)){if(qt=Qt,_t=Ce,this.options.backtrack_lexer){if(Mt=this.test_match(Qt,oi[Ce]),Mt!==!1)return Mt;if(this._backtrack){qt=!1;continue}else return!1}else if(!this.options.flex)break}return qt?(Mt=this.test_match(qt,oi[_t]),Mt!==!1?Mt:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var qt=this.next();return qt||this.lex()},"lex"),begin:o(function(qt){this.conditionStack.push(qt)},"begin"),popState:o(function(){var qt=this.conditionStack.length-1;return qt>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(qt){return qt=this.conditionStack.length-1-Math.abs(qt||0),qt>=0?this.conditionStack[qt]:"INITIAL"},"topState"),pushState:o(function(qt){this.begin(qt)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(qt,Qt,_t,oi){var Ce=oi;switch(_t){case 0:return this.begin("acc_title"),34;break;case 1:return this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),36;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return this.pushState("shapeData"),Qt.yytext="",40;break;case 8:return this.pushState("shapeDataStr"),40;break;case 9:return this.popState(),40;break;case 10:let Zs=/\n\s*/g;return Qt.yytext=Qt.yytext.replace(Zs,"<br/>"),40;break;case 11:return 40;case 12:this.popState();break;case 13:this.begin("callbackname");break;case 14:this.popState();break;case 15:this.popState(),this.begin("callbackargs");break;case 16:return 95;case 17:this.popState();break;case 18:return 96;case 19:return"MD_STR";case 20:this.popState();break;case 21:this.begin("md_string");break;case 22:return"STR";case 23:this.popState();break;case 24:this.pushState("string");break;case 25:return 84;case 26:return 102;case 27:return 85;case 28:return 104;case 29:return 86;case 30:return 87;case 31:return 97;case 32:this.begin("click");break;case 33:this.popState();break;case 34:return 88;case 35:return qt.lex.firstGraph()&&this.begin("dir"),12;break;case 36:return qt.lex.firstGraph()&&this.begin("dir"),12;break;case 37:return qt.lex.firstGraph()&&this.begin("dir"),12;break;case 38:return 27;case 39:return 32;case 40:return 98;case 41:return 98;case 42:return 98;case 43:return 98;case 44:return this.popState(),13;break;case 45:return this.popState(),14;break;case 46:return this.popState(),14;break;case 47:return this.popState(),14;break;case 48:return this.popState(),14;break;case 49:return this.popState(),14;break;case 50:return this.popState(),14;break;case 51:return this.popState(),14;break;case 52:return this.popState(),14;break;case 53:return this.popState(),14;break;case 54:return this.popState(),14;break;case 55:return 121;case 56:return 122;case 57:return 123;case 58:return 124;case 59:return 125;case 60:return 78;case 61:return 105;case 62:return 111;case 63:return 46;case 64:return 60;case 65:return 44;case 66:return 8;case 67:return 106;case 68:return 115;case 69:return this.popState(),77;break;case 70:return this.pushState("edgeText"),75;break;case 71:return 119;case 72:return this.popState(),77;break;case 73:return this.pushState("thickEdgeText"),75;break;case 74:return 119;case 75:return this.popState(),77;break;case 76:return this.pushState("dottedEdgeText"),75;break;case 77:return 119;case 78:return 77;case 79:return this.popState(),53;break;case 80:return"TEXT";case 81:return this.pushState("ellipseText"),52;break;case 82:return this.popState(),55;break;case 83:return this.pushState("text"),54;break;case 84:return this.popState(),57;break;case 85:return this.pushState("text"),56;break;case 86:return 58;case 87:return this.pushState("text"),67;break;case 88:return this.popState(),64;break;case 89:return this.pushState("text"),63;break;case 90:return this.popState(),49;break;case 91:return this.pushState("text"),48;break;case 92:return this.popState(),69;break;case 93:return this.popState(),71;break;case 94:return 117;case 95:return this.pushState("trapText"),68;break;case 96:return this.pushState("trapText"),70;break;case 97:return 118;case 98:return 67;case 99:return 90;case 100:return"SEP";case 101:return 89;case 102:return 115;case 103:return 111;case 104:return 44;case 105:return 109;case 106:return 114;case 107:return 116;case 108:return this.popState(),62;break;case 109:return this.pushState("text"),62;break;case 110:return this.popState(),51;break;case 111:return this.pushState("text"),50;break;case 112:return this.popState(),31;break;case 113:return this.pushState("text"),29;break;case 114:return this.popState(),66;break;case 115:return this.pushState("text"),65;break;case 116:return"TEXT";case 117:return"QUOTE";case 118:return 9;case 119:return 10;case 120:return 11}},"anonymous"),rules:[/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:@\{)/,/^(?:["])/,/^(?:["])/,/^(?:[^\"]+)/,/^(?:[^}^"]+)/,/^(?:\})/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["][`])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:["])/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s])/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:flowchart-elk\b)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:.*direction\s+TD[^\n]*)/,/^(?:[^\s\"]+@(?=[^\{\"]))/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:[^-]|-(?!-)+)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:[^=]|=(?!))/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:[^\.]|\.(?!))/,/^(?:\s*~~[\~]+\s*)/,/^(?:[-/\)][\)])/,/^(?:[^\(\)\[\]\{\}]|!\)+)/,/^(?:\(-)/,/^(?:\]\))/,/^(?:\(\[)/,/^(?:\]\])/,/^(?:\[\[)/,/^(?:\[\|)/,/^(?:>)/,/^(?:\)\])/,/^(?:\[\()/,/^(?:\)\)\))/,/^(?:\(\(\()/,/^(?:[\\(?=\])][\]])/,/^(?:\/(?=\])\])/,/^(?:\/(?!\])|\\(?!\])|[^\\\[\]\(\)\{\}\/]+)/,/^(?:\[\/)/,/^(?:\[\\)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:\*)/,/^(?:#)/,/^(?:&)/,/^(?:([A-Za-z0-9!"\#$%&'*+\.`?\\_\/]|-(?=[^\>\-\.])|(?!))+)/,/^(?:-)/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2D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zt})();Xi.lexer=In;function pi(){this.yy={}}return o(pi,"Parser"),pi.prototype=Xi,Xi.Parser=pi,new pi})();VP.parser=VP;WP=VP});var Kge,Zge,Qge=z(()=>{"use strict";Xge();Kge=Object.assign({},WP);Kge.parse=e=>{let t=e.replace(/}\s*\n/g,`}
+`);return WP.parse(t)};Zge=Kge});var Vc,k1=z(()=>{"use strict";Vc=o(()=>`
+  /* Font Awesome icon styling - consolidated */
+  .label-icon {
+    display: inline-block;
+    height: 1em;
+    overflow: visible;
+    vertical-align: -0.125em;
+  }
+  
+  .node .label-icon path {
+    fill: currentColor;
+    stroke: revert;
+    stroke-width: revert;
+  }
+`,"getIconStyles")});var Kot,Zot,Jge,e1e=z(()=>{"use strict";Cn();k1();Kot=o((e,t)=>{let r=Ff,i=r(e,"r"),n=r(e,"g"),a=r(e,"b");return mn(i,n,a,t)},"fade"),Zot=o(e=>`.label {
+    font-family: ${e.fontFamily};
+    color: ${e.nodeTextColor||e.textColor};
+  }
+  .cluster-label text {
+    fill: ${e.titleColor};
+  }
+  .cluster-label span {
+    color: ${e.titleColor};
+  }
+  .cluster-label span p {
+    background-color: transparent;
+  }
+
+  .label text,span {
+    fill: ${e.nodeTextColor||e.textColor};
+    color: ${e.nodeTextColor||e.textColor};
+  }
+
+  .node rect,
+  .node circle,
+  .node ellipse,
+  .node polygon,
+  .node path {
+    fill: ${e.mainBkg};
+    stroke: ${e.nodeBorder};
+    stroke-width: ${e.strokeWidth??1}px;
+  }
+  .rough-node .label text , .node .label text, .image-shape .label, .icon-shape .label {
+    text-anchor: middle;
+  }
+  // .flowchart-label .text-outer-tspan {
+  //   text-anchor: middle;
+  // }
+  // .flowchart-label .text-inner-tspan {
+  //   text-anchor: start;
+  // }
+
+  .node .katex path {
+    fill: #000;
+    stroke: #000;
+    stroke-width: 1px;
+  }
+
+  .rough-node .label,.node .label, .image-shape .label, .icon-shape .label {
+    text-align: center;
+  }
+  .node.clickable {
+    cursor: pointer;
+  }
+
+
+  .root .anchor path {
+    fill: ${e.lineColor} !important;
+    stroke-width: 0;
+    stroke: ${e.lineColor};
+  }
+
+  .arrowheadPath {
+    fill: ${e.arrowheadColor};
+  }
+
+  .edgePath .path {
+    stroke: ${e.lineColor};
+    stroke-width: ${e.strokeWidth??2}px;
+  }
+
+  .flowchart-link {
+    stroke: ${e.lineColor};
+    fill: none;
+  }
+
+  .edgeLabel {
+    background-color: ${e.edgeLabelBackground};
+    p {
+      background-color: ${e.edgeLabelBackground};
+    }
+    rect {
+      opacity: 0.5;
+      background-color: ${e.edgeLabelBackground};
+      fill: ${e.edgeLabelBackground};
+    }
+    text-align: center;
+  }
+
+  /* For html labels only */
+  .labelBkg {
+    background-color: ${Kot(e.edgeLabelBackground,.5)};
+    // background-color:
+  }
+
+  .cluster rect {
+    fill: ${e.clusterBkg};
+    stroke: ${e.clusterBorder};
+    stroke-width: 1px;
+  }
+
+  .cluster text {
+    fill: ${e.titleColor};
+  }
+
+  .cluster span {
+    color: ${e.titleColor};
+  }
+  /* .cluster div {
+    color: ${e.titleColor};
+  } */
+
+  div.mermaidTooltip {
+    position: absolute;
+    text-align: center;
+    max-width: 200px;
+    padding: 2px;
+    font-family: ${e.fontFamily};
+    font-size: 12px;
+    background: ${e.tertiaryColor};
+    border: 1px solid ${e.border2};
+    border-radius: 2px;
+    pointer-events: none;
+    z-index: 100;
+  }
+
+  .flowchartTitleText {
+    text-anchor: middle;
+    font-size: 18px;
+    fill: ${e.textColor};
+  }
+
+  rect.text {
+    fill: none;
+    stroke-width: 0;
+  }
+
+  .icon-shape, .image-shape {
+    background-color: ${e.edgeLabelBackground};
+    p {
+      background-color: ${e.edgeLabelBackground};
+      padding: 2px;
+    }
+    .label rect {
+      opacity: 0.5;
+      background-color: ${e.edgeLabelBackground};
+      fill: ${e.edgeLabelBackground};
+    }
+    text-align: center;
+  }
+  ${Vc()}
+`,"getStyles"),Jge=Zot});var hE={};xr(hE,{diagram:()=>Qot});var Qot,dE=z(()=>{"use strict";Ut();Mae();jge();Qge();e1e();Qot={parser:Zge,get db(){return new V4},renderer:Yge,styles:Jge,init:o(e=>{e.flowchart||(e.flowchart={}),e.layout&&Sv({layout:e.layout}),e.flowchart.arrowMarkerAbsolute=e.arrowMarkerAbsolute,Sv({flowchart:{arrowMarkerAbsolute:e.arrowMarkerAbsolute}})},"init")}});var qP,a1e,s1e=z(()=>{"use strict";qP=(function(){var e=o(function(De,Ie,Ee,we){for(Ee=Ee||{},we=De.length;we--;Ee[De[we]]=Ie);return 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15:Me.addEntity(de[ke-4]),Me.setClass([de[ke-4]],de[ke-2]);break;case 16:Me.addEntity(de[ke]);break;case 17:Me.addEntity(de[ke-2]),Me.setClass([de[ke-2]],de[ke]);break;case 18:Me.addEntity(de[ke-6],de[ke-4]),Me.addAttributes(de[ke-6],de[ke-1]);break;case 19:Me.addEntity(de[ke-8],de[ke-6]),Me.addAttributes(de[ke-8],de[ke-1]),Me.setClass([de[ke-8]],de[ke-3]);break;case 20:Me.addEntity(de[ke-5],de[ke-3]);break;case 21:Me.addEntity(de[ke-7],de[ke-5]),Me.setClass([de[ke-7]],de[ke-2]);break;case 22:Me.addEntity(de[ke-3],de[ke-1]);break;case 23:Me.addEntity(de[ke-5],de[ke-3]),Me.setClass([de[ke-5]],de[ke]);break;case 24:case 25:this.$=de[ke].trim(),Me.setAccTitle(this.$);break;case 26:case 27:this.$=de[ke].trim(),Me.setAccDescription(this.$);break;case 32:Me.setDirection("TB");break;case 33:Me.setDirection("BT");break;case 34:Me.setDirection("RL");break;case 35:Me.setDirection("LR");break;case 36:this.$=de[ke-3],Me.addClass(de[ke-2],de[ke-1]);break;case 37:case 38:case 59:case 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72:this.$=Me.Cardinality.ZERO_OR_ONE;break;case 73:this.$=Me.Cardinality.ZERO_OR_MORE;break;case 74:this.$=Me.Cardinality.ONE_OR_MORE;break;case 75:this.$=Me.Cardinality.ONLY_ONE;break;case 76:this.$=Me.Cardinality.MD_PARENT;break;case 77:this.$=Me.Identification.NON_IDENTIFYING;break;case 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we=new Error(Ie);throw we.hash=Ee,we}},"parseError"),parse:o(function(Ie){var Ee=this,we=[0],Me=[],$e=[null],de=[],rt=this.table,ke="",Fe=0,He=0,at=0,qe=2,Ue=1,ye=de.slice.call(arguments,1),ve=Object.create(this.lexer),ie={yy:{}};for(var fe in this.yy)Object.prototype.hasOwnProperty.call(this.yy,fe)&&(ie.yy[fe]=this.yy[fe]);ve.setInput(Ie,ie.yy),ie.yy.lexer=ve,ie.yy.parser=this,typeof ve.yylloc>"u"&&(ve.yylloc={});var W=ve.yylloc;de.push(W);var ce=ve.options&&ve.options.ranges;typeof ie.yy.parseError=="function"?this.parseError=ie.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function K(kt){we.length=we.length-2*kt,$e.length=$e.length-kt,de.length=de.length-kt}o(K,"popStack");function Re(){var kt;return kt=Me.pop()||ve.lex()||Ue,typeof kt!="number"&&(kt instanceof Array&&(Me=kt,kt=Me.pop()),kt=Ee.symbols_[kt]||kt),kt}o(Re,"lex");for(var xe,Oe,be,Be,Ae,Ve,Pe={},Ye,le,st,me;;){if(be=we[we.length-1],this.defaultActions[be]?Be=this.defaultActions[be]:((xe===null||typeof xe>"u")&&(xe=Re()),Be=rt[be]&&rt[be][xe]),typeof Be>"u"||!Be.length||!Be[0]){var ot="";me=[];for(Ye in rt[be])this.terminals_[Ye]&&Ye>qe&&me.push("'"+this.terminals_[Ye]+"'");ve.showPosition?ot="Parse error on line "+(Fe+1)+`:
+`+ve.showPosition()+`
+Expecting `+me.join(", ")+", got '"+(this.terminals_[xe]||xe)+"'":ot="Parse error on line "+(Fe+1)+": Unexpected "+(xe==Ue?"end of input":"'"+(this.terminals_[xe]||xe)+"'"),this.parseError(ot,{text:ve.match,token:this.terminals_[xe]||xe,line:ve.yylineno,loc:W,expected:me})}if(Be[0]instanceof Array&&Be.length>1)throw new Error("Parse Error: multiple actions possible at state: "+be+", token: "+xe);switch(Be[0]){case 1:we.push(xe),$e.push(ve.yytext),de.push(ve.yylloc),we.push(Be[1]),xe=null,Oe?(xe=Oe,Oe=null):(He=ve.yyleng,ke=ve.yytext,Fe=ve.yylineno,W=ve.yylloc,at>0&&at--);break;case 2:if(le=this.productions_[Be[1]][1],Pe.$=$e[$e.length-le],Pe._$={first_line:de[de.length-(le||1)].first_line,last_line:de[de.length-1].last_line,first_column:de[de.length-(le||1)].first_column,last_column:de[de.length-1].last_column},ce&&(Pe._$.range=[de[de.length-(le||1)].range[0],de[de.length-1].range[1]]),Ve=this.performAction.apply(Pe,[ke,He,Fe,ie.yy,Be[1],$e,de].concat(ye)),typeof Ve<"u")return Ve;le&&(we=we.slice(0,-1*le*2),$e=$e.slice(0,-1*le),de=de.slice(0,-1*le)),we.push(this.productions_[Be[1]][0]),$e.push(Pe.$),de.push(Pe._$),st=rt[we[we.length-2]][we[we.length-1]],we.push(st);break;case 3:return!0}}return!0},"parse")},Te=(function(){var De={EOF:1,parseError:o(function(Ee,we){if(this.yy.parser)this.yy.parser.parseError(Ee,we);else throw new Error(Ee)},"parseError"),setInput:o(function(Ie,Ee){return this.yy=Ee||this.yy||{},this._input=Ie,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var Ie=this._input[0];this.yytext+=Ie,this.yyleng++,this.offset++,this.match+=Ie,this.matched+=Ie;var Ee=Ie.match(/(?:\r\n?|\n).*/g);return Ee?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Ie},"input"),unput:o(function(Ie){var Ee=Ie.length,we=Ie.split(/(?:\r\n?|\n)/g);this._input=Ie+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-Ee),this.offset-=Ee;var Me=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),we.length-1&&(this.yylineno-=we.length-1);var $e=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:we?(we.length===Me.length?this.yylloc.first_column:0)+Me[Me.length-we.length].length-we[0].length:this.yylloc.first_column-Ee},this.options.ranges&&(this.yylloc.range=[$e[0],$e[0]+this.yyleng-Ee]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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+
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+  .labelBkg {
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+  .edgeLabel .label {
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+  }
+
+  .label {
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+    color: ${e.nodeTextColor||e.textColor};
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+
+  .edge-pattern-dashed {
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+  }
+
+  .node rect,
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+  {
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+  .relationshipLine {
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+
+  .marker {
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+	Complement Sets cannot be automatically optimized.
+	This will disable the lexer's first char optimizations.
+	See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#COMPLEMENT for details.`);else{let i="";t&&(i=`
+	This will disable the lexer's first char optimizations.
+	See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#REGEXP_PARSING for details.`),w5(`${S5}
+	Failed parsing: < ${e.toString()} >
+	Using the @chevrotain/regexp-to-ast library
+	Please open an issue at: https://github.com/chevrotain/chevrotain/issues`+i)}}return[]}function E5(e,t,r){switch(e.type){case"Disjunction":for(let n=0;n<e.value.length;n++)E5(e.value[n],t,r);break;case"Alternative":let i=e.value;for(let n=0;n<i.length;n++){let a=i[n];switch(a.type){case"EndAnchor":case"GroupBackReference":case"Lookahead":case"NegativeLookahead":case"Lookbehind":case"NegativeLookbehind":case"StartAnchor":case"WordBoundary":case"NonWordBoundary":continue}let s=a;switch(s.type){case"Character":O2(s.value,t,r);break;case"Set":if(s.complement===!0)throw Error(oCe);vr(s.value,u=>{if(typeof u=="number")O2(u,t,r);else{let h=u;if(r===!0)for(let d=h.from;d<=h.to;d++)O2(d,t,r);else{for(let d=h.from;d<=h.to&&d<B2;d++)O2(d,t,r);if(h.to>=B2){let d=h.from>=B2?h.from:B2,f=h.to,p=fh(d),m=fh(f);for(let g=p;g<=m;g++)t[g]=g}}}});break;case"Group":E5(s.value,t,r);break;default:throw Error("Non Exhaustive Match")}let l=s.quantifier!==void 0&&s.quantifier.atLeast===0;if(s.type==="Group"&&A5(s)===!1||s.type!=="Group"&&l===!1)break}break;default:throw Error("non exhaustive match!")}return Hn(t)}function O2(e,t,r){let i=fh(e);t[i]=i,r===!0&&cCe(e,t)}function cCe(e,t){let r=String.fromCharCode(e),i=r.toUpperCase();if(i!==r){let n=fh(i.charCodeAt(0));t[n]=n}else{let n=r.toLowerCase();if(n!==r){let a=fh(n.charCodeAt(0));t[a]=a}}}function TF(e,t){return ty(e.value,r=>{if(typeof r=="number")return fs(t,r);{let i=r;return ty(t,n=>i.from<=n&&n<=i.to)!==void 0}})}function A5(e){let t=e.quantifier;return t&&t.atLeast===0?!0:e.value?Kr(e.value)?Hl(e.value,A5):A5(e.value):!1}function kA(e,t){if(t instanceof RegExp){let r=OT(t),i=new T0t(e);return i.visit(r),i.found}else return ty(t,r=>fs(e,r.charCodeAt(0)))!==void 0}function hCe(e,t){t=Rz(t,{useSticky:uCe,debug:!1,safeMode:!1,positionTracking:"full",lineTerminatorCharacters:["\r",`
+`],tracer:S((b,T)=>T(),"tracer")});let r=t.tracer;r("initCharCodeToOptimizedIndexMap",()=>{MCe()});let i;r("Reject Lexer.NA",()=>{i=TA(e,b=>b[G0]===ls.NA)});let n=!1,a;r("Transform Patterns",()=>{n=!1,a=nr(i,b=>{let T=b[G0];if(hh(T)){let w=T.source;return w.length===1&&w!=="^"&&w!=="$"&&w!=="."&&!T.ignoreCase?w:w.length===2&&w[0]==="\\"&&!fs(["d","D","s","S","t","r","n","t","0","c","b","B","f","v","w","W"],w[1])?w[1]:t.useSticky?kF(T):CF(T)}else{if(vh(T))return n=!0,{exec:T};if(typeof T=="object")return n=!0,T;if(typeof T=="string"){if(T.length===1)return T;{let w=T.replace(/[\\^$.*+?()[\]{}|]/g,"\\$&"),C=new RegExp(w);return t.useSticky?kF(C):CF(C)}}else throw Error("non exhaustive match")}})});let s,l,u,h,d;r("misc mapping",()=>{s=nr(i,b=>b.tokenTypeIdx),l=nr(i,b=>{let T=b.GROUP;if(T!==ls.SKIPPED){if(Vs(T))return T;if(dh(T))return!1;throw Error("non exhaustive match")}}),u=nr(i,b=>{let T=b.LONGER_ALT;if(T)return Kr(T)?nr(T,C=>K1e(i,C)):[K1e(i,T)]}),h=nr(i,b=>b.PUSH_MODE),d=nr(i,b=>mr(b,"POP_MODE"))});let f;r("Line Terminator Handling",()=>{let b=Bz(t.lineTerminatorCharacters);f=nr(i,T=>!1),t.positionTracking!=="onlyOffset"&&(f=nr(i,T=>mr(T,"LINE_BREAKS")?!!T.LINE_BREAKS:Pz(T,b)===!1&&kA(b,T.PATTERN)))});let p,m,g,y;r("Misc Mapping #2",()=>{p=nr(i,Oz),m=nr(a,RCe),g=go(i,(b,T)=>{let w=T.GROUP;return Vs(w)&&w!==ls.SKIPPED&&(b[w]=[]),b},{}),y=nr(a,(b,T)=>({pattern:a[T],longerAlt:u[T],canLineTerminator:f[T],isCustom:p[T],short:m[T],group:l[T],push:h[T],pop:d[T],tokenTypeIdx:s[T],tokenType:i[T]}))});let v=!0,x=[];return t.safeMode||r("First Char Optimization",()=>{x=go(i,(b,T,w)=>{if(typeof T.PATTERN=="string"){let C=T.PATTERN.charCodeAt(0),k=fh(C);qE(b,k,y[w])}else if(Kr(T.START_CHARS_HINT)){let C;vr(T.START_CHARS_HINT,k=>{let E=typeof k=="string"?k.charCodeAt(0):k,A=fh(E);C!==A&&(C=A,qE(b,A,y[w]))})}else if(hh(T.PATTERN))if(T.PATTERN.unicode)v=!1,t.ensureOptimizations&&w5(`${S5}	Unable to analyze < ${T.PATTERN.toString()} > pattern.
+	The regexp unicode flag is not currently supported by the regexp-to-ast library.
+	This will disable the lexer's first char optimizations.
+	For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#UNICODE_OPTIMIZE`);else{let C=lCe(T.PATTERN,t.ensureOptimizations);Oi(C)&&(v=!1),vr(C,k=>{qE(b,k,y[w])})}else t.ensureOptimizations&&w5(`${S5}	TokenType: <${T.name}> is using a custom token pattern without providing <start_chars_hint> parameter.
+	This will disable the lexer's first char optimizations.
+	For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_OPTIMIZE`),v=!1;return b},[])}),{emptyGroups:g,patternIdxToConfig:y,charCodeToPatternIdxToConfig:x,hasCustom:n,canBeOptimized:v}}function dCe(e,t){let r=[],i=pCe(e);r=r.concat(i.errors);let n=mCe(i.valid),a=n.valid;return r=r.concat(n.errors),r=r.concat(fCe(a)),r=r.concat(TCe(a)),r=r.concat(CCe(a,t)),r=r.concat(kCe(a)),r}function fCe(e){let t=[],r=il(e,i=>hh(i[G0]));return t=t.concat(gCe(r)),t=t.concat(vCe(r)),t=t.concat(xCe(r)),t=t.concat(bCe(r)),t=t.concat(yCe(r)),t}function pCe(e){let t=il(e,n=>!mr(n,G0)),r=nr(t,n=>({message:"Token Type: ->"+n.name+"<- missing static 'PATTERN' property",type:dn.MISSING_PATTERN,tokenTypes:[n]})),i=bA(e,t);return{errors:r,valid:i}}function mCe(e){let t=il(e,n=>{let a=n[G0];return!hh(a)&&!vh(a)&&!mr(a,"exec")&&!Vs(a)}),r=nr(t,n=>({message:"Token Type: ->"+n.name+"<- static 'PATTERN' can only be a RegExp, a Function matching the {CustomPatternMatcherFunc} type or an Object matching the {ICustomPattern} interface.",type:dn.INVALID_PATTERN,tokenTypes:[n]})),i=bA(e,t);return{errors:r,valid:i}}function gCe(e){class t extends tA{static{o(this,"EndAnchorFinder")}static{S(this,"EndAnchorFinder")}constructor(){super(...arguments),this.found=!1}visitEndAnchor(a){this.found=!0}}let r=il(e,n=>{let a=n.PATTERN;try{let s=OT(a),l=new t;return l.visit(s),l.found}catch{return C0t.test(a.source)}});return nr(r,n=>({message:`Unexpected RegExp Anchor Error:
+	Token Type: ->`+n.name+`<- static 'PATTERN' cannot contain end of input anchor '$'
+	See chevrotain.io/docs/guide/resolving_lexer_errors.html#ANCHORS	for details.`,type:dn.EOI_ANCHOR_FOUND,tokenTypes:[n]}))}function yCe(e){let t=il(e,i=>i.PATTERN.test(""));return nr(t,i=>({message:"Token Type: ->"+i.name+"<- static 'PATTERN' must not match an empty string",type:dn.EMPTY_MATCH_PATTERN,tokenTypes:[i]}))}function vCe(e){class t extends tA{static{o(this,"StartAnchorFinder")}static{S(this,"StartAnchorFinder")}constructor(){super(...arguments),this.found=!1}visitStartAnchor(a){this.found=!0}}let r=il(e,n=>{let a=n.PATTERN;try{let s=OT(a),l=new t;return l.visit(s),l.found}catch{return k0t.test(a.source)}});return nr(r,n=>({message:`Unexpected RegExp Anchor Error:
+	Token Type: ->`+n.name+`<- static 'PATTERN' cannot contain start of input anchor '^'
+	See https://chevrotain.io/docs/guide/resolving_lexer_errors.html#ANCHORS	for details.`,type:dn.SOI_ANCHOR_FOUND,tokenTypes:[n]}))}function xCe(e){let t=il(e,i=>{let n=i[G0];return n instanceof RegExp&&(n.multiline||n.global)});return nr(t,i=>({message:"Token Type: ->"+i.name+"<- static 'PATTERN' may NOT contain global('g') or multiline('m')",type:dn.UNSUPPORTED_FLAGS_FOUND,tokenTypes:[i]}))}function bCe(e){let t=[],r=nr(e,a=>go(e,(s,l)=>(a.PATTERN.source===l.PATTERN.source&&!fs(t,l)&&l.PATTERN!==ls.NA&&(t.push(l),s.push(l)),s),[]));r=IT(r);let i=il(r,a=>a.length>1);return nr(i,a=>{let s=nr(a,u=>u.name);return{message:`The same RegExp pattern ->${jl(a).PATTERN}<-has been used in all of the following Token Types: ${s.join(", ")} <-`,type:dn.DUPLICATE_PATTERNS_FOUND,tokenTypes:a}})}function TCe(e){let t=il(e,i=>{if(!mr(i,"GROUP"))return!1;let n=i.GROUP;return n!==ls.SKIPPED&&n!==ls.NA&&!Vs(n)});return nr(t,i=>({message:"Token Type: ->"+i.name+"<- static 'GROUP' can only be Lexer.SKIPPED/Lexer.NA/A String",type:dn.INVALID_GROUP_TYPE_FOUND,tokenTypes:[i]}))}function CCe(e,t){let r=il(e,n=>n.PUSH_MODE!==void 0&&!fs(t,n.PUSH_MODE));return nr(r,n=>({message:`Token Type: ->${n.name}<- static 'PUSH_MODE' value cannot refer to a Lexer Mode ->${n.PUSH_MODE}<-which does not exist`,type:dn.PUSH_MODE_DOES_NOT_EXIST,tokenTypes:[n]}))}function kCe(e){let t=[],r=go(e,(i,n,a)=>{let s=n.PATTERN;return s===ls.NA||(Vs(s)?i.push({str:s,idx:a,tokenType:n}):hh(s)&&SCe(s)&&i.push({str:s.source,idx:a,tokenType:n})),i},[]);return vr(e,(i,n)=>{vr(r,({str:a,idx:s,tokenType:l})=>{if(n<s&&wCe(a,i.PATTERN)){let u=`Token: ->${l.name}<- can never be matched.
+Because it appears AFTER the Token Type ->${i.name}<-in the lexer's definition.
+See https://chevrotain.io/docs/guide/resolving_lexer_errors.html#UNREACHABLE`;t.push({message:u,type:dn.UNREACHABLE_PATTERN,tokenTypes:[i,l]})}})}),t}function wCe(e,t){if(hh(t)){if(ECe(t))return!1;let r=t.exec(e);return r!==null&&r.index===0}else{if(vh(t))return t(e,0,[],{});if(mr(t,"exec"))return t.exec(e,0,[],{});if(typeof t=="string")return t===e;throw Error("non exhaustive match")}}function SCe(e){return ty([".","\\","[","]","|","^","$","(",")","?","*","+","{"],r=>e.source.indexOf(r)!==-1)===void 0}function ECe(e){return/(\(\?=)|(\(\?!)|(\(\?<=)|(\(\?<!)/.test(e.source)}function CF(e){let t=e.ignoreCase?"i":"";return new RegExp(`^(?:${e.source})`,t)}function kF(e){let t=e.ignoreCase?"iy":"y";return new RegExp(`${e.source}`,t)}function ACe(e,t,r){let i=[];return mr(e,P2)||i.push({message:"A MultiMode Lexer cannot be initialized without a <"+P2+`> property in its definition
+`,type:dn.MULTI_MODE_LEXER_WITHOUT_DEFAULT_MODE}),mr(e,yE)||i.push({message:"A MultiMode Lexer cannot be initialized without a <"+yE+`> property in its definition
+`,type:dn.MULTI_MODE_LEXER_WITHOUT_MODES_PROPERTY}),mr(e,yE)&&mr(e,P2)&&!mr(e.modes,e.defaultMode)&&i.push({message:`A MultiMode Lexer cannot be initialized with a ${P2}: <${e.defaultMode}>which does not exist
+`,type:dn.MULTI_MODE_LEXER_DEFAULT_MODE_VALUE_DOES_NOT_EXIST}),mr(e,yE)&&vr(e.modes,(n,a)=>{vr(n,(s,l)=>{if(dh(s))i.push({message:`A Lexer cannot be initialized using an undefined Token Type. Mode:<${a}> at index: <${l}>
+`,type:dn.LEXER_DEFINITION_CANNOT_CONTAIN_UNDEFINED});else if(mr(s,"LONGER_ALT")){let u=Kr(s.LONGER_ALT)?s.LONGER_ALT:[s.LONGER_ALT];vr(u,h=>{!dh(h)&&!fs(n,h)&&i.push({message:`A MultiMode Lexer cannot be initialized with a longer_alt <${h.name}> on token <${s.name}> outside of mode <${a}>
+`,type:dn.MULTI_MODE_LEXER_LONGER_ALT_NOT_IN_CURRENT_MODE})})}})}),i}function _Ce(e,t,r){let i=[],n=!1,a=IT(Ul(Hn(e.modes))),s=TA(a,u=>u[G0]===ls.NA),l=Bz(r);return t&&vr(s,u=>{let h=Pz(u,l);if(h!==!1){let f={message:LCe(u,h),type:h.issue,tokenType:u};i.push(f)}else mr(u,"LINE_BREAKS")?u.LINE_BREAKS===!0&&(n=!0):kA(l,u.PATTERN)&&(n=!0)}),t&&!n&&i.push({message:`Warning: No LINE_BREAKS Found.
+	This Lexer has been defined to track line and column information,
+	But none of the Token Types can be identified as matching a line terminator.
+	See https://chevrotain.io/docs/guide/resolving_lexer_errors.html#LINE_BREAKS 
+	for details.`,type:dn.NO_LINE_BREAKS_FLAGS}),i}function DCe(e){let t={},r=po(e);return vr(r,i=>{let n=e[i];if(Kr(n))t[i]=[];else throw Error("non exhaustive match")}),t}function Oz(e){let t=e.PATTERN;if(hh(t))return!1;if(vh(t))return!0;if(mr(t,"exec"))return!0;if(Vs(t))return!1;throw Error("non exhaustive match")}function RCe(e){return Vs(e)&&e.length===1?e.charCodeAt(0):!1}function Pz(e,t){if(mr(e,"LINE_BREAKS"))return!1;if(hh(e.PATTERN)){try{kA(t,e.PATTERN)}catch(r){return{issue:dn.IDENTIFY_TERMINATOR,errMsg:r.message}}return!1}else{if(Vs(e.PATTERN))return!1;if(Oz(e))return{issue:dn.CUSTOM_LINE_BREAK};throw Error("non exhaustive match")}}function LCe(e,t){if(t.issue===dn.IDENTIFY_TERMINATOR)return`Warning: unable to identify line terminator usage in pattern.
+	The problem is in the <${e.name}> Token Type
+	 Root cause: ${t.errMsg}.
+	For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#IDENTIFY_TERMINATOR`;if(t.issue===dn.CUSTOM_LINE_BREAK)return`Warning: A Custom Token Pattern should specify the <line_breaks> option.
+	The problem is in the <${e.name}> Token Type
+	For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_LINE_BREAK`;throw Error("non exhaustive match")}function Bz(e){return nr(e,r=>Vs(r)?r.charCodeAt(0):r)}function qE(e,t,r){e[t]===void 0?e[t]=[r]:e[t].push(r)}function fh(e){return e<B2?e:UE[e]}function MCe(){if(Oi(UE)){UE=new Array(65536);for(let e=0;e<65536;e++)UE[e]=e>255?255+~~(e/255):e}}function uy(e,t){let r=e.tokenTypeIdx;return r===t.tokenTypeIdx?!0:t.isParent===!0&&t.categoryMatchesMap[r]===!0}function hT(e,t){return e.tokenTypeIdx===t.tokenTypeIdx}function hy(e){let t=ICe(e);OCe(t),BCe(t),PCe(t),vr(t,r=>{r.isParent=r.categoryMatches.length>0})}function ICe(e){let t=ka(e),r=e,i=!0;for(;i;){r=IT(Ul(nr(r,a=>a.CATEGORIES)));let n=bA(r,t);t=t.concat(n),Oi(n)?i=!1:r=n}return t}function OCe(e){vr(e,t=>{$z(t)||(NCe[Q1e]=t,t.tokenTypeIdx=Q1e++),wF(t)&&!Kr(t.CATEGORIES)&&(t.CATEGORIES=[t.CATEGORIES]),wF(t)||(t.CATEGORIES=[]),FCe(t)||(t.categoryMatches=[]),$Ce(t)||(t.categoryMatchesMap={})})}function PCe(e){vr(e,t=>{t.categoryMatches=[],vr(t.categoryMatchesMap,(r,i)=>{t.categoryMatches.push(NCe[i].tokenTypeIdx)})})}function BCe(e){vr(e,t=>{Fz([],t)})}function Fz(e,t){vr(e,r=>{t.categoryMatchesMap[r.tokenTypeIdx]=!0}),vr(t.CATEGORIES,r=>{let i=e.concat(t);fs(i,r)||Fz(i,r)})}function $z(e){return mr(e,"tokenTypeIdx")}function wF(e){return mr(e,"CATEGORIES")}function FCe(e){return mr(e,"categoryMatches")}function $Ce(e){return mr(e,"categoryMatchesMap")}function zCe(e){return mr(e,"tokenTypeIdx")}function B0(e){return zz(e)?e.LABEL:e.name}function zz(e){return Vs(e.LABEL)&&e.LABEL!==""}function q1(e){return GCe(e)}function GCe(e){let t=e.pattern,r={};if(r.name=e.name,dh(t)||(r.PATTERN=t),mr(e,S0t))throw`The parent property is no longer supported.
+See: https://github.com/chevrotain/chevrotain/issues/564#issuecomment-349062346 for details.`;return mr(e,J1e)&&(r.CATEGORIES=e[J1e]),hy([r]),mr(e,eye)&&(r.LABEL=e[eye]),mr(e,tye)&&(r.GROUP=e[tye]),mr(e,iye)&&(r.POP_MODE=e[iye]),mr(e,rye)&&(r.PUSH_MODE=e[rye]),mr(e,nye)&&(r.LONGER_ALT=e[nye]),mr(e,aye)&&(r.LINE_BREAKS=e[aye]),mr(e,sye)&&(r.START_CHARS_HINT=e[sye]),r}function PT(e,t,r,i,n,a,s,l){return{image:t,startOffset:r,endOffset:i,startLine:n,endLine:a,startColumn:s,endColumn:l,tokenTypeIdx:e.tokenTypeIdx,tokenType:e}}function Gz(e,t){return uy(e,t)}function VCe(e,t){let r=new A0t(e,t);return r.resolveRefs(),r.errors}function _5(e,t,r=[]){r=ka(r);let i=[],n=0;function a(l){return l.concat(Ta(e,n+1))}o(a,"remainingPathWith"),S(a,"remainingPathWith");function s(l){let u=_5(a(l),t,r);return i.concat(u)}for(o(s,"getAlternativesForProd"),S(s,"getAlternativesForProd");r.length<t&&n<e.length;){let l=e[n];if(l instanceof Ws)return s(l.definition);if(l instanceof us)return 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p=f.pop();if(p===s){l&&z0(f).idx<=h&&f.pop();continue}let m=p.def,g=p.idx,y=p.ruleStack,v=p.occurrenceStack;if(Oi(m))continue;let x=m[0];if(x===n){let b={idx:g,def:Ta(m),ruleStack:cT(y),occurrenceStack:cT(v)};f.push(b)}else if(x instanceof Wi)if(g<u-1){let b=g+1,T=t[b];if(r(T,x.terminalType)){let w={idx:b,def:Ta(m),ruleStack:y,occurrenceStack:v};f.push(w)}}else if(g===u-1)d.push({nextTokenType:x.terminalType,nextTokenOccurrence:x.idx,ruleStack:y,occurrenceStack:v}),l=!0;else throw Error("non exhaustive match");else if(x instanceof us){let b=ka(y);b.push(x.nonTerminalName);let T=ka(v);T.push(x.idx);let w={idx:g,def:x.definition.concat(a,Ta(m)),ruleStack:b,occurrenceStack:T};f.push(w)}else if(x instanceof Ca){let b={idx:g,def:Ta(m),ruleStack:y,occurrenceStack:v};f.push(b),f.push(s);let T={idx:g,def:x.definition.concat(Ta(m)),ruleStack:y,occurrenceStack:v};f.push(T)}else if(x instanceof yo){let b=new 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T=x.definition[b],w={idx:g,def:T.definition.concat(Ta(m)),ruleStack:y,occurrenceStack:v};f.push(w),f.push(s)}else if(x instanceof Ws)f.push({idx:g,def:x.definition.concat(Ta(m)),ruleStack:y,occurrenceStack:v});else if(x instanceof oy)f.push(WCe(x,g,y,v));else throw Error("non exhaustive match")}return d}function WCe(e,t,r,i){let n=ka(r);n.push(e.name);let a=ka(i);return a.push(1),{idx:t,def:e.definition,ruleStack:n,occurrenceStack:a}}function SA(e){if(e instanceof Ca||e==="Option")return en.OPTION;if(e instanceof hn||e==="Repetition")return en.REPETITION;if(e instanceof yo||e==="RepetitionMandatory")return en.REPETITION_MANDATORY;if(e instanceof vo||e==="RepetitionMandatoryWithSeparator")return en.REPETITION_MANDATORY_WITH_SEPARATOR;if(e instanceof qs||e==="RepetitionWithSeparator")return en.REPETITION_WITH_SEPARATOR;if(e instanceof Us||e==="Alternation")return en.ALTERNATION;throw Error("non exhaustive match")}function EF(e){let{occurrence:t,rule:r,prodType:i,maxLookahead:n}=e,a=SA(i);return a===en.ALTERNATION?BT(t,r,n):FT(t,r,a,n)}function qCe(e,t,r,i,n,a){let s=BT(e,t,r),l=qz(s)?hT:uy;return a(s,i,l,n)}function UCe(e,t,r,i,n,a){let s=FT(e,t,n,r),l=qz(s)?hT:uy;return a(s[0],l,i)}function HCe(e,t,r,i){let n=e.length,a=Hl(e,s=>Hl(s,l=>l.length===1));if(t)return function(s){let l=nr(s,u=>u.GATE);for(let u=0;u<n;u++){let h=e[u],d=h.length,f=l[u];if(!(f!==void 0&&f.call(this)===!1))e:for(let p=0;p<d;p++){let m=h[p],g=m.length;for(let y=0;y<g;y++){let v=this.LA(y+1);if(r(v,m[y])===!1)continue e}return u}}};if(a&&!i){let s=nr(e,u=>Ul(u)),l=go(s,(u,h,d)=>(vr(h,f=>{mr(u,f.tokenTypeIdx)||(u[f.tokenTypeIdx]=d),vr(f.categoryMatches,p=>{mr(u,p)||(u[p]=d)})}),u),{});return function(){let u=this.LA(1);return l[u.tokenTypeIdx]}}else return function(){for(let s=0;s<n;s++){let l=e[s],u=l.length;e:for(let h=0;h<u;h++){let d=l[h],f=d.length;for(let p=0;p<f;p++){let m=this.LA(p+1);if(r(m,d[p])===!1)continue e}return s}}}}function YCe(e,t,r){let i=Hl(e,a=>a.length===1),n=e.length;if(i&&!r){let a=Ul(e);if(a.length===1&&Oi(a[0].categoryMatches)){let l=a[0].tokenTypeIdx;return function(){return this.LA(1).tokenTypeIdx===l}}else{let s=go(a,(l,u,h)=>(l[u.tokenTypeIdx]=!0,vr(u.categoryMatches,d=>{l[d]=!0}),l),[]);return function(){let l=this.LA(1);return s[l.tokenTypeIdx]===!0}}}else return function(){e:for(let a=0;a<n;a++){let s=e[a],l=s.length;for(let u=0;u<l;u++){let h=this.LA(u+1);if(t(h,s[u])===!1)continue e}return!0}return!1}}function AF(e){let t=new Array(e);for(let r=0;r<e;r++)t[r]=[];return t}function HE(e){let t=[""];for(let r=0;r<e.length;r++){let i=e[r],n=[];for(let a=0;a<t.length;a++){let s=t[a];n.push(s+"_"+i.tokenTypeIdx);for(let l=0;l<i.categoryMatches.length;l++){let u="_"+i.categoryMatches[l];n.push(s+u)}}t=n}return t}function XCe(e,t,r){for(let i=0;i<e.length;i++){if(i===r)continue;let n=e[i];for(let a=0;a<t.length;a++){let s=t[a];if(n[s]===!0)return!1}}return!0}function Wz(e,t){let 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no promise from cancellation strategy when cancelling id ${Rt}`),Promise.resolve()):pt.catch(()=>{B.log(`Sending cancellation messages for id ${Rt} failed`)})}));let lr={jsonrpc:F,id:Rt,method:nt,params:dt};return Ve(lr),typeof de.sender.enableCancellation=="function"&&de.sender.enableCancellation(lr),new Promise(async(pt,Se)=>{let it=S(_r=>{pt(_r),de.sender.cleanup(Rt),$t?.dispose()},"resolveWithCleanup"),xt=S(_r=>{Se(_r),de.sender.cleanup(Rt),$t?.dispose()},"rejectWithCleanup"),rr={method:nt,timerStart:Date.now(),resolve:it,reject:xt};try{await R.write(lr),J.set(Rt,rr)}catch(_r){throw B.error("Sending request failed."),rr.reject(new i.ResponseError(i.ErrorCodes.MessageWriteError,_r.message?_r.message:"Unknown reason")),_r}})},"sendRequest"),onRequest:S((pe,Qe)=>{kt();let nt=null;return f.is(pe)?(nt=void 0,V=pe):r.string(pe)?(nt=null,Qe!==void 0&&(nt=pe,H.set(pe,{handler:Qe,type:void 0}))):Qe!==void 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+	For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#MISSING_LINE_TERM_CHARS`);if(t.safeMode&&t.ensureOptimizations)throw Error('"safeMode" and "ensureOptimizations" flags are mutually exclusive.');this.trackStartLines=/full|onlyStart/i.test(this.config.positionTracking),this.trackEndLines=/full/i.test(this.config.positionTracking),Kr(e)?i={modes:{defaultMode:ka(e)},defaultMode:P2}:(n=!1,i=ka(e))}),this.config.skipValidations===!1&&(this.TRACE_INIT("performRuntimeChecks",()=>{this.lexerDefinitionErrors=this.lexerDefinitionErrors.concat(ACe(i,this.trackStartLines,this.config.lineTerminatorCharacters))}),this.TRACE_INIT("performWarningRuntimeChecks",()=>{this.lexerDefinitionWarning=this.lexerDefinitionWarning.concat(_Ce(i,this.trackStartLines,this.config.lineTerminatorCharacters))})),i.modes=i.modes?i.modes:{},vr(i.modes,(s,l)=>{i.modes[l]=TA(s,u=>dh(u))});let a=po(i.modes);if(vr(i.modes,(s,l)=>{this.TRACE_INIT(`Mode: <${l}> processing`,()=>{if(this.modes.push(l),this.config.skipValidations===!1&&this.TRACE_INIT("validatePatterns",()=>{this.lexerDefinitionErrors=this.lexerDefinitionErrors.concat(dCe(s,a))}),Oi(this.lexerDefinitionErrors)){hy(s);let u;this.TRACE_INIT("analyzeTokenTypes",()=>{u=hCe(s,{lineTerminatorCharacters:this.config.lineTerminatorCharacters,positionTracking:t.positionTracking,ensureOptimizations:t.ensureOptimizations,safeMode:t.safeMode,tracer:this.TRACE_INIT})}),this.patternIdxToConfig[l]=u.patternIdxToConfig,this.charCodeToPatternIdxToConfig[l]=u.charCodeToPatternIdxToConfig,this.emptyGroups=mo({},this.emptyGroups,u.emptyGroups),this.hasCustom=u.hasCustom||this.hasCustom,this.canModeBeOptimized[l]=u.canBeOptimized}})}),this.defaultMode=i.defaultMode,!Oi(this.lexerDefinitionErrors)&&!this.config.deferDefinitionErrorsHandling){let l=nr(this.lexerDefinitionErrors,u=>u.message).join(`-----------------------
+`);throw new Error(`Errors detected in definition of Lexer:
+`+l)}vr(this.lexerDefinitionWarning,s=>{Mz(s.message)}),this.TRACE_INIT("Choosing sub-methods implementations",()=>{if(uCe?(this.chopInput=J1,this.match=this.matchWithTest):(this.updateLastIndex=Un,this.match=this.matchWithExec),n&&(this.handleModes=Un),this.trackStartLines===!1&&(this.computeNewColumn=J1),this.trackEndLines===!1&&(this.updateTokenEndLineColumnLocation=Un),/full/i.test(this.config.positionTracking))this.createTokenInstance=this.createFullToken;else if(/onlyStart/i.test(this.config.positionTracking))this.createTokenInstance=this.createStartOnlyToken;else if(/onlyOffset/i.test(this.config.positionTracking))this.createTokenInstance=this.createOffsetOnlyToken;else throw Error(`Invalid <positionTracking> config option: "${this.config.positionTracking}"`);this.hasCustom?(this.addToken=this.addTokenUsingPush,this.handlePayload=this.handlePayloadWithCustom):(this.addToken=this.addTokenUsingMemberAccess,this.handlePayload=this.handlePayloadNoCustom)}),this.TRACE_INIT("Failed Optimization Warnings",()=>{let s=go(this.canModeBeOptimized,(l,u,h)=>(u===!1&&l.push(h),l),[]);if(t.ensureOptimizations&&!Oi(s))throw Error(`Lexer Modes: < ${s.join(", ")} > cannot be optimized.
+	 Disable the "ensureOptimizations" lexer config flag to silently ignore this and run the lexer in an un-optimized mode.
+	 Or inspect the console log for details on how to resolve these issues.`)}),this.TRACE_INIT("clearRegExpParserCache",()=>{sCe()}),this.TRACE_INIT("toFastProperties",()=>{Iz(this)})})}tokenize(e,t=this.defaultMode){if(!Oi(this.lexerDefinitionErrors)){let i=nr(this.lexerDefinitionErrors,n=>n.message).join(`-----------------------
+`);throw new Error(`Unable to Tokenize because Errors detected in definition of Lexer:
+`+i)}return this.tokenizeInternal(e,t)}tokenizeInternal(e,t){let r,i,n,a,s,l,u,h,d,f,p,m,g,y,v,x,b=e,T=b.length,w=0,C=0,k=this.hasCustom?0:Math.floor(e.length/10),E=new Array(k),A=[],N=this.trackStartLines?1:void 0,P=this.trackStartLines?1:void 0,I=DCe(this.emptyGroups),D=this.trackStartLines,_=this.config.lineTerminatorsPattern,R=0,M=[],L=[],B=[],O=[];Object.freeze(O);let $;function G(){return M}o(G,"getPossiblePatternsSlow"),S(G,"getPossiblePatternsSlow");function F(Q){let Y=fh(Q),ae=L[Y];return ae===void 0?O:ae}o(F,"getPossiblePatternsOptimized"),S(F,"getPossiblePatternsOptimized");let V=S(Q=>{if(B.length===1&&Q.tokenType.PUSH_MODE===void 0){let Y=this.config.errorMessageProvider.buildUnableToPopLexerModeMessage(Q);A.push({offset:Q.startOffset,line:Q.startLine,column:Q.startColumn,length:Q.image.length,message:Y})}else{B.pop();let Y=z0(B);M=this.patternIdxToConfig[Y],L=this.charCodeToPatternIdxToConfig[Y],R=M.length;let ae=this.canModeBeOptimized[Y]&&this.config.safeMode===!1;L&&ae?$=F:$=G}},"pop_mode");function H(Q){B.push(Q),L=this.charCodeToPatternIdxToConfig[Q],M=this.patternIdxToConfig[Q],R=M.length,R=M.length;let Y=this.canModeBeOptimized[Q]&&this.config.safeMode===!1;L&&Y?$=F:$=G}o(H,"push_mode"),S(H,"push_mode"),H.call(this,t);let j,U=this.config.recoveryEnabled;for(;w<T;){l=null;let Q=b.charCodeAt(w),Y=$(Q),ae=Y.length;for(r=0;r<ae;r++){j=Y[r];let J=j.pattern;u=null;let te=j.short;if(te!==!1?Q===te&&(l=J):j.isCustom===!0?(x=J.exec(b,w,E,I),x!==null?(l=x[0],x.payload!==void 0&&(u=x.payload)):l=null):(this.updateLastIndex(J,w),l=this.match(J,e,w)),l!==null){if(s=j.longerAlt,s!==void 0){let re=s.length;for(n=0;n<re;n++){let ee=M[s[n]],Te=ee.pattern;if(h=null,ee.isCustom===!0?(x=Te.exec(b,w,E,I),x!==null?(a=x[0],x.payload!==void 0&&(h=x.payload)):a=null):(this.updateLastIndex(Te,w),a=this.match(Te,e,w)),a&&a.length>l.length){l=a,u=h,j=ee;break}}}break}}if(l!==null){if(d=l.length,f=j.group,f!==void 0&&(p=j.tokenTypeIdx,m=this.createTokenInstance(l,w,p,j.tokenType,N,P,d),this.handlePayload(m,u),f===!1?C=this.addToken(E,C,m):I[f].push(m)),e=this.chopInput(e,d),w=w+d,P=this.computeNewColumn(P,d),D===!0&&j.canLineTerminator===!0){let J=0,te,re;_.lastIndex=0;do te=_.test(l),te===!0&&(re=_.lastIndex-1,J++);while(te===!0);J!==0&&(N=N+J,P=d-re,this.updateTokenEndLineColumnLocation(m,f,re,J,N,P,d))}this.handleModes(j,V,H,m)}else{let J=w,te=N,re=P,ee=U===!1;for(;ee===!1&&w<T;)for(e=this.chopInput(e,1),w++,i=0;i<R;i++){let Te=M[i],ue=Te.pattern,De=Te.short;if(De!==!1?b.charCodeAt(w)===De&&(ee=!0):Te.isCustom===!0?ee=ue.exec(b,w,E,I)!==null:(this.updateLastIndex(ue,w),ee=ue.exec(e)!==null),ee===!0)break}if(g=w-J,P=this.computeNewColumn(P,g),v=this.config.errorMessageProvider.buildUnexpectedCharactersMessage(b,J,g,te,re,z0(B)),A.push({offset:J,line:te,column:re,length:g,message:v}),U===!1)break}}return this.hasCustom||(E.length=C),{tokens:E,groups:I,errors:A}}handleModes(e,t,r,i){if(e.pop===!0){let n=e.push;t(i),n!==void 0&&r.call(this,n)}else e.push!==void 0&&r.call(this,e.push)}chopInput(e,t){return e.substring(t)}updateLastIndex(e,t){e.lastIndex=t}updateTokenEndLineColumnLocation(e,t,r,i,n,a,s){let l,u;t!==void 0&&(l=r===s-1,u=l?-1:0,i===1&&l===!0||(e.endLine=n+u,e.endColumn=a-1+-u))}computeNewColumn(e,t){return e+t}createOffsetOnlyToken(e,t,r,i){return{image:e,startOffset:t,tokenTypeIdx:r,tokenType:i}}createStartOnlyToken(e,t,r,i,n,a){return{image:e,startOffset:t,startLine:n,startColumn:a,tokenTypeIdx:r,tokenType:i}}createFullToken(e,t,r,i,n,a,s){return{image:e,startOffset:t,endOffset:t+s-1,startLine:n,endLine:n,startColumn:a,endColumn:a+s-1,tokenTypeIdx:r,tokenType:i}}addTokenUsingPush(e,t,r){return e.push(r),t}addTokenUsingMemberAccess(e,t,r){return e[t]=r,t++,t}handlePayloadNoCustom(e,t){}handlePayloadWithCustom(e,t){t!==null&&(e.payload=t)}matchWithTest(e,t,r){return e.test(t)===!0?t.substring(r,e.lastIndex):null}matchWithExec(e,t){let r=e.exec(t);return r!==null?r[0]:null}};ls.SKIPPED="This marks a skipped Token pattern, this means each token identified by it will be consumed and then thrown into oblivion, this can be used to for example to completely ignore whitespace.";ls.NA=/NOT_APPLICABLE/;o(B0,"tokenLabel2");S(B0,"tokenLabel");o(zz,"hasTokenLabel2");S(zz,"hasTokenLabel");S0t="parent",J1e="categories",eye="label",tye="group",rye="push_mode",iye="pop_mode",nye="longer_alt",aye="line_breaks",sye="start_chars_hint";o(q1,"createToken");S(q1,"createToken");o(GCe,"createTokenInternal");S(GCe,"createTokenInternal");gf=q1({name:"EOF",pattern:ls.NA});hy([gf]);o(PT,"createTokenInstance");S(PT,"createTokenInstance");o(Gz,"tokenMatcher");S(Gz,"tokenMatcher");z1={buildMismatchTokenMessage({expected:e,actual:t,previous:r,ruleName:i}){return`Expecting ${zz(e)?`--> ${B0(e)} <--`:`token of type --> ${e.name} <--`} but found --> '${t.image}' <--`},buildNotAllInputParsedMessage({firstRedundant:e,ruleName:t}){return"Redundant input, expecting EOF but found: "+e.image},buildNoViableAltMessage({expectedPathsPerAlt:e,actual:t,previous:r,customUserDescription:i,ruleName:n}){let a="Expecting: ",l=`
+but found: '`+jl(t).image+"'";if(i)return a+i+l;{let u=go(e,(p,m)=>p.concat(m),[]),h=nr(u,p=>`[${nr(p,m=>B0(m)).join(", ")}]`),f=`one of these possible Token sequences:
+${nr(h,(p,m)=>`  ${m+1}. ${p}`).join(`
+`)}`;return a+f+l}},buildEarlyExitMessage({expectedIterationPaths:e,actual:t,customUserDescription:r,ruleName:i}){let n="Expecting: ",s=`
+but found: '`+jl(t).image+"'";if(r)return n+r+s;{let u=`expecting at least one iteration which starts with one of these possible Token sequences::
+  <${nr(e,h=>`[${nr(h,d=>B0(d)).join(",")}]`).join(" ,")}>`;return n+u+s}}};Object.freeze(z1);E0t={buildRuleNotFoundError(e,t){return"Invalid grammar, reference to a rule which is not defined: ->"+t.nonTerminalName+`<-
+inside top level rule: ->`+e.name+"<-"}},I0={buildDuplicateFoundError(e,t){function r(d){return d instanceof Wi?d.terminalType.name:d instanceof us?d.nonTerminalName:""}o(r,"getExtraProductionArgument2"),S(r,"getExtraProductionArgument");let i=e.name,n=jl(t),a=n.idx,s=Gl(n),l=r(n),u=a>0,h=`->${s}${u?a:""}<- ${l?`with argument: ->${l}<-`:""}
+                  appears more than once (${t.length} times) in the top level rule: ->${i}<-.                  
+                  For further details see: https://chevrotain.io/docs/FAQ.html#NUMERICAL_SUFFIXES 
+                  `;return h=h.replace(/[ \t]+/g," "),h=h.replace(/\s\s+/g,`
+`),h},buildNamespaceConflictError(e){return`Namespace conflict found in grammar.
+The grammar has both a Terminal(Token) and a Non-Terminal(Rule) named: <${e.name}>.
+To resolve this make sure each Terminal and Non-Terminal names are unique
+This is easy to accomplish by using the convention that Terminal names start with an uppercase letter
+and Non-Terminal names start with a lower case letter.`},buildAlternationPrefixAmbiguityError(e){let t=nr(e.prefixPath,n=>B0(n)).join(", "),r=e.alternation.idx===0?"":e.alternation.idx;return`Ambiguous alternatives: <${e.ambiguityIndices.join(" ,")}> due to common lookahead prefix
+in <OR${r}> inside <${e.topLevelRule.name}> Rule,
+<${t}> may appears as a prefix path in all these alternatives.
+See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#COMMON_PREFIX
+For Further details.`},buildAlternationAmbiguityError(e){let t=nr(e.prefixPath,n=>B0(n)).join(", "),r=e.alternation.idx===0?"":e.alternation.idx,i=`Ambiguous Alternatives Detected: <${e.ambiguityIndices.join(" ,")}> in <OR${r}> inside <${e.topLevelRule.name}> Rule,
+<${t}> may appears as a prefix path in all these alternatives.
+`;return i=i+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES
+For Further details.`,i},buildEmptyRepetitionError(e){let t=Gl(e.repetition);return e.repetition.idx!==0&&(t+=e.repetition.idx),`The repetition <${t}> within Rule <${e.topLevelRule.name}> can never consume any tokens.
+This could lead to an infinite loop.`},buildTokenNameError(e){return"deprecated"},buildEmptyAlternationError(e){return`Ambiguous empty alternative: <${e.emptyChoiceIdx+1}> in <OR${e.alternation.idx}> inside <${e.topLevelRule.name}> Rule.
+Only the last alternative may be an empty alternative.`},buildTooManyAlternativesError(e){return`An Alternation cannot have more than 256 alternatives:
+<OR${e.alternation.idx}> inside <${e.topLevelRule.name}> Rule.
+ has ${e.alternation.definition.length+1} alternatives.`},buildLeftRecursionError(e){let t=e.topLevelRule.name,r=nr(e.leftRecursionPath,a=>a.name),i=`${t} --> ${r.concat([t]).join(" --> ")}`;return`Left Recursion found in grammar.
+rule: <${t}> can be invoked from itself (directly or indirectly)
+without consuming any Tokens. The grammar path that causes this is: 
+ ${i}
+ To fix this refactor your grammar to remove the left recursion.
+see: https://en.wikipedia.org/wiki/LL_parser#Left_factoring.`},buildInvalidRuleNameError(e){return"deprecated"},buildDuplicateRuleNameError(e){let t;return e.topLevelRule instanceof oy?t=e.topLevelRule.name:t=e.topLevelRule,`Duplicate definition, rule: ->${t}<- is already defined in the grammar: ->${e.grammarName}<-`}};o(VCe,"resolveGrammar");S(VCe,"resolveGrammar");A0t=class extends ly{static{o(this,"GastRefResolverVisitor")}static{S(this,"GastRefResolverVisitor")}constructor(e,t){super(),this.nameToTopRule=e,this.errMsgProvider=t,this.errors=[]}resolveRefs(){vr(Hn(this.nameToTopRule),e=>{this.currTopLevel=e,e.accept(this)})}visitNonTerminal(e){let t=this.nameToTopRule[e.nonTerminalName];if(t)e.referencedRule=t;else{let r=this.errMsgProvider.buildRuleNotFoundError(this.currTopLevel,e);this.errors.push({message:r,type:hs.UNRESOLVED_SUBRULE_REF,ruleName:this.currTopLevel.name,unresolvedRefName:e.nonTerminalName})}}},_0t=class extends CA{static{o(this,"AbstractNextPossibleTokensWalker")}static{S(this,"AbstractNextPossibleTokensWalker")}constructor(e,t){super(),this.topProd=e,this.path=t,this.possibleTokTypes=[],this.nextProductionName="",this.nextProductionOccurrence=0,this.found=!1,this.isAtEndOfPath=!1}startWalking(){if(this.found=!1,this.path.ruleStack[0]!==this.topProd.name)throw Error("The path does not start with the walker's top Rule!");return this.ruleStack=ka(this.path.ruleStack).reverse(),this.occurrenceStack=ka(this.path.occurrenceStack).reverse(),this.ruleStack.pop(),this.occurrenceStack.pop(),this.updateExpectedNext(),this.walk(this.topProd),this.possibleTokTypes}walk(e,t=[]){this.found||super.walk(e,t)}walkProdRef(e,t,r){if(e.referencedRule.name===this.nextProductionName&&e.idx===this.nextProductionOccurrence){let i=t.concat(r);this.updateExpectedNext(),this.walk(e.referencedRule,i)}}updateExpectedNext(){Oi(this.ruleStack)?(this.nextProductionName="",this.nextProductionOccurrence=0,this.isAtEndOfPath=!0):(this.nextProductionName=this.ruleStack.pop(),this.nextProductionOccurrence=this.occurrenceStack.pop())}},D0t=class extends _0t{static{o(this,"NextAfterTokenWalker")}static{S(this,"NextAfterTokenWalker")}constructor(e,t){super(e,t),this.path=t,this.nextTerminalName="",this.nextTerminalOccurrence=0,this.nextTerminalName=this.path.lastTok.name,this.nextTerminalOccurrence=this.path.lastTokOccurrence}walkTerminal(e,t,r){if(this.isAtEndOfPath&&e.terminalType.name===this.nextTerminalName&&e.idx===this.nextTerminalOccurrence&&!this.found){let i=t.concat(r),n=new Ws({definition:i});this.possibleTokTypes=cy(n),this.found=!0}}},wA=class extends CA{static{o(this,"AbstractNextTerminalAfterProductionWalker")}static{S(this,"AbstractNextTerminalAfterProductionWalker")}constructor(e,t){super(),this.topRule=e,this.occurrence=t,this.result={token:void 0,occurrence:void 0,isEndOfRule:void 0}}startWalking(){return this.walk(this.topRule),this.result}},R0t=class extends wA{static{o(this,"NextTerminalAfterManyWalker")}static{S(this,"NextTerminalAfterManyWalker")}walkMany(e,t,r){if(e.idx===this.occurrence){let i=jl(t.concat(r));this.result.isEndOfRule=i===void 0,i instanceof Wi&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkMany(e,t,r)}},oye=class extends wA{static{o(this,"NextTerminalAfterManySepWalker")}static{S(this,"NextTerminalAfterManySepWalker")}walkManySep(e,t,r){if(e.idx===this.occurrence){let i=jl(t.concat(r));this.result.isEndOfRule=i===void 0,i instanceof Wi&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkManySep(e,t,r)}},L0t=class extends wA{static{o(this,"NextTerminalAfterAtLeastOneWalker")}static{S(this,"NextTerminalAfterAtLeastOneWalker")}walkAtLeastOne(e,t,r){if(e.idx===this.occurrence){let i=jl(t.concat(r));this.result.isEndOfRule=i===void 0,i instanceof Wi&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkAtLeastOne(e,t,r)}},lye=class extends wA{static{o(this,"NextTerminalAfterAtLeastOneSepWalker")}static{S(this,"NextTerminalAfterAtLeastOneSepWalker")}walkAtLeastOneSep(e,t,r){if(e.idx===this.occurrence){let i=jl(t.concat(r));this.result.isEndOfRule=i===void 0,i instanceof Wi&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkAtLeastOneSep(e,t,r)}};o(_5,"possiblePathsFrom");S(_5,"possiblePathsFrom");o(Vz,"nextPossibleTokensAfter");S(Vz,"nextPossibleTokensAfter");o(WCe,"expandTopLevelRule");S(WCe,"expandTopLevelRule");(function(e){e[e.OPTION=0]="OPTION",e[e.REPETITION=1]="REPETITION",e[e.REPETITION_MANDATORY=2]="REPETITION_MANDATORY",e[e.REPETITION_MANDATORY_WITH_SEPARATOR=3]="REPETITION_MANDATORY_WITH_SEPARATOR",e[e.REPETITION_WITH_SEPARATOR=4]="REPETITION_WITH_SEPARATOR",e[e.ALTERNATION=5]="ALTERNATION"})(en||(en={}));o(SA,"getProdType");S(SA,"getProdType");o(EF,"getLookaheadPaths");S(EF,"getLookaheadPaths");o(qCe,"buildLookaheadFuncForOr");S(qCe,"buildLookaheadFuncForOr");o(UCe,"buildLookaheadFuncForOptionalProd");S(UCe,"buildLookaheadFuncForOptionalProd");o(HCe,"buildAlternativesLookAheadFunc");S(HCe,"buildAlternativesLookAheadFunc");o(YCe,"buildSingleAlternativeLookaheadFunction");S(YCe,"buildSingleAlternativeLookaheadFunction");M0t=class extends CA{static{o(this,"RestDefinitionFinderWalker")}static{S(this,"RestDefinitionFinderWalker")}constructor(e,t,r){super(),this.topProd=e,this.targetOccurrence=t,this.targetProdType=r}startWalking(){return this.walk(this.topProd),this.restDef}checkIsTarget(e,t,r,i){return e.idx===this.targetOccurrence&&this.targetProdType===t?(this.restDef=r.concat(i),!0):!1}walkOption(e,t,r){this.checkIsTarget(e,en.OPTION,t,r)||super.walkOption(e,t,r)}walkAtLeastOne(e,t,r){this.checkIsTarget(e,en.REPETITION_MANDATORY,t,r)||super.walkOption(e,t,r)}walkAtLeastOneSep(e,t,r){this.checkIsTarget(e,en.REPETITION_MANDATORY_WITH_SEPARATOR,t,r)||super.walkOption(e,t,r)}walkMany(e,t,r){this.checkIsTarget(e,en.REPETITION,t,r)||super.walkOption(e,t,r)}walkManySep(e,t,r){this.checkIsTarget(e,en.REPETITION_WITH_SEPARATOR,t,r)||super.walkOption(e,t,r)}},jCe=class extends ly{static{o(this,"InsideDefinitionFinderVisitor")}static{S(this,"InsideDefinitionFinderVisitor")}constructor(e,t,r){super(),this.targetOccurrence=e,this.targetProdType=t,this.targetRef=r,this.result=[]}checkIsTarget(e,t){e.idx===this.targetOccurrence&&this.targetProdType===t&&(this.targetRef===void 0||e===this.targetRef)&&(this.result=e.definition)}visitOption(e){this.checkIsTarget(e,en.OPTION)}visitRepetition(e){this.checkIsTarget(e,en.REPETITION)}visitRepetitionMandatory(e){this.checkIsTarget(e,en.REPETITION_MANDATORY)}visitRepetitionMandatoryWithSeparator(e){this.checkIsTarget(e,en.REPETITION_MANDATORY_WITH_SEPARATOR)}visitRepetitionWithSeparator(e){this.checkIsTarget(e,en.REPETITION_WITH_SEPARATOR)}visitAlternation(e){this.checkIsTarget(e,en.ALTERNATION)}};o(AF,"initializeArrayOfArrays");S(AF,"initializeArrayOfArrays");o(HE,"pathToHashKeys");S(HE,"pathToHashKeys");o(XCe,"isUniquePrefixHash");S(XCe,"isUniquePrefixHash");o(Wz,"lookAheadSequenceFromAlternatives");S(Wz,"lookAheadSequenceFromAlternatives");o(BT,"getLookaheadPathsForOr");S(BT,"getLookaheadPathsForOr");o(FT,"getLookaheadPathsForOptionalProd");S(FT,"getLookaheadPathsForOptionalProd");o(D5,"containsPath");S(D5,"containsPath");o(KCe,"isStrictPrefixOfPath");S(KCe,"isStrictPrefixOfPath");o(qz,"areTokenCategoriesNotUsed");S(qz,"areTokenCategoriesNotUsed");o(ZCe,"validateLookahead");S(ZCe,"validateLookahead");o(QCe,"validateGrammar");S(QCe,"validateGrammar");o(JCe,"validateDuplicateProductions");S(JCe,"validateDuplicateProductions");o(eke,"identifyProductionForDuplicates");S(eke,"identifyProductionForDuplicates");o(Uz,"getExtraProductionArgument");S(Uz,"getExtraProductionArgument");N0t=class extends ly{static{o(this,"OccurrenceValidationCollector")}static{S(this,"OccurrenceValidationCollector")}constructor(){super(...arguments),this.allProductions=[]}visitNonTerminal(e){this.allProductions.push(e)}visitOption(e){this.allProductions.push(e)}visitRepetitionWithSeparator(e){this.allProductions.push(e)}visitRepetitionMandatory(e){this.allProductions.push(e)}visitRepetitionMandatoryWithSeparator(e){this.allProductions.push(e)}visitRepetition(e){this.allProductions.push(e)}visitAlternation(e){this.allProductions.push(e)}visitTerminal(e){this.allProductions.push(e)}};o(tke,"validateRuleDoesNotAlreadyExist");S(tke,"validateRuleDoesNotAlreadyExist");o(rke,"validateRuleIsOverridden");S(rke,"validateRuleIsOverridden");o(Hz,"validateNoLeftRecursion");S(Hz,"validateNoLeftRecursion");o(Z2,"getFirstNoneTerminal");S(Z2,"getFirstNoneTerminal");Yz=class extends ly{static{o(this,"OrCollector")}static{S(this,"OrCollector")}constructor(){super(...arguments),this.alternations=[]}visitAlternation(e){this.alternations.push(e)}};o(ike,"validateEmptyOrAlternative");S(ike,"validateEmptyOrAlternative");o(nke,"validateAmbiguousAlternationAlternatives");S(nke,"validateAmbiguousAlternationAlternatives");I0t=class extends ly{static{o(this,"RepetitionCollector")}static{S(this,"RepetitionCollector")}constructor(){super(...arguments),this.allProductions=[]}visitRepetitionWithSeparator(e){this.allProductions.push(e)}visitRepetitionMandatory(e){this.allProductions.push(e)}visitRepetitionMandatoryWithSeparator(e){this.allProductions.push(e)}visitRepetition(e){this.allProductions.push(e)}};o(ake,"validateTooManyAlts");S(ake,"validateTooManyAlts");o(ske,"validateSomeNonEmptyLookaheadPath");S(ske,"validateSomeNonEmptyLookaheadPath");o(oke,"checkAlternativesAmbiguities");S(oke,"checkAlternativesAmbiguities");o(lke,"checkPrefixAlternativesAmbiguities");S(lke,"checkPrefixAlternativesAmbiguities");o(cke,"checkTerminalAndNoneTerminalsNameSpace");S(cke,"checkTerminalAndNoneTerminalsNameSpace");o(uke,"resolveGrammar2");S(uke,"resolveGrammar");o(hke,"validateGrammar2");S(hke,"validateGrammar");dke="MismatchedTokenException",fke="NoViableAltException",pke="EarlyExitException",mke="NotAllInputParsedException",gke=[dke,fke,pke,mke];Object.freeze(gke);o(dT,"isRecognitionException");S(dT,"isRecognitionException");EA=class extends Error{static{o(this,"RecognitionException")}static{S(this,"RecognitionException")}constructor(e,t){super(e),this.token=t,this.resyncedTokens=[],Object.setPrototypeOf(this,new.target.prototype),Error.captureStackTrace&&Error.captureStackTrace(this,this.constructor)}},yke=class extends EA{static{o(this,"MismatchedTokenException")}static{S(this,"MismatchedTokenException")}constructor(e,t,r){super(e,t),this.previousToken=r,this.name=dke}},O0t=class extends EA{static{o(this,"NoViableAltException")}static{S(this,"NoViableAltException")}constructor(e,t,r){super(e,t),this.previousToken=r,this.name=fke}},P0t=class extends EA{static{o(this,"NotAllInputParsedException")}static{S(this,"NotAllInputParsedException")}constructor(e,t){super(e,t),this.name=mke}},B0t=class extends EA{static{o(this,"EarlyExitException")}static{S(this,"EarlyExitException")}constructor(e,t,r){super(e,t),this.previousToken=r,this.name=pke}},KP={},vke="InRuleRecoveryException",F0t=class extends Error{static{o(this,"InRuleRecoveryException")}static{S(this,"InRuleRecoveryException")}constructor(e){super(e),this.name=vke}},$0t=class{static{o(this,"Recoverable")}static{S(this,"Recoverable")}initRecoverable(e){this.firstAfterRepMap={},this.resyncFollows={},this.recoveryEnabled=mr(e,"recoveryEnabled")?e.recoveryEnabled:ph.recoveryEnabled,this.recoveryEnabled&&(this.attemptInRepetitionRecovery=xke)}getTokenToInsert(e){let t=PT(e,"",NaN,NaN,NaN,NaN,NaN,NaN);return t.isInsertedInRecovery=!0,t}canTokenTypeBeInsertedInRecovery(e){return!0}canTokenTypeBeDeletedInRecovery(e){return!0}tryInRepetitionRecovery(e,t,r,i){let n=this.findReSyncTokenType(),a=this.exportLexerState(),s=[],l=!1,u=this.LA(1),h=this.LA(1),d=S(()=>{let f=this.LA(0),p=this.errorMessageProvider.buildMismatchTokenMessage({expected:i,actual:u,previous:f,ruleName:this.getCurrRuleFullName()}),m=new yke(p,u,this.LA(0));m.resyncedTokens=cT(s),this.SAVE_ERROR(m)},"generateErrorMessage");for(;!l;)if(this.tokenMatcher(h,i)){d();return}else if(r.call(this)){d(),e.apply(this,t);return}else this.tokenMatcher(h,n)?l=!0:(h=this.SKIP_TOKEN(),this.addToResyncTokens(h,s));this.importLexerState(a)}shouldInRepetitionRecoveryBeTried(e,t,r){return!(r===!1||this.tokenMatcher(this.LA(1),e)||this.isBackTracking()||this.canPerformInRuleRecovery(e,this.getFollowsForInRuleRecovery(e,t)))}getFollowsForInRuleRecovery(e,t){let r=this.getCurrentGrammarPath(e,t);return this.getNextPossibleTokenTypes(r)}tryInRuleRecovery(e,t){if(this.canRecoverWithSingleTokenInsertion(e,t))return this.getTokenToInsert(e);if(this.canRecoverWithSingleTokenDeletion(e)){let r=this.SKIP_TOKEN();return this.consumeToken(),r}throw new F0t("sad sad panda")}canPerformInRuleRecovery(e,t){return this.canRecoverWithSingleTokenInsertion(e,t)||this.canRecoverWithSingleTokenDeletion(e)}canRecoverWithSingleTokenInsertion(e,t){if(!this.canTokenTypeBeInsertedInRecovery(e)||Oi(t))return!1;let r=this.LA(1);return ty(t,n=>this.tokenMatcher(r,n))!==void 0}canRecoverWithSingleTokenDeletion(e){return this.canTokenTypeBeDeletedInRecovery(e)?this.tokenMatcher(this.LA(2),e):!1}isInCurrentRuleReSyncSet(e){let t=this.getCurrFollowKey(),r=this.getFollowSetFromFollowKey(t);return fs(r,e)}findReSyncTokenType(){let e=this.flattenFollowSet(),t=this.LA(1),r=2;for(;;){let i=ty(e,n=>Gz(t,n));if(i!==void 0)return i;t=this.LA(r),r++}}getCurrFollowKey(){if(this.RULE_STACK.length===1)return KP;let e=this.getLastExplicitRuleShortName(),t=this.getLastExplicitRuleOccurrenceIndex(),r=this.getPreviousExplicitRuleShortName();return{ruleName:this.shortRuleNameToFullName(e),idxInCallingRule:t,inRule:this.shortRuleNameToFullName(r)}}buildFullFollowKeyStack(){let e=this.RULE_STACK,t=this.RULE_OCCURRENCE_STACK;return nr(e,(r,i)=>i===0?KP:{ruleName:this.shortRuleNameToFullName(r),idxInCallingRule:t[i],inRule:this.shortRuleNameToFullName(e[i-1])})}flattenFollowSet(){let e=nr(this.buildFullFollowKeyStack(),t=>this.getFollowSetFromFollowKey(t));return Ul(e)}getFollowSetFromFollowKey(e){if(e===KP)return[gf];let t=e.ruleName+e.idxInCallingRule+iCe+e.inRule;return this.resyncFollows[t]}addToResyncTokens(e,t){return this.tokenMatcher(e,gf)||t.push(e),t}reSyncTo(e){let t=[],r=this.LA(1);for(;this.tokenMatcher(r,e)===!1;)r=this.SKIP_TOKEN(),this.addToResyncTokens(r,t);return cT(t)}attemptInRepetitionRecovery(e,t,r,i,n,a,s){}getCurrentGrammarPath(e,t){let r=this.getHumanReadableRuleStack(),i=ka(this.RULE_OCCURRENCE_STACK);return{ruleStack:r,occurrenceStack:i,lastTok:e,lastTokOccurrence:t}}getHumanReadableRuleStack(){return nr(this.RULE_STACK,e=>this.shortRuleNameToFullName(e))}};o(xke,"attemptInRepetitionRecovery");S(xke,"attemptInRepetitionRecovery");z0t=4,Cf=8,G0t=8,bke=1<<Cf,Tke=2<<Cf,_F=3<<Cf,DF=4<<Cf,RF=5<<Cf,YE=6<<Cf;o(jE,"getKeyForAutomaticLookahead");S(jE,"getKeyForAutomaticLookahead");_wr=32-G0t,jz=class{static{o(this,"LLkLookaheadStrategy")}static{S(this,"LLkLookaheadStrategy")}constructor(e){var t;this.maxLookahead=(t=e?.maxLookahead)!==null&&t!==void 0?t:ph.maxLookahead}validate(e){let t=this.validateNoLeftRecursion(e.rules);if(Oi(t)){let r=this.validateEmptyOrAlternatives(e.rules),i=this.validateAmbiguousAlternationAlternatives(e.rules,this.maxLookahead),n=this.validateSomeNonEmptyLookaheadPath(e.rules,this.maxLookahead);return[...t,...r,...i,...n]}return t}validateNoLeftRecursion(e){return Jo(e,t=>Hz(t,t,I0))}validateEmptyOrAlternatives(e){return Jo(e,t=>ike(t,I0))}validateAmbiguousAlternationAlternatives(e,t){return Jo(e,r=>nke(r,t,I0))}validateSomeNonEmptyLookaheadPath(e,t){return ske(e,t,I0)}buildLookaheadForAlternation(e){return qCe(e.prodOccurrence,e.rule,e.maxLookahead,e.hasPredicates,e.dynamicTokensEnabled,HCe)}buildLookaheadForOptional(e){return UCe(e.prodOccurrence,e.rule,e.maxLookahead,e.dynamicTokensEnabled,SA(e.prodType),YCe)}},V0t=class{static{o(this,"LooksAhead")}static{S(this,"LooksAhead")}initLooksAhead(e){this.dynamicTokensEnabled=mr(e,"dynamicTokensEnabled")?e.dynamicTokensEnabled:ph.dynamicTokensEnabled,this.maxLookahead=mr(e,"maxLookahead")?e.maxLookahead:ph.maxLookahead,this.lookaheadStrategy=mr(e,"lookaheadStrategy")?e.lookaheadStrategy:new jz({maxLookahead:this.maxLookahead}),this.lookAheadFuncsCache=new Map}preComputeLookaheadFunctions(e){vr(e,t=>{this.TRACE_INIT(`${t.name} Rule Lookahead`,()=>{let{alternation:r,repetition:i,option:n,repetitionMandatory:a,repetitionMandatoryWithSeparator:s,repetitionWithSeparator:l}=Cke(t);vr(r,u=>{let h=u.idx===0?"":u.idx;this.TRACE_INIT(`${Gl(u)}${h}`,()=>{let d=this.lookaheadStrategy.buildLookaheadForAlternation({prodOccurrence:u.idx,rule:t,maxLookahead:u.maxLookahead||this.maxLookahead,hasPredicates:u.hasPredicates,dynamicTokensEnabled:this.dynamicTokensEnabled}),f=jE(this.fullRuleNameToShort[t.name],bke,u.idx);this.setLaFuncCache(f,d)})}),vr(i,u=>{this.computeLookaheadFunc(t,u.idx,_F,"Repetition",u.maxLookahead,Gl(u))}),vr(n,u=>{this.computeLookaheadFunc(t,u.idx,Tke,"Option",u.maxLookahead,Gl(u))}),vr(a,u=>{this.computeLookaheadFunc(t,u.idx,DF,"RepetitionMandatory",u.maxLookahead,Gl(u))}),vr(s,u=>{this.computeLookaheadFunc(t,u.idx,YE,"RepetitionMandatoryWithSeparator",u.maxLookahead,Gl(u))}),vr(l,u=>{this.computeLookaheadFunc(t,u.idx,RF,"RepetitionWithSeparator",u.maxLookahead,Gl(u))})})})}computeLookaheadFunc(e,t,r,i,n,a){this.TRACE_INIT(`${a}${t===0?"":t}`,()=>{let s=this.lookaheadStrategy.buildLookaheadForOptional({prodOccurrence:t,rule:e,maxLookahead:n||this.maxLookahead,dynamicTokensEnabled:this.dynamicTokensEnabled,prodType:i}),l=jE(this.fullRuleNameToShort[e.name],r,t);this.setLaFuncCache(l,s)})}getKeyForAutomaticLookahead(e,t){let r=this.getLastExplicitRuleShortName();return jE(r,e,t)}getLaFuncFromCache(e){return this.lookAheadFuncsCache.get(e)}setLaFuncCache(e,t){this.lookAheadFuncsCache.set(e,t)}},W0t=class extends ly{static{o(this,"DslMethodsCollectorVisitor")}static{S(this,"DslMethodsCollectorVisitor")}constructor(){super(...arguments),this.dslMethods={option:[],alternation:[],repetition:[],repetitionWithSeparator:[],repetitionMandatory:[],repetitionMandatoryWithSeparator:[]}}reset(){this.dslMethods={option:[],alternation:[],repetition:[],repetitionWithSeparator:[],repetitionMandatory:[],repetitionMandatoryWithSeparator:[]}}visitOption(e){this.dslMethods.option.push(e)}visitRepetitionWithSeparator(e){this.dslMethods.repetitionWithSeparator.push(e)}visitRepetitionMandatory(e){this.dslMethods.repetitionMandatory.push(e)}visitRepetitionMandatoryWithSeparator(e){this.dslMethods.repetitionMandatoryWithSeparator.push(e)}visitRepetition(e){this.dslMethods.repetition.push(e)}visitAlternation(e){this.dslMethods.alternation.push(e)}},vE=new 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if(/full/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=MF,this.setNodeLocationFromNode=MF,this.cstPostRule=Un,this.setInitialNodeLocation=this.setInitialNodeLocationFullRecovery):(this.setNodeLocationFromToken=Un,this.setNodeLocationFromNode=Un,this.cstPostRule=this.cstPostRuleFull,this.setInitialNodeLocation=this.setInitialNodeLocationFullRegular);else if(/onlyOffset/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=LF,this.setNodeLocationFromNode=LF,this.cstPostRule=Un,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRecovery):(this.setNodeLocationFromToken=Un,this.setNodeLocationFromNode=Un,this.cstPostRule=this.cstPostRuleOnlyOffset,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRegular);else if(/none/i.test(this.nodeLocationTracking))this.setNodeLocationFromToken=Un,this.setNodeLocationFromNode=Un,this.cstPostRule=Un,this.setInitialNodeLocation=Un;else throw Error(`Invalid <nodeLocationTracking> config option: "${e.nodeLocationTracking}"`)}setInitialNodeLocationOnlyOffsetRecovery(e){e.location={startOffset:NaN,endOffset:NaN}}setInitialNodeLocationOnlyOffsetRegular(e){e.location={startOffset:this.LA(1).startOffset,endOffset:NaN}}setInitialNodeLocationFullRecovery(e){e.location={startOffset:NaN,startLine:NaN,startColumn:NaN,endOffset:NaN,endLine:NaN,endColumn:NaN}}setInitialNodeLocationFullRegular(e){let t=this.LA(1);e.location={startOffset:t.startOffset,startLine:t.startLine,startColumn:t.startColumn,endOffset:NaN,endLine:NaN,endColumn:NaN}}cstInvocationStateUpdate(e){let t={name:e,children:Object.create(null)};this.setInitialNodeLocation(t),this.CST_STACK.push(t)}cstFinallyStateUpdate(){this.CST_STACK.pop()}cstPostRuleFull(e){let t=this.LA(0),r=e.location;r.startOffset<=t.startOffset?(r.endOffset=t.endOffset,r.endLine=t.endLine,r.endColumn=t.endColumn):(r.startOffset=NaN,r.startLine=NaN,r.startColumn=NaN)}cstPostRuleOnlyOffset(e){let t=this.LA(0),r=e.location;r.startOffset<=t.startOffset?r.endOffset=t.endOffset:r.startOffset=NaN}cstPostTerminal(e,t){let r=this.CST_STACK[this.CST_STACK.length-1];kke(r,t,e),this.setNodeLocationFromToken(r.location,t)}cstPostNonTerminal(e,t){let r=this.CST_STACK[this.CST_STACK.length-1];wke(r,t,e),this.setNodeLocationFromNode(r.location,e.location)}getBaseCstVisitorConstructor(){if(dh(this.baseCstVisitorConstructor)){let e=Eke(this.className,po(this.gastProductionsCache));return this.baseCstVisitorConstructor=e,e}return this.baseCstVisitorConstructor}getBaseCstVisitorConstructorWithDefaults(){if(dh(this.baseCstVisitorWithDefaultsConstructor)){let e=Ake(this.className,po(this.gastProductionsCache),this.getBaseCstVisitorConstructor());return this.baseCstVisitorWithDefaultsConstructor=e,e}return this.baseCstVisitorWithDefaultsConstructor}getLastExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-1]}getPreviousExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-2]}getLastExplicitRuleOccurrenceIndex(){let e=this.RULE_OCCURRENCE_STACK;return e[e.length-1]}},H0t=class{static{o(this,"LexerAdapter")}static{S(this,"LexerAdapter")}initLexerAdapter(){this.tokVector=[],this.tokVectorLength=0,this.currIdx=-1}set input(e){if(this.selfAnalysisDone!==!0)throw Error("Missing <performSelfAnalysis> invocation at the end of the Parser's constructor.");this.reset(),this.tokVector=e,this.tokVectorLength=e.length}get input(){return this.tokVector}SKIP_TOKEN(){return this.currIdx<=this.tokVector.length-2?(this.consumeToken(),this.LA(1)):R5}LA(e){let t=this.currIdx+e;return t<0||this.tokVectorLength<=t?R5:this.tokVector[t]}consumeToken(){this.currIdx++}exportLexerState(){return this.currIdx}importLexerState(e){this.currIdx=e}resetLexerState(){this.currIdx=-1}moveToTerminatedState(){this.currIdx=this.tokVector.length-1}getLexerPosition(){return this.exportLexerState()}},Y0t=class{static{o(this,"RecognizerApi")}static{S(this,"RecognizerApi")}ACTION(e){return e.call(this)}consume(e,t,r){return this.consumeInternal(t,e,r)}subrule(e,t,r){return this.subruleInternal(t,e,r)}option(e,t){return this.optionInternal(t,e)}or(e,t){return this.orInternal(t,e)}many(e,t){return this.manyInternal(e,t)}atLeastOne(e,t){return this.atLeastOneInternal(e,t)}CONSUME(e,t){return this.consumeInternal(e,0,t)}CONSUME1(e,t){return this.consumeInternal(e,1,t)}CONSUME2(e,t){return this.consumeInternal(e,2,t)}CONSUME3(e,t){return this.consumeInternal(e,3,t)}CONSUME4(e,t){return this.consumeInternal(e,4,t)}CONSUME5(e,t){return this.consumeInternal(e,5,t)}CONSUME6(e,t){return this.consumeInternal(e,6,t)}CONSUME7(e,t){return this.consumeInternal(e,7,t)}CONSUME8(e,t){return this.consumeInternal(e,8,t)}CONSUME9(e,t){return this.consumeInternal(e,9,t)}SUBRULE(e,t){return this.subruleInternal(e,0,t)}SUBRULE1(e,t){return this.subruleInternal(e,1,t)}SUBRULE2(e,t){return this.subruleInternal(e,2,t)}SUBRULE3(e,t){return this.subruleInternal(e,3,t)}SUBRULE4(e,t){return this.subruleInternal(e,4,t)}SUBRULE5(e,t){return this.subruleInternal(e,5,t)}SUBRULE6(e,t){return this.subruleInternal(e,6,t)}SUBRULE7(e,t){return this.subruleInternal(e,7,t)}SUBRULE8(e,t){return this.subruleInternal(e,8,t)}SUBRULE9(e,t){return this.subruleInternal(e,9,t)}OPTION(e){return this.optionInternal(e,0)}OPTION1(e){return this.optionInternal(e,1)}OPTION2(e){return this.optionInternal(e,2)}OPTION3(e){return this.optionInternal(e,3)}OPTION4(e){return this.optionInternal(e,4)}OPTION5(e){return this.optionInternal(e,5)}OPTION6(e){return this.optionInternal(e,6)}OPTION7(e){return this.optionInternal(e,7)}OPTION8(e){return this.optionInternal(e,8)}OPTION9(e){return this.optionInternal(e,9)}OR(e){return this.orInternal(e,0)}OR1(e){return this.orInternal(e,1)}OR2(e){return this.orInternal(e,2)}OR3(e){return this.orInternal(e,3)}OR4(e){return this.orInternal(e,4)}OR5(e){return this.orInternal(e,5)}OR6(e){return this.orInternal(e,6)}OR7(e){return this.orInternal(e,7)}OR8(e){return this.orInternal(e,8)}OR9(e){return this.orInternal(e,9)}MANY(e){this.manyInternal(0,e)}MANY1(e){this.manyInternal(1,e)}MANY2(e){this.manyInternal(2,e)}MANY3(e){this.manyInternal(3,e)}MANY4(e){this.manyInternal(4,e)}MANY5(e){this.manyInternal(5,e)}MANY6(e){this.manyInternal(6,e)}MANY7(e){this.manyInternal(7,e)}MANY8(e){this.manyInternal(8,e)}MANY9(e){this.manyInternal(9,e)}MANY_SEP(e){this.manySepFirstInternal(0,e)}MANY_SEP1(e){this.manySepFirstInternal(1,e)}MANY_SEP2(e){this.manySepFirstInternal(2,e)}MANY_SEP3(e){this.manySepFirstInternal(3,e)}MANY_SEP4(e){this.manySepFirstInternal(4,e)}MANY_SEP5(e){this.manySepFirstInternal(5,e)}MANY_SEP6(e){this.manySepFirstInternal(6,e)}MANY_SEP7(e){this.manySepFirstInternal(7,e)}MANY_SEP8(e){this.manySepFirstInternal(8,e)}MANY_SEP9(e){this.manySepFirstInternal(9,e)}AT_LEAST_ONE(e){this.atLeastOneInternal(0,e)}AT_LEAST_ONE1(e){return this.atLeastOneInternal(1,e)}AT_LEAST_ONE2(e){this.atLeastOneInternal(2,e)}AT_LEAST_ONE3(e){this.atLeastOneInternal(3,e)}AT_LEAST_ONE4(e){this.atLeastOneInternal(4,e)}AT_LEAST_ONE5(e){this.atLeastOneInternal(5,e)}AT_LEAST_ONE6(e){this.atLeastOneInternal(6,e)}AT_LEAST_ONE7(e){this.atLeastOneInternal(7,e)}AT_LEAST_ONE8(e){this.atLeastOneInternal(8,e)}AT_LEAST_ONE9(e){this.atLeastOneInternal(9,e)}AT_LEAST_ONE_SEP(e){this.atLeastOneSepFirstInternal(0,e)}AT_LEAST_ONE_SEP1(e){this.atLeastOneSepFirstInternal(1,e)}AT_LEAST_ONE_SEP2(e){this.atLeastOneSepFirstInternal(2,e)}AT_LEAST_ONE_SEP3(e){this.atLeastOneSepFirstInternal(3,e)}AT_LEAST_ONE_SEP4(e){this.atLeastOneSepFirstInternal(4,e)}AT_LEAST_ONE_SEP5(e){this.atLeastOneSepFirstInternal(5,e)}AT_LEAST_ONE_SEP6(e){this.atLeastOneSepFirstInternal(6,e)}AT_LEAST_ONE_SEP7(e){this.atLeastOneSepFirstInternal(7,e)}AT_LEAST_ONE_SEP8(e){this.atLeastOneSepFirstInternal(8,e)}AT_LEAST_ONE_SEP9(e){this.atLeastOneSepFirstInternal(9,e)}RULE(e,t,r=L5){if(fs(this.definedRulesNames,e)){let a={message:I0.buildDuplicateRuleNameError({topLevelRule:e,grammarName:this.className}),type:hs.DUPLICATE_RULE_NAME,ruleName:e};this.definitionErrors.push(a)}this.definedRulesNames.push(e);let i=this.defineRule(e,t,r);return this[e]=i,i}OVERRIDE_RULE(e,t,r=L5){let i=rke(e,this.definedRulesNames,this.className);this.definitionErrors=this.definitionErrors.concat(i);let n=this.defineRule(e,t,r);return this[e]=n,n}BACKTRACK(e,t){return function(){this.isBackTrackingStack.push(1);let r=this.saveRecogState();try{return e.apply(this,t),!0}catch(i){if(dT(i))return!1;throw i}finally{this.reloadRecogState(r),this.isBackTrackingStack.pop()}}}getGAstProductions(){return this.gastProductionsCache}getSerializedGastProductions(){return ZTe(Hn(this.gastProductionsCache))}},j0t=class{static{o(this,"RecognizerEngine")}static{S(this,"RecognizerEngine")}initRecognizerEngine(e,t){if(this.className=this.constructor.name,this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=hT,this.subruleIdx=0,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},mr(t,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a <serializedGrammar> property.
+	See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0
+	For Further details.`);if(Kr(e)){if(Oi(e))throw Error(`A Token Vocabulary cannot be empty.
+	Note that the first argument for the parser constructor
+	is no longer a Token vector (since v4.0).`);if(typeof e[0].startOffset=="number")throw Error(`The Parser constructor no longer accepts a token vector as the first argument.
+	See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_4-0-0
+	For Further details.`)}if(Kr(e))this.tokensMap=go(e,(n,a)=>(n[a.name]=a,n),{});else if(mr(e,"modes")&&Hl(Ul(Hn(e.modes)),zCe)){let n=Ul(Hn(e.modes)),a=Lz(n);this.tokensMap=go(a,(s,l)=>(s[l.name]=l,s),{})}else if(tl(e))this.tokensMap=ka(e);else throw new Error("<tokensDictionary> argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=gf;let r=mr(e,"modes")?Ul(Hn(e.modes)):Hn(e),i=Hl(r,n=>Oi(n.categoryMatches));this.tokenMatcher=i?hT:uy,hy(Hn(this.tokensMap))}defineRule(e,t,r){if(this.selfAnalysisDone)throw Error(`Grammar rule <${e}> may not be defined after the 'performSelfAnalysis' method has been called'
+Make sure that all grammar rule definitions are done before 'performSelfAnalysis' is called.`);let i=mr(r,"resyncEnabled")?r.resyncEnabled:L5.resyncEnabled,n=mr(r,"recoveryValueFunc")?r.recoveryValueFunc:L5.recoveryValueFunc,a=this.ruleShortNameIdx<<z0t+Cf;this.ruleShortNameIdx++,this.shortRuleNameToFull[a]=e,this.fullRuleNameToShort[e]=a;let s;return this.outputCst===!0?s=S(o(function(...h){try{this.ruleInvocationStateUpdate(a,e,this.subruleIdx),t.apply(this,h);let d=this.CST_STACK[this.CST_STACK.length-1];return this.cstPostRule(d),d}catch(d){return this.invokeRuleCatch(d,i,n)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTry2"),"invokeRuleWithTry"):s=S(o(function(...h){try{return this.ruleInvocationStateUpdate(a,e,this.subruleIdx),t.apply(this,h)}catch(d){return this.invokeRuleCatch(d,i,n)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTryCst"),"invokeRuleWithTryCst"),Object.assign(s,{ruleName:e,originalGrammarAction:t})}invokeRuleCatch(e,t,r){let i=this.RULE_STACK.length===1,n=t&&!this.isBackTracking()&&this.recoveryEnabled;if(dT(e)){let a=e;if(n){let s=this.findReSyncTokenType();if(this.isInCurrentRuleReSyncSet(s))if(a.resyncedTokens=this.reSyncTo(s),this.outputCst){let l=this.CST_STACK[this.CST_STACK.length-1];return l.recoveredNode=!0,l}else return r(e);else{if(this.outputCst){let l=this.CST_STACK[this.CST_STACK.length-1];l.recoveredNode=!0,a.partialCstResult=l}throw a}}else{if(i)return this.moveToTerminatedState(),r(e);throw a}}else throw e}optionInternal(e,t){let r=this.getKeyForAutomaticLookahead(Tke,t);return this.optionInternalLogic(e,t,r)}optionInternalLogic(e,t,r){let i=this.getLaFuncFromCache(r),n;if(typeof e!="function"){n=e.DEF;let a=e.GATE;if(a!==void 0){let s=i;i=S(()=>a.call(this)&&s.call(this),"lookAheadFunc")}}else n=e;if(i.call(this)===!0)return n.call(this)}atLeastOneInternal(e,t){let r=this.getKeyForAutomaticLookahead(DF,e);return this.atLeastOneInternalLogic(e,t,r)}atLeastOneInternalLogic(e,t,r){let i=this.getLaFuncFromCache(r),n;if(typeof t!="function"){n=t.DEF;let a=t.GATE;if(a!==void 0){let s=i;i=S(()=>a.call(this)&&s.call(this),"lookAheadFunc")}}else n=t;if(i.call(this)===!0){let a=this.doSingleRepetition(n);for(;i.call(this)===!0&&a===!0;)a=this.doSingleRepetition(n)}else throw this.raiseEarlyExitException(e,en.REPETITION_MANDATORY,t.ERR_MSG);this.attemptInRepetitionRecovery(this.atLeastOneInternal,[e,t],i,DF,e,L0t)}atLeastOneSepFirstInternal(e,t){let r=this.getKeyForAutomaticLookahead(YE,e);this.atLeastOneSepFirstInternalLogic(e,t,r)}atLeastOneSepFirstInternalLogic(e,t,r){let i=t.DEF,n=t.SEP;if(this.getLaFuncFromCache(r).call(this)===!0){i.call(this);let s=S(()=>this.tokenMatcher(this.LA(1),n),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),n)===!0;)this.CONSUME(n),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,n,s,i,lye],s,YE,e,lye)}else throw this.raiseEarlyExitException(e,en.REPETITION_MANDATORY_WITH_SEPARATOR,t.ERR_MSG)}manyInternal(e,t){let r=this.getKeyForAutomaticLookahead(_F,e);return this.manyInternalLogic(e,t,r)}manyInternalLogic(e,t,r){let i=this.getLaFuncFromCache(r),n;if(typeof t!="function"){n=t.DEF;let s=t.GATE;if(s!==void 0){let l=i;i=S(()=>s.call(this)&&l.call(this),"lookaheadFunction")}}else n=t;let a=!0;for(;i.call(this)===!0&&a===!0;)a=this.doSingleRepetition(n);this.attemptInRepetitionRecovery(this.manyInternal,[e,t],i,_F,e,R0t,a)}manySepFirstInternal(e,t){let r=this.getKeyForAutomaticLookahead(RF,e);this.manySepFirstInternalLogic(e,t,r)}manySepFirstInternalLogic(e,t,r){let i=t.DEF,n=t.SEP;if(this.getLaFuncFromCache(r).call(this)===!0){i.call(this);let s=S(()=>this.tokenMatcher(this.LA(1),n),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),n)===!0;)this.CONSUME(n),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,n,s,i,oye],s,RF,e,oye)}}repetitionSepSecondInternal(e,t,r,i,n){for(;r();)this.CONSUME(t),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,t,r,i,n],r,YE,e,n)}doSingleRepetition(e){let t=this.getLexerPosition();return e.call(this),this.getLexerPosition()>t}orInternal(e,t){let r=this.getKeyForAutomaticLookahead(bke,t),i=Kr(e)?e:e.DEF,a=this.getLaFuncFromCache(r).call(this,i);if(a!==void 0)return i[a].ALT.call(this);this.raiseNoAltException(t,e.ERR_MSG)}ruleFinallyStateUpdate(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){let e=this.LA(1),t=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new P0t(t,e))}}subruleInternal(e,t,r){let i;try{let n=r!==void 0?r.ARGS:void 0;return this.subruleIdx=t,i=e.apply(this,n),this.cstPostNonTerminal(i,r!==void 0&&r.LABEL!==void 0?r.LABEL:e.ruleName),i}catch(n){throw this.subruleInternalError(n,r,e.ruleName)}}subruleInternalError(e,t,r){throw dT(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,t!==void 0&&t.LABEL!==void 0?t.LABEL:r),delete e.partialCstResult),e}consumeInternal(e,t,r){let i;try{let n=this.LA(1);this.tokenMatcher(n,e)===!0?(this.consumeToken(),i=n):this.consumeInternalError(e,n,r)}catch(n){i=this.consumeInternalRecovery(e,t,n)}return this.cstPostTerminal(r!==void 0&&r.LABEL!==void 0?r.LABEL:e.name,i),i}consumeInternalError(e,t,r){let i,n=this.LA(0);throw r!==void 0&&r.ERR_MSG?i=r.ERR_MSG:i=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:t,previous:n,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new yke(i,t,n))}consumeInternalRecovery(e,t,r){if(this.recoveryEnabled&&r.name==="MismatchedTokenException"&&!this.isBackTracking()){let i=this.getFollowsForInRuleRecovery(e,t);try{return this.tryInRuleRecovery(e,i)}catch(n){throw n.name===vke?r:n}}else throw r}saveRecogState(){let e=this.errors,t=ka(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:t,CST_STACK:this.CST_STACK}}reloadRecogState(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK}ruleInvocationStateUpdate(e,t,r){this.RULE_OCCURRENCE_STACK.push(r),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(t)}isBackTracking(){return this.isBackTrackingStack.length!==0}getCurrRuleFullName(){let e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]}shortRuleNameToFullName(e){return this.shortRuleNameToFull[e]}isAtEndOfInput(){return this.tokenMatcher(this.LA(1),gf)}reset(){this.resetLexerState(),this.subruleIdx=0,this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]}},X0t=class{static{o(this,"ErrorHandler")}static{S(this,"ErrorHandler")}initErrorHandler(e){this._errors=[],this.errorMessageProvider=mr(e,"errorMessageProvider")?e.errorMessageProvider:ph.errorMessageProvider}SAVE_ERROR(e){if(dT(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:ka(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")}get errors(){return ka(this._errors)}set errors(e){this._errors=e}raiseEarlyExitException(e,t,r){let i=this.getCurrRuleFullName(),n=this.getGAstProductions()[i],s=FT(e,n,t,this.maxLookahead)[0],l=[];for(let h=1;h<=this.maxLookahead;h++)l.push(this.LA(h));let u=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:s,actual:l,previous:this.LA(0),customUserDescription:r,ruleName:i});throw this.SAVE_ERROR(new B0t(u,this.LA(1),this.LA(0)))}raiseNoAltException(e,t){let r=this.getCurrRuleFullName(),i=this.getGAstProductions()[r],n=BT(e,i,this.maxLookahead),a=[];for(let u=1;u<=this.maxLookahead;u++)a.push(this.LA(u));let s=this.LA(0),l=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:n,actual:a,previous:s,customUserDescription:t,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new O0t(l,this.LA(1),s))}},K0t=class{static{o(this,"ContentAssist")}static{S(this,"ContentAssist")}initContentAssist(){}computeContentAssist(e,t){let r=this.gastProductionsCache[e];if(dh(r))throw Error(`Rule ->${e}<- does not exist in this grammar.`);return Vz([r],t,this.tokenMatcher,this.maxLookahead)}getNextPossibleTokenTypes(e){let t=jl(e.ruleStack),i=this.getGAstProductions()[t];return new D0t(i,e).startWalking()}},AA={description:"This Object indicates the Parser is during Recording Phase"};Object.freeze(AA);cye=!0,uye=Math.pow(2,Cf)-1,Rke=q1({name:"RECORDING_PHASE_TOKEN",pattern:ls.NA});hy([Rke]);Lke=PT(Rke,`This IToken indicates the Parser is in Recording Phase
+	See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,-1,-1,-1,-1,-1,-1);Object.freeze(Lke);Z0t={name:`This CSTNode indicates the Parser is in Recording Phase
+	See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,children:{}},Q0t=class{static{o(this,"GastRecorder")}static{S(this,"GastRecorder")}initGastRecorder(e){this.recordingProdStack=[],this.RECORDING_PHASE=!1}enableRecording(){this.RECORDING_PHASE=!0,this.TRACE_INIT("Enable Recording",()=>{for(let e=0;e<10;e++){let t=e>0?e:"";this[`CONSUME${t}`]=function(r,i){return this.consumeInternalRecord(r,e,i)},this[`SUBRULE${t}`]=function(r,i){return this.subruleInternalRecord(r,e,i)},this[`OPTION${t}`]=function(r){return this.optionInternalRecord(r,e)},this[`OR${t}`]=function(r){return this.orInternalRecord(r,e)},this[`MANY${t}`]=function(r){this.manyInternalRecord(e,r)},this[`MANY_SEP${t}`]=function(r){this.manySepFirstInternalRecord(e,r)},this[`AT_LEAST_ONE${t}`]=function(r){this.atLeastOneInternalRecord(e,r)},this[`AT_LEAST_ONE_SEP${t}`]=function(r){this.atLeastOneSepFirstInternalRecord(e,r)}}this.consume=function(e,t,r){return this.consumeInternalRecord(t,e,r)},this.subrule=function(e,t,r){return this.subruleInternalRecord(t,e,r)},this.option=function(e,t){return this.optionInternalRecord(t,e)},this.or=function(e,t){return this.orInternalRecord(t,e)},this.many=function(e,t){this.manyInternalRecord(e,t)},this.atLeastOne=function(e,t){this.atLeastOneInternalRecord(e,t)},this.ACTION=this.ACTION_RECORD,this.BACKTRACK=this.BACKTRACK_RECORD,this.LA=this.LA_RECORD})}disableRecording(){this.RECORDING_PHASE=!1,this.TRACE_INIT("Deleting Recording methods",()=>{let e=this;for(let t=0;t<10;t++){let r=t>0?t:"";delete e[`CONSUME${r}`],delete e[`SUBRULE${r}`],delete e[`OPTION${r}`],delete e[`OR${r}`],delete e[`MANY${r}`],delete e[`MANY_SEP${r}`],delete e[`AT_LEAST_ONE${r}`],delete e[`AT_LEAST_ONE_SEP${r}`]}delete e.consume,delete e.subrule,delete e.option,delete e.or,delete e.many,delete e.atLeastOne,delete e.ACTION,delete e.BACKTRACK,delete e.LA})}ACTION_RECORD(e){}BACKTRACK_RECORD(e,t){return()=>!0}LA_RECORD(e){return R5}topLevelRuleRecord(e,t){try{let r=new oy({definition:[],name:e});return r.name=e,this.recordingProdStack.push(r),t.call(this),this.recordingProdStack.pop(),r}catch(r){if(r.KNOWN_RECORDER_ERROR!==!0)try{r.message=r.message+`
+	 This error was thrown during the "grammar recording phase" For more info see:
+	https://chevrotain.io/docs/guide/internals.html#grammar-recording`}catch{throw r}throw r}}optionInternalRecord(e,t){return L1.call(this,Ca,e,t)}atLeastOneInternalRecord(e,t){L1.call(this,yo,t,e)}atLeastOneSepFirstInternalRecord(e,t){L1.call(this,vo,t,e,cye)}manyInternalRecord(e,t){L1.call(this,hn,t,e)}manySepFirstInternalRecord(e,t){L1.call(this,qs,t,e,cye)}orInternalRecord(e,t){return Mke.call(this,e,t)}subruleInternalRecord(e,t,r){if(fT(t),!e||mr(e,"ruleName")===!1){let s=new Error(`<SUBRULE${IF(t)}> argument is invalid expecting a Parser method reference but got: <${JSON.stringify(e)}>
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n=r.concat(i);for(let a of n)this.clear(a)}))}},MG=class extends LG{static{o(this,"WorkspaceCache")}static{S(this,"WorkspaceCache")}constructor(e,t){super(),t?(this.toDispose.push(e.workspace.DocumentBuilder.onBuildPhase(t,()=>{this.clear()})),this.toDispose.push(e.workspace.DocumentBuilder.onUpdate((r,i)=>{i.length>0&&this.clear()}))):this.toDispose.push(e.workspace.DocumentBuilder.onUpdate(()=>{this.clear()}))}},k3e=class{static{o(this,"DefaultScopeProvider")}static{S(this,"DefaultScopeProvider")}constructor(e){this.reflection=e.shared.AstReflection,this.nameProvider=e.references.NameProvider,this.descriptions=e.workspace.AstNodeDescriptionProvider,this.indexManager=e.shared.workspace.IndexManager,this.globalScopeCache=new MG(e.shared)}getScope(e){let t=[],r=this.reflection.getReferenceType(e),i=Wl(e.container).localSymbols;if(i){let a=e.container;do i.has(a)&&t.push(i.getStream(a).filter(s=>this.reflection.isSubtype(s.type,r))),a=a.$container;while(a)}let 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r=t??{},i=t?.replacer,n=S((s,l)=>this.replacer(s,l,r),"defaultReplacer"),a=i?(s,l)=>i(s,l,n):n;try{return this.currentDocument=Wl(e),JSON.stringify(e,a,t?.space)}finally{this.currentDocument=void 0}}deserialize(e,t){let r=t??{},i=JSON.parse(e);return this.linkNode(i,i,r),i}replacer(e,t,{refText:r,sourceText:i,textRegions:n,comments:a,uriConverter:s}){if(!this.ignoreProperties.has(e))if(ss(t)){let l=t.ref,u=r?t.$refText:void 0;if(l){let h=Wl(l),d="";this.currentDocument&&this.currentDocument!==h&&(s?d=s(h.uri,l):d=h.uri.toString());let f=this.astNodeLocator.getAstNodePath(l);return{$ref:`${d}#${f}`,$refText:u}}else return{$error:t.error?.message??"Could not resolve reference",$refText:u}}else if(Qc(t)){let l=r?t.$refText:void 0,u=[];for(let h of t.items){let d=h.ref,f=Wl(h.ref),p="";this.currentDocument&&this.currentDocument!==f&&(s?p=s(f.uri,d):p=f.uri.toString());let m=this.astNodeLocator.getAstNodePath(d);u.push(`${p}#${m}`)}return{$refs:u,$refText:l}}else if(Rn(t)){let l;if(n&&(l=this.addAstNodeRegionWithAssignmentsTo({...t}),(!e||t.$document)&&l?.$textRegion&&(l.$textRegion.documentURI=this.currentDocument?.uri.toString())),i&&!e&&(l??(l={...t}),l.$sourceText=t.$cstNode?.text),a){l??(l={...t});let u=this.commentProvider.getComment(t);u&&(l.$comment=u.replace(/\r/g,""))}return l??t}else return t}addAstNodeRegionWithAssignmentsTo(e){let t=S(r=>({offset:r.offset,end:r.end,length:r.length,range:r.range}),"createDocumentSegment");if(e.$cstNode){let r=e.$textRegion=t(e.$cstNode),i=r.assignments={};return Object.keys(e).filter(n=>!n.startsWith("$")).forEach(n=>{let a=rz(e.$cstNode,n).map(t);a.length!==0&&(i[n]=a)}),e}}linkNode(e,t,r,i,n,a){for(let[l,u]of Object.entries(e))if(Array.isArray(u))for(let h=0;h<u.length;h++){let d=u[h];HF(d)?u[h]=this.reviveReference(e,l,t,d,r):Rn(d)&&this.linkNode(d,t,r,e,l,h)}else HF(u)?e[l]=this.reviveReference(e,l,t,u,r):Rn(u)&&this.linkNode(u,t,r,e,l);let s=e;s.$container=i,s.$containerProperty=n,s.$containerIndex=a}reviveReference(e,t,r,i,n){let a=i.$refText,s=i.$error,l;if(i.$ref){let u=this.getRefNode(r,i.$ref,n.uriConverter);if(Rn(u))return a||(a=this.nameProvider.getName(u)),{$refText:a??"",ref:u};s=u}else if(i.$refs){let u=[];for(let h of i.$refs){let d=this.getRefNode(r,h,n.uriConverter);Rn(d)&&u.push({ref:d})}if(u.length===0)l={$refText:a??"",items:u},s??(s="Could not resolve multi-reference");else return{$refText:a??"",items:u}}if(s)return l??(l={$refText:a??"",ref:void 0}),l.error={info:{container:e,property:t,reference:l},message:s},l}getRefNode(e,t,r){try{let i=t.indexOf("#");if(i===0){let l=this.astNodeLocator.getAstNode(e,t.substring(1));return l||"Could not resolve path: "+t}if(i<0){let l=r?r(t):fo.parse(t),u=this.langiumDocuments.getDocument(l);return u?u.parseResult.value:"Could not find document for URI: "+t}let n=r?r(t.substring(0,i)):fo.parse(t.substring(0,i)),a=this.langiumDocuments.getDocument(n);if(!a)return"Could not find document for URI: "+t;if(i===t.length-1)return a.parseResult.value;let s=this.astNodeLocator.getAstNode(a.parseResult.value,t.substring(i+1));return s||"Could not resolve URI: "+t}catch(i){return String(i)}}},S3e=class{static{o(this,"DefaultServiceRegistry")}static{S(this,"DefaultServiceRegistry")}get map(){return this.fileExtensionMap}constructor(e){this.languageIdMap=new Map,this.fileExtensionMap=new Map,this.fileNameMap=new Map,this.textDocuments=e?.workspace.TextDocuments}register(e){let t=e.LanguageMetaData;for(let r of t.fileExtensions)this.fileExtensionMap.has(r)&&console.warn(`The file extension ${r} is used by multiple languages. It is now assigned to '${t.languageId}'.`),this.fileExtensionMap.set(r,e);if(t.fileNames)for(let r of t.fileNames)this.fileNameMap.has(r)&&console.warn(`The file name ${r} is used by multiple languages. It is now assigned to '${t.languageId}'.`),this.fileNameMap.set(r,e);this.languageIdMap.set(t.languageId,e)}getServices(e){if(this.languageIdMap.size===0)throw new Error("The service registry is empty. Use `register` to register the services of a language.");let t=this.textDocuments?.get(e)?.languageId;if(t!==void 0){let a=this.languageIdMap.get(t);if(a)return a}let r=os.extname(e),i=os.basename(e),n=this.fileNameMap.get(i)??this.fileExtensionMap.get(r);if(!n)throw t?new Error(`The service registry contains no services for the extension '${r}' for language '${t}'.`):new Error(`The service registry contains no services for the extension '${r}'.`);return n}hasServices(e){try{return this.getServices(e),!0}catch{return!1}}get all(){return Array.from(this.languageIdMap.values())}};o(O0,"diagnosticData");S(O0,"diagnosticData");(function(e){e.defaults=["fast","slow","built-in"],e.all=e.defaults})(z5||(z5={}));E3e=class{static{o(this,"ValidationRegistry")}static{S(this,"ValidationRegistry")}constructor(e){this.entries=new gh,this.knownCategories=new Set(z5.defaults),this.entriesBefore=[],this.entriesAfter=[],this.reflection=e.shared.AstReflection}register(e,t=this,r="fast"){if(r==="built-in")throw new Error("The 'built-in' category is reserved for lexer, parser, and linker errors.");this.knownCategories.add(r);for(let[i,n]of Object.entries(e)){let a=n;if(Array.isArray(a))for(let s of a){let l={check:this.wrapValidationException(s,t),category:r};this.addEntry(i,l)}else if(typeof a=="function"){let s={check:this.wrapValidationException(a,t),category:r};this.addEntry(i,s)}else bf(a)}}wrapValidationException(e,t){return async(r,i,n)=>{await this.handleException(()=>e.call(t,r,i,n),"An error occurred during validation",i,r)}}async handleException(e,t,r,i){try{await e()}catch(n){if(cm(n))throw n;console.error(`${t}:`,n),n instanceof Error&&n.stack&&console.error(n.stack);let a=n instanceof Error?n.message:String(n);r("error",`${t}: ${a}`,{node:i})}}addEntry(e,t){if(e==="AstNode"){this.entries.add("AstNode",t);return}for(let r of 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a}},L3e=class{static{o(this,"DefaultAstNodeLocator")}static{S(this,"DefaultAstNodeLocator")}constructor(){this.segmentSeparator="/",this.indexSeparator="@"}getAstNodePath(e){if(e.$container){let t=this.getAstNodePath(e.$container),r=this.getPathSegment(e);return t+this.segmentSeparator+r}return""}getPathSegment({$containerProperty:e,$containerIndex:t}){if(!e)throw new Error("Missing '$containerProperty' in AST node.");return t!==void 0?e+this.indexSeparator+t:e}getAstNode(e,t){return t.split(this.segmentSeparator).reduce((i,n)=>{if(!i||n.length===0)return i;let a=n.indexOf(this.indexSeparator);if(a>0){let s=n.substring(0,a),l=parseInt(n.substring(a+1));return i[s]?.[l]}return i[n]},e)}},XA={};W5(XA,c$(ny(),1));M3e=class{static{o(this,"DefaultConfigurationProvider")}static{S(this,"DefaultConfigurationProvider")}constructor(e){this._ready=new mh,this.onConfigurationSectionUpdateEmitter=new XA.Emitter,this.settings={},this.workspaceConfig=!1,this.serviceRegistry=e.ServiceRegistry}get ready(){return this._ready.promise}initialize(e){this.workspaceConfig=e.capabilities.workspace?.configuration??!1}async initialized(e){if(this.workspaceConfig){if(e.register){let t=this.serviceRegistry.all;e.register({section:t.map(r=>this.toSectionName(r.LanguageMetaData.languageId))})}if(e.fetchConfiguration){let t=this.serviceRegistry.all.map(i=>({section:this.toSectionName(i.LanguageMetaData.languageId)})),r=await e.fetchConfiguration(t);t.forEach((i,n)=>{this.updateSectionConfiguration(i.section,r[n])})}}this._ready.resolve()}updateConfiguration(e){typeof e.settings!="object"||e.settings===null||Object.entries(e.settings).forEach(([t,r])=>{this.updateSectionConfiguration(t,r),this.onConfigurationSectionUpdateEmitter.fire({section:t,configuration:r})})}updateSectionConfiguration(e,t){this.settings[e]=t}async getConfiguration(e,t){await this.ready;let r=this.toSectionName(e);if(this.settings[r])return this.settings[r][t]}toSectionName(e){return`${e}`}get onConfigurationSectionUpdate(){return this.onConfigurationSectionUpdateEmitter.event}},bE=c$(jlt(),1);(function(e){function t(r){return{dispose:S(async()=>await r(),"dispose")}}o(t,"create"),S(t,"create"),e.create=t})(F0||(F0={}));N3e=class{static{o(this,"DefaultDocumentBuilder")}static{S(this,"DefaultDocumentBuilder")}constructor(e){this.updateBuildOptions={validation:{categories:["built-in","fast"]}},this.updateListeners=[],this.buildPhaseListeners=new gh,this.documentPhaseListeners=new gh,this.buildState=new Map,this.documentBuildWaiters=new Map,this.currentState=zr.Changed,this.langiumDocuments=e.workspace.LangiumDocuments,this.langiumDocumentFactory=e.workspace.LangiumDocumentFactory,this.textDocuments=e.workspace.TextDocuments,this.indexManager=e.workspace.IndexManager,this.fileSystemProvider=e.workspace.FileSystemProvider,this.workspaceManager=()=>e.workspace.WorkspaceManager,this.serviceRegistry=e.ServiceRegistry}async build(e,t={},r=Ii.CancellationToken.None){for(let i of 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t=this.updateListeners.indexOf(e);t>=0&&this.updateListeners.splice(t,1)})}resetToState(e,t){switch(t){case zr.Changed:case zr.Parsed:this.indexManager.removeContent(e.uri);case zr.IndexedContent:e.localSymbols=void 0;case zr.ComputedScopes:this.serviceRegistry.getServices(e.uri).references.Linker.unlink(e);case zr.Linked:this.indexManager.removeReferences(e.uri);case zr.IndexedReferences:e.diagnostics=void 0,this.buildState.delete(e.uri.toString());case zr.Validated:}e.state>t&&(e.state=t)}cleanUpDeleted(e){this.buildState.delete(e.uri.toString()),this.indexManager.remove(e.uri),e.state=zr.Changed}async buildDocuments(e,t,r){this.prepareBuild(e,t),await this.runCancelable(e,zr.Parsed,r,a=>this.langiumDocumentFactory.update(a,r)),await this.runCancelable(e,zr.IndexedContent,r,a=>this.indexManager.updateContent(a,r)),await this.runCancelable(e,zr.ComputedScopes,r,async a=>{let s=this.serviceRegistry.getServices(a.uri).references.ScopeComputation;a.localSymbols=await 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Returning undefined.`))}else return Promise.reject(new bE.ResponseError(bE.LSPErrorCodes.ServerCancelled,`No document found for URI: ${t.toString()}`));return new Promise((n,a)=>{let s=this.onDocumentPhase(e,u=>{os.equals(u.uri,t)&&(s.dispose(),l.dispose(),n(u.uri))}),l=r.onCancellationRequested(()=>{s.dispose(),l.dispose(),a(Zc)})})}awaitBuilderState(e,t){return this.currentState>=e?Promise.resolve():t.isCancellationRequested?Promise.reject(Zc):new Promise((r,i)=>{let n=this.onBuildPhase(e,()=>{n.dispose(),a.dispose(),r()}),a=t.onCancellationRequested(()=>{n.dispose(),a.dispose(),i(Zc)})})}async notifyDocumentPhase(e,t,r){let n=this.documentPhaseListeners.get(t).slice();for(let a of n)try{await la(r),await a(e,r)}catch(s){if(!cm(s))throw s}}async notifyBuildPhase(e,t,r){if(e.length===0)return;let n=this.buildPhaseListeners.get(t).slice();for(let a of n)await la(r),await a(e,r)}shouldLink(e){return 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+
+  .branch {
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+
+  .commit-merge {
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+    fill: ${i?e.mainBkg:e.primaryColor};
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+  .commit-reverse {
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+    fill: ${i?e.mainBkg:e.primaryColor};
+    stroke-width: ${i?e.strokeWidth:3};
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+  }
+  .commit-highlight-inner {
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+    fill: ${i?e.mainBkg:e.primaryColor};
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+
+  .arrow {
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+    stroke-linecap: round;
+    fill: none
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w.add(C.years()*k,"y").add(C.months()*k,"M").add(C.days()*k,"d").add(C.hours()*k,"h").add(C.minutes()*k,"m").add(C.seconds()*k,"s").add(C.milliseconds()*k,"ms")},"p");return function(w,C,k){e=k,t=k().$utils(),k.duration=function(N,P){var I=k.locale();return p(N,{$l:I},P)},k.isDuration=f;var E=C.prototype.add,A=C.prototype.subtract;C.prototype.add=function(N,P){return f(N)?T(this,N,1):E.bind(this)(N,P)},C.prototype.subtract=function(N,P){return f(N)?T(this,N,-1):A.bind(this)(N,P)}}}))});var yy,M5e,i2t,L5e,n2t,kh,$V,a2t,N5e,I5e=z(()=>{"use strict";yy=ys(wC(),1),M5e=ys(R5e(),1);St();Rr();Ir();Ut();Di();yy.default.extend(M5e.default);i2t=o(function(){Z.debug("Something is calling, setConf, remove the call")},"setConf"),L5e={monday:Qh,tuesday:fw,wednesday:pw,thursday:vc,friday:mw,saturday:gw,sunday:wl},n2t=o((e,t)=>{let r=[...e].map(()=>-1/0),i=[...e].sort((a,s)=>a.startTime-s.startTime||a.order-s.order),n=0;for(let a of i)for(let 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+  .mermaid-main-font {
+        font-family: ${e.fontFamily};
+  }
+
+  .exclude-range {
+    fill: ${e.excludeBkgColor};
+  }
+
+  .section {
+    stroke: none;
+    opacity: 0.2;
+  }
+
+  .section0 {
+    fill: ${e.sectionBkgColor};
+  }
+
+  .section2 {
+    fill: ${e.sectionBkgColor2};
+  }
+
+  .section1,
+  .section3 {
+    fill: ${e.altSectionBkgColor};
+    opacity: 0.2;
+  }
+
+  .sectionTitle0 {
+    fill: ${e.titleColor};
+  }
+
+  .sectionTitle1 {
+    fill: ${e.titleColor};
+  }
+
+  .sectionTitle2 {
+    fill: ${e.titleColor};
+  }
+
+  .sectionTitle3 {
+    fill: ${e.titleColor};
+  }
+
+  .sectionTitle {
+    text-anchor: start;
+    font-family: ${e.fontFamily};
+  }
+
+
+  /* Grid and axis */
+
+  .grid .tick {
+    stroke: ${e.gridColor};
+    opacity: 0.8;
+    shape-rendering: crispEdges;
+  }
+
+  .grid .tick text {
+    font-family: ${e.fontFamily};
+    fill: ${e.textColor};
+  }
+
+  .grid path {
+    stroke-width: 0;
+  }
+
+
+  /* Today line */
+
+  .today {
+    fill: none;
+    stroke: ${e.todayLineColor};
+    stroke-width: 2px;
+  }
+
+
+  /* Task styling */
+
+  /* Default task */
+
+  .task {
+    stroke-width: 2;
+  }
+
+  .taskText {
+    text-anchor: middle;
+    font-family: ${e.fontFamily};
+  }
+
+  .taskTextOutsideRight {
+    fill: ${e.taskTextDarkColor};
+    text-anchor: start;
+    font-family: ${e.fontFamily};
+  }
+
+  .taskTextOutsideLeft {
+    fill: ${e.taskTextDarkColor};
+    text-anchor: end;
+  }
+
+
+  /* Special case clickable */
+
+  .task.clickable {
+    cursor: pointer;
+  }
+
+  .taskText.clickable {
+    cursor: pointer;
+    fill: ${e.taskTextClickableColor} !important;
+    font-weight: bold;
+  }
+
+  .taskTextOutsideLeft.clickable {
+    cursor: pointer;
+    fill: ${e.taskTextClickableColor} !important;
+    font-weight: bold;
+  }
+
+  .taskTextOutsideRight.clickable {
+    cursor: pointer;
+    fill: ${e.taskTextClickableColor} !important;
+    font-weight: bold;
+  }
+
+
+  /* Specific task settings for the sections*/
+
+  .taskText0,
+  .taskText1,
+  .taskText2,
+  .taskText3 {
+    fill: ${e.taskTextColor};
+  }
+
+  .task0,
+  .task1,
+  .task2,
+  .task3 {
+    fill: ${e.taskBkgColor};
+    stroke: ${e.taskBorderColor};
+  }
+
+  .taskTextOutside0,
+  .taskTextOutside2
+  {
+    fill: ${e.taskTextOutsideColor};
+  }
+
+  .taskTextOutside1,
+  .taskTextOutside3 {
+    fill: ${e.taskTextOutsideColor};
+  }
+
+
+  /* Active task */
+
+  .active0,
+  .active1,
+  .active2,
+  .active3 {
+    fill: ${e.activeTaskBkgColor};
+    stroke: ${e.activeTaskBorderColor};
+  }
+
+  .activeText0,
+  .activeText1,
+  .activeText2,
+  .activeText3 {
+    fill: ${e.taskTextDarkColor} !important;
+  }
+
+
+  /* Completed task */
+
+  .done0,
+  .done1,
+  .done2,
+  .done3 {
+    stroke: ${e.doneTaskBorderColor};
+    fill: ${e.doneTaskBkgColor};
+    stroke-width: 2;
+  }
+
+  .doneText0,
+  .doneText1,
+  .doneText2,
+  .doneText3 {
+    fill: ${e.taskTextDarkColor} !important;
+  }
+
+  /* Done task text displayed outside the bar sits against the diagram background,
+     not against the done-task bar, so it must use the outside/contrast color. */
+  .doneText0.taskTextOutsideLeft,
+  .doneText0.taskTextOutsideRight,
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+
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+  }
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fe=new Error(ve);throw fe.hash=ie,fe}},"parseError"),parse:o(function(ve){var ie=this,fe=[0],W=[],ce=[null],K=[],Re=this.table,xe="",Oe=0,be=0,Be=0,Ae=2,Ve=1,Pe=K.slice.call(arguments,1),Ye=Object.create(this.lexer),le={yy:{}};for(var st in this.yy)Object.prototype.hasOwnProperty.call(this.yy,st)&&(le.yy[st]=this.yy[st]);Ye.setInput(ve,le.yy),le.yy.lexer=Ye,le.yy.parser=this,typeof Ye.yylloc>"u"&&(Ye.yylloc={});var me=Ye.yylloc;K.push(me);var ot=Ye.options&&Ye.options.ranges;typeof le.yy.parseError=="function"?this.parseError=le.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function kt(Rt){fe.length=fe.length-2*Rt,ce.length=ce.length-Rt,K.length=K.length-Rt}o(kt,"popStack");function Gt(){var Rt;return Rt=W.pop()||Ye.lex()||Ve,typeof Rt!="number"&&(Rt instanceof Array&&(W=Rt,Rt=W.pop()),Rt=ie.symbols_[Rt]||Rt),Rt}o(Gt,"lex");for(var Tt,Et,yt,oe,ht,gt,et={},pe,Qe,nt,dt;;){if(yt=fe[fe.length-1],this.defaultActions[yt]?oe=this.defaultActions[yt]:((Tt===null||typeof Tt>"u")&&(Tt=Gt()),oe=Re[yt]&&Re[yt][Tt]),typeof oe>"u"||!oe.length||!oe[0]){var Ft="";dt=[];for(pe in Re[yt])this.terminals_[pe]&&pe>Ae&&dt.push("'"+this.terminals_[pe]+"'");Ye.showPosition?Ft="Parse error on line "+(Oe+1)+`:
+`+Ye.showPosition()+`
+Expecting `+dt.join(", ")+", got '"+(this.terminals_[Tt]||Tt)+"'":Ft="Parse error on line "+(Oe+1)+": Unexpected "+(Tt==Ve?"end of input":"'"+(this.terminals_[Tt]||Tt)+"'"),this.parseError(Ft,{text:Ye.match,token:this.terminals_[Tt]||Tt,line:Ye.yylineno,loc:me,expected:dt})}if(oe[0]instanceof Array&&oe.length>1)throw new Error("Parse Error: multiple actions possible at state: "+yt+", token: "+Tt);switch(oe[0]){case 1:fe.push(Tt),ce.push(Ye.yytext),K.push(Ye.yylloc),fe.push(oe[1]),Tt=null,Et?(Tt=Et,Et=null):(be=Ye.yyleng,xe=Ye.yytext,Oe=Ye.yylineno,me=Ye.yylloc,Be>0&&Be--);break;case 2:if(Qe=this.productions_[oe[1]][1],et.$=ce[ce.length-Qe],et._$={first_line:K[K.length-(Qe||1)].first_line,last_line:K[K.length-1].last_line,first_column:K[K.length-(Qe||1)].first_column,last_column:K[K.length-1].last_column},ot&&(et._$.range=[K[K.length-(Qe||1)].range[0],K[K.length-1].range[1]]),gt=this.performAction.apply(et,[xe,be,Oe,le.yy,oe[1],ce,K].concat(Pe)),typeof gt<"u")return gt;Qe&&(fe=fe.slice(0,-1*Qe*2),ce=ce.slice(0,-1*Qe),K=K.slice(0,-1*Qe)),fe.push(this.productions_[oe[1]][0]),ce.push(et.$),K.push(et._$),nt=Re[fe[fe.length-2]][fe[fe.length-1]],fe.push(nt);break;case 3:return!0}}return!0},"parse")},qe=(function(){var ye={EOF:1,parseError:o(function(ie,fe){if(this.yy.parser)this.yy.parser.parseError(ie,fe);else throw new Error(ie)},"parseError"),setInput:o(function(ve,ie){return this.yy=ie||this.yy||{},this._input=ve,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var ve=this._input[0];this.yytext+=ve,this.yyleng++,this.offset++,this.match+=ve,this.matched+=ve;var ie=ve.match(/(?:\r\n?|\n).*/g);return ie?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),ve},"input"),unput:o(function(ve){var ie=ve.length,fe=ve.split(/(?:\r\n?|\n)/g);this._input=ve+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-ie),this.offset-=ie;var W=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),fe.length-1&&(this.yylineno-=fe.length-1);var ce=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:fe?(fe.length===W.length?this.yylloc.first_column:0)+W[W.length-fe.length].length-fe[0].length:this.yylloc.first_column-ie},this.options.ranges&&(this.yylloc.range=[ce[0],ce[0]+this.yyleng-ie]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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this.begin("style"),77;break;case 50:return 75;case 51:return 81;case 52:return 88;case 53:return"PERCENT";case 54:return 86;case 55:return 84;case 56:break;case 57:this.begin("string");break;case 58:this.popState();break;case 59:return this.begin("style"),72;break;case 60:return this.begin("style"),74;break;case 61:return 61;case 62:return 64;case 63:return 63;case 64:this.begin("string");break;case 65:this.popState();break;case 66:return"qString";case 67:return fe.yytext=fe.yytext.trim(),89;break;case 68:return 75;case 69:return 80;case 70:return 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this.direction}setDirection(t){this.direction=t}resetLatestRequirement(){this.latestRequirement=this.getInitialRequirement()}resetLatestElement(){this.latestElement=this.getInitialElement()}getInitialRequirement(){return{requirementId:"",text:"",risk:"",verifyMethod:"",name:"",type:"",cssStyles:[],classes:["default"]}}getInitialElement(){return{name:"",type:"",docRef:"",cssStyles:[],classes:["default"]}}addRequirement(t,r){return this.requirements.has(t)||this.requirements.set(t,{name:t,type:r,requirementId:this.latestRequirement.requirementId,text:this.latestRequirement.text,risk:this.latestRequirement.risk,verifyMethod:this.latestRequirement.verifyMethod,cssStyles:[],classes:["default"]}),this.resetLatestRequirement(),this.requirements.get(t)}getRequirements(){return this.requirements}setNewReqId(t){this.latestRequirement!==void 0&&(this.latestRequirement.requirementId=t)}setNewReqText(t){this.latestRequirement!==void 0&&(this.latestRequirement.text=t)}setNewReqRisk(t){this.latestRequirement!==void 0&&(this.latestRequirement.risk=t)}setNewReqVerifyMethod(t){this.latestRequirement!==void 0&&(this.latestRequirement.verifyMethod=t)}addElement(t){return this.elements.has(t)||(this.elements.set(t,{name:t,type:this.latestElement.type,docRef:this.latestElement.docRef,cssStyles:[],classes:["default"]}),Z.info("Added new element: ",t)),this.resetLatestElement(),this.elements.get(t)}getElements(){return this.elements}setNewElementType(t){this.latestElement!==void 0&&(this.latestElement.type=t)}setNewElementDocRef(t){this.latestElement!==void 0&&(this.latestElement.docRef=t)}addRelationship(t,r,i){this.relations.push({type:t,src:r,dst:i})}getRelationships(){return this.relations}clear(){this.relations=[],this.resetLatestRequirement(),this.requirements=new Map,this.resetLatestElement(),this.elements=new Map,this.classes=new Map,gr()}setCssStyle(t,r){for(let i of t){let n=this.requirements.get(i)??this.elements.get(i);if(!r||!n)return;for(let a of r)a.includes(",")?n.cssStyles.push(...a.split(",")):n.cssStyles.push(a)}}setClass(t,r){for(let i of t){let n=this.requirements.get(i)??this.elements.get(i);if(n)for(let a of r){n.classes.push(a);let s=this.classes.get(a)?.styles;s&&n.cssStyles.push(...s)}}}defineClass(t,r){for(let i of t){let n=this.classes.get(i);n===void 0&&(n={id:i,styles:[],textStyles:[]},this.classes.set(i,n)),r&&r.forEach(function(a){if(/color/.exec(a)){let s=a.replace("fill","bgFill");n.textStyles.push(s)}n.styles.push(a)}),this.requirements.forEach(a=>{a.classes.includes(i)&&a.cssStyles.push(...r.flatMap(s=>s.split(",")))}),this.elements.forEach(a=>{a.classes.includes(i)&&a.cssStyles.push(...r.flatMap(s=>s.split(",")))})}}getClasses(){return this.classes}getData(){let t=ge(),r=[],i=[];for(let n of this.requirements.values()){let a=n;a.id=n.name,a.cssStyles=n.cssStyles,a.cssClasses=n.classes.join(" "),a.shape="requirementBox",a.look=t.look,a.colorIndex=r.length,r.push(a)}for(let n of this.elements.values()){let a=n;a.shape="requirementBox",a.look=t.look,a.id=n.name,a.cssStyles=n.cssStyles,a.cssClasses=n.classes.join(" "),a.colorIndex=r.length,r.push(a)}for(let n of this.relations){let a=0,s=n.type===this.Relationships.CONTAINS,l={id:`${n.src}-${n.dst}-${a}`,start:this.requirements.get(n.src)?.name??this.elements.get(n.src)?.name,end:this.requirements.get(n.dst)?.name??this.elements.get(n.dst)?.name,label:`&lt;&lt;${n.type}&gt;&gt;`,classes:"relationshipLine",style:["fill:none",s?"":"stroke-dasharray: 10,7"],labelpos:"c",thickness:"normal",type:"normal",pattern:s?"normal":"dashed",arrowTypeStart:s?"requirement_contains":"",arrowTypeEnd:s?"":"requirement_arrow",look:t.look,labelType:"markdown"};i.push(l),a++}return{nodes:r,edges:i,other:{},config:t,direction:this.getDirection()}}}});var gTt,yTt,QAe,JAe=z(()=>{"use strict";br();gTt=o(e=>{let 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+
+    [data-look="${i}"][data-color-id="color-${l}"].node path {
+    stroke: ${a[l]};
+    fill: ${n?.length?n[l]:""};
+    }
+
+    [data-look="${i}"][data-color-id="color-${l}"].node  rect {
+    stroke: ${a[l]};
+    fill: ${n?.length?n[l]:""};
+     }
+    `;return s},"genColor"),yTt=o(e=>{let t=Nt(),{look:r,themeVariables:i}=t,{requirementEdgeLabelBackground:n}=i;return`
+  ${gTt(e)}
+  marker {
+    fill: ${e.relationColor};
+    stroke: ${e.relationColor};
+  }
+
+  marker.cross {
+    stroke: ${e.lineColor};
+  }
+
+  svg {
+    font-family: ${e.fontFamily};
+    font-size: ${e.fontSize};
+  }
+
+  .reqBox {
+    fill: ${e.requirementBackground};
+    fill-opacity: 1.0;
+    stroke: ${e.requirementBorderColor};
+    stroke-width: ${e.requirementBorderSize};
+  }
+  
+  .reqTitle, .reqLabel{
+    fill:  ${e.requirementTextColor};
+  }
+  .reqLabelBox {
+    fill: ${e.relationLabelBackground};
+    fill-opacity: 1.0;
+  }
+
+  .req-title-line {
+    stroke: ${e.requirementBorderColor};
+    stroke-width: ${e.requirementBorderSize};
+  }
+  .relationshipLine {
+    stroke: ${e.relationColor};
+    stroke-width: ${r==="neo"?e.strokeWidth:"1px"};
+  }
+  .relationshipLabel {
+    fill: ${e.relationLabelColor};
+  }
+    .edgeLabel {
+    background-color: ${e.edgeLabelBackground};
+  }
+  .edgeLabel .label rect {
+    fill: ${e.edgeLabelBackground};
+  }
+  .edgeLabel .label text {
+    fill: ${e.relationLabelColor};
+  }
+  .divider {
+    stroke: ${e.nodeBorder};
+    stroke-width: 1;
+  }
+  .label {
+    font-family: ${e.fontFamily};
+    color: ${e.nodeTextColor||e.textColor};
+  }
+  .label text,span {
+    fill: ${e.nodeTextColor||e.textColor};
+    color: ${e.nodeTextColor||e.textColor};
+  }
+  .labelBkg {
+    background-color: ${n??e.edgeLabelBackground};
+  }
+
+`},"getStyles"),QAe=yTt});var iW={};xr(iW,{draw:()=>vTt});var vTt,e6e=z(()=>{"use strict";Ut();St();Lp();tf();rf();Xt();vTt=o(async function(e,t,r,i){Z.info("REF0:"),Z.info("Drawing requirement diagram (unified)",t);let{securityLevel:n,state:a,layout:s,look:l}=ge(),u=i.db.getData(),h=Fo(t,n);u.type=i.type,u.layoutAlgorithm=Gc(s),u.nodeSpacing=a?.nodeSpacing??50,u.rankSpacing=a?.rankSpacing??50,u.markers=l==="neo"?["requirement_contains_neo","requirement_arrow_neo"]:["requirement_contains","requirement_arrow"],u.diagramId=t,await Zo(u,h);let d=8;Zt.insertTitle(h,"requirementDiagramTitleText",a?.titleTopMargin??25,i.db.getDiagramTitle()),$s(h,d,"requirementDiagram",a?.useMaxWidth??!0)},"draw")});var t6e={};xr(t6e,{diagram:()=>xTt});var xTt,r6e=z(()=>{"use strict";KAe();ZAe();JAe();e6e();xTt={parser:XAe,get db(){return new V6},renderer:iW,styles:QAe}});var nW,a6e,s6e=z(()=>{"use strict";nW=(function(){var e=o(function(be,Be,Ae,Ve){for(Ae=Ae||{},Ve=be.length;Ve--;Ae[be[Ve]]=Be);return 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me=le.length-1;switch(Ye){case 3:return Pe.apply(le[me]),le[me];break;case 4:case 10:this.$=[];break;case 5:case 11:le[me-1].push(le[me]),this.$=le[me-1];break;case 6:case 7:case 12:case 13:this.$=le[me];break;case 8:case 9:case 14:this.$=[];break;case 16:le[me].type="createParticipant",this.$=le[me];break;case 17:le[me-1].unshift({type:"boxStart",boxData:Pe.parseBoxData(le[me-2])}),le[me-1].push({type:"boxEnd",boxText:le[me-2]}),this.$=le[me-1];break;case 19:this.$={type:"sequenceIndex",sequenceIndex:Number(le[me-2]),sequenceIndexStep:Number(le[me-1]),sequenceVisible:!0,signalType:Pe.LINETYPE.AUTONUMBER};break;case 20:this.$={type:"sequenceIndex",sequenceIndex:Number(le[me-1]),sequenceIndexStep:1,sequenceVisible:!0,signalType:Pe.LINETYPE.AUTONUMBER};break;case 21:this.$={type:"sequenceIndex",sequenceVisible:!1,signalType:Pe.LINETYPE.AUTONUMBER};break;case 22:this.$={type:"sequenceIndex",sequenceVisible:!0,signalType:Pe.LINETYPE.AUTONUMBER};break;case 23:this.$={type:"activeStart",signalType:Pe.LINETYPE.ACTIVE_START,actor:le[me-1].actor};break;case 24:this.$={type:"activeEnd",signalType:Pe.LINETYPE.ACTIVE_END,actor:le[me-1].actor};break;case 30:Pe.setDiagramTitle(le[me].substring(6)),this.$=le[me].substring(6);break;case 31:Pe.setDiagramTitle(le[me].substring(7)),this.$=le[me].substring(7);break;case 32:this.$=le[me].trim(),Pe.setAccTitle(this.$);break;case 33:case 34:this.$=le[me].trim(),Pe.setAccDescription(this.$);break;case 35:le[me-1].unshift({type:"loopStart",loopText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.LOOP_START}),le[me-1].push({type:"loopEnd",loopText:le[me-2],signalType:Pe.LINETYPE.LOOP_END}),this.$=le[me-1];break;case 36:le[me-1].unshift({type:"rectStart",color:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.RECT_START}),le[me-1].push({type:"rectEnd",color:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.RECT_END}),this.$=le[me-1];break;case 37:le[me-1].unshift({type:"optStart",optText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.OPT_START}),le[me-1].push({type:"optEnd",optText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.OPT_END}),this.$=le[me-1];break;case 38:le[me-1].unshift({type:"altStart",altText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.ALT_START}),le[me-1].push({type:"altEnd",signalType:Pe.LINETYPE.ALT_END}),this.$=le[me-1];break;case 39:le[me-1].unshift({type:"parStart",parText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.PAR_START}),le[me-1].push({type:"parEnd",signalType:Pe.LINETYPE.PAR_END}),this.$=le[me-1];break;case 40:le[me-1].unshift({type:"parStart",parText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.PAR_OVER_START}),le[me-1].push({type:"parEnd",signalType:Pe.LINETYPE.PAR_END}),this.$=le[me-1];break;case 41:le[me-1].unshift({type:"criticalStart",criticalText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.CRITICAL_START}),le[me-1].push({type:"criticalEnd",signalType:Pe.LINETYPE.CRITICAL_END}),this.$=le[me-1];break;case 42:le[me-1].unshift({type:"breakStart",breakText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.BREAK_START}),le[me-1].push({type:"breakEnd",optText:Pe.parseMessage(le[me-2]),signalType:Pe.LINETYPE.BREAK_END}),this.$=le[me-1];break;case 44:this.$=le[me-3].concat([{type:"option",optionText:Pe.parseMessage(le[me-1]),signalType:Pe.LINETYPE.CRITICAL_OPTION},le[me]]);break;case 46:this.$=le[me-3].concat([{type:"and",parText:Pe.parseMessage(le[me-1]),signalType:Pe.LINETYPE.PAR_AND},le[me]]);break;case 48:this.$=le[me-3].concat([{type:"else",altText:Pe.parseMessage(le[me-1]),signalType:Pe.LINETYPE.ALT_ELSE},le[me]]);break;case 49:le[me-3].draw="participant",le[me-3].type="addParticipant",le[me-3].description=Pe.parseMessage(le[me-1]),this.$=le[me-3];break;case 50:le[me-1].draw="participant",le[me-1].type="addParticipant",this.$=le[me-1];break;case 51:le[me-3].draw="actor",le[me-3].type="addParticipant",le[me-3].description=Pe.parseMessage(le[me-1]),this.$=le[me-3];break;case 52:case 57:le[me-1].draw="actor",le[me-1].type="addParticipant",this.$=le[me-1];break;case 53:le[me-1].type="destroyParticipant",this.$=le[me-1];break;case 54:le[me-3].draw="participant",le[me-3].type="addParticipant",le[me-3].description=Pe.parseMessage(le[me-1]),this.$=le[me-3];break;case 55:le[me-1].draw="participant",le[me-1].type="addParticipant",this.$=le[me-1];break;case 56:le[me-3].draw="actor",le[me-3].type="addParticipant",le[me-3].description=Pe.parseMessage(le[me-1]),this.$=le[me-3];break;case 58:this.$=[le[me-1],{type:"addNote",placement:le[me-2],actor:le[me-1].actor,text:le[me]}];break;case 59:le[me-2]=[].concat(le[me-1],le[me-1]).slice(0,2),le[me-2][0]=le[me-2][0].actor,le[me-2][1]=le[me-2][1].actor,this.$=[le[me-1],{type:"addNote",placement:Pe.PLACEMENT.OVER,actor:le[me-2].slice(0,2),text:le[me]}];break;case 60:this.$=[le[me-1],{type:"addLinks",actor:le[me-1].actor,text:le[me]}];break;case 61:this.$=[le[me-1],{type:"addALink",actor:le[me-1].actor,text:le[me]}];break;case 62:this.$=[le[me-1],{type:"addProperties",actor:le[me-1].actor,text:le[me]}];break;case 63:this.$=[le[me-1],{type:"addDetails",actor:le[me-1].actor,text:le[me]}];break;case 66:this.$=[le[me-2],le[me]];break;case 67:this.$=le[me];break;case 68:this.$=Pe.PLACEMENT.LEFTOF;break;case 69:this.$=Pe.PLACEMENT.RIGHTOF;break;case 70:this.$=[le[me-4],le[me-1],{type:"addMessage",from:le[me-4].actor,to:le[me-1].actor,signalType:le[me-3],msg:le[me],activate:!0},{type:"activeStart",signalType:Pe.LINETYPE.ACTIVE_START,actor:le[me-1].actor}];break;case 71:this.$=[le[me-4],le[me-1],{type:"addMessage",from:le[me-4].actor,to:le[me-1].actor,signalType:le[me-3],msg:le[me]},{type:"activeEnd",signalType:Pe.LINETYPE.ACTIVE_END,actor:le[me-4].actor}];break;case 72:this.$=[le[me-4],le[me-1],{type:"addMessage",from:le[me-4].actor,to:le[me-1].actor,signalType:le[me-3],msg:le[me],activate:!0,centralConnection:Pe.LINETYPE.CENTRAL_CONNECTION},{type:"centralConnection",signalType:Pe.LINETYPE.CENTRAL_CONNECTION,actor:le[me-1].actor}];break;case 73:this.$=[le[me-4],le[me-1],{type:"addMessage",from:le[me-4].actor,to:le[me-1].actor,signalType:le[me-2],msg:le[me],activate:!1,centralConnection:Pe.LINETYPE.CENTRAL_CONNECTION_REVERSE},{type:"centralConnectionReverse",signalType:Pe.LINETYPE.CENTRAL_CONNECTION_REVERSE,actor:le[me-4].actor}];break;case 74:this.$=[le[me-5],le[me-1],{type:"addMessage",from:le[me-5].actor,to:le[me-1].actor,signalType:le[me-3],msg:le[me],activate:!0,centralConnection:Pe.LINETYPE.CENTRAL_CONNECTION_DUAL},{type:"centralConnection",signalType:Pe.LINETYPE.CENTRAL_CONNECTION,actor:le[me-1].actor},{type:"centralConnectionReverse",signalType:Pe.LINETYPE.CENTRAL_CONNECTION_REVERSE,actor:le[me-5].actor}];break;case 75:this.$=[le[me-3],le[me-1],{type:"addMessage",from:le[me-3].actor,to:le[me-1].actor,signalType:le[me-2],msg:le[me]}];break;case 76:this.$={type:"addParticipant",actor:le[me-1],config:le[me]};break;case 77:this.$=le[me-1].trim();break;case 78:this.$={type:"addParticipant",actor:le[me]};break;case 79:this.$=Pe.LINETYPE.SOLID_OPEN;break;case 80:this.$=Pe.LINETYPE.DOTTED_OPEN;break;case 81:this.$=Pe.LINETYPE.SOLID;break;case 82:this.$=Pe.LINETYPE.SOLID_TOP;break;case 83:this.$=Pe.LINETYPE.SOLID_BOTTOM;break;case 84:this.$=Pe.LINETYPE.STICK_TOP;break;case 85:this.$=Pe.LINETYPE.STICK_BOTTOM;break;case 86:this.$=Pe.LINETYPE.SOLID_TOP_DOTTED;break;case 87:this.$=Pe.LINETYPE.SOLID_BOTTOM_DOTTED;break;case 88:this.$=Pe.LINETYPE.STICK_TOP_DOTTED;break;case 89:this.$=Pe.LINETYPE.STICK_BOTTOM_DOTTED;break;case 90:this.$=Pe.LINETYPE.SOLID_ARROW_TOP_REVERSE;break;case 91:this.$=Pe.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE;break;case 92:this.$=Pe.LINETYPE.STICK_ARROW_TOP_REVERSE;break;case 93:this.$=Pe.LINETYPE.STICK_ARROW_BOTTOM_REVERSE;break;case 94:this.$=Pe.LINETYPE.SOLID_ARROW_TOP_REVERSE_DOTTED;break;case 95:this.$=Pe.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE_DOTTED;break;case 96:this.$=Pe.LINETYPE.STICK_ARROW_TOP_REVERSE_DOTTED;break;case 97:this.$=Pe.LINETYPE.STICK_ARROW_BOTTOM_REVERSE_DOTTED;break;case 98:this.$=Pe.LINETYPE.BIDIRECTIONAL_SOLID;break;case 99:this.$=Pe.LINETYPE.DOTTED;break;case 100:this.$=Pe.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 101:this.$=Pe.LINETYPE.SOLID_CROSS;break;case 102:this.$=Pe.LINETYPE.DOTTED_CROSS;break;case 103:this.$=Pe.LINETYPE.SOLID_POINT;break;case 104:this.$=Pe.LINETYPE.DOTTED_POINT;break;case 105:this.$=Pe.parseMessage(le[me].trim().substring(1));break}},"anonymous"),table:[{3:1,4:t,5:r,6:i},{1:[3]},{3:5,4:t,5:r,6:i},{3:6,4:t,5:r,6:i},e([1,4,5,10,14,15,19,22,24,30,31,32,34,36,37,38,39,40,42,44,45,47,51,53,54,56,61,62,63,64,73],n,{7:7}),{1:[2,1]},{1:[2,2]},{1:[2,3],4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},e($,[2,5]),{9:48,13:13,14:u,15:h,18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},e($,[2,7]),e($,[2,8]),e($,[2,9]),e($,[2,15]),{13:49,51:P,53:I,54:D},{16:[1,50]},{5:[1,51]},{5:[1,54],20:[1,52],21:[1,53]},{23:55,73:O},{23:56,73:O},{5:[1,57]},{5:[1,58]},{5:[1,59]},{5:[1,60]},{5:[1,61]},e($,[2,30]),e($,[2,31]),{33:[1,62]},{35:[1,63]},e($,[2,34]),{16:[1,64]},{16:[1,65]},{16:[1,66]},{16:[1,67]},{16:[1,68]},{16:[1,69]},{16:[1,70]},{16:[1,71]},{23:72,55:73,73:G},{23:75,55:76,73:G},{23:77,73:O},{69:78,72:[1,79],78:F,79:V,80:H,81:j,82:U,83:Q,84:Y,85:ae,86:J,87:te,88:re,89:ee,90:Te,91:ue,92:De,93:Ie,94:Ee,95:we,96:Me,97:$e,98:de,99:rt,100:ke,101:Fe,102:He,103:at},{57:106,59:[1,107],67:[1,108],68:[1,109]},{23:110,73:O},{23:111,73:O},{23:112,73:O},{23:113,73:O},e([5,66,72,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104],qe),e($,[2,6]),e($,[2,16]),e(Ue,[2,10],{11:114}),e($,[2,18]),{5:[1,116],20:[1,115]},{5:[1,117]},e($,[2,22]),{5:[1,118]},{5:[1,119]},e($,[2,25]),e($,[2,26]),e($,[2,27]),e($,[2,28]),e($,[2,29]),e($,[2,32]),e($,[2,33]),e(ye,n,{7:120}),e(ye,n,{7:121}),e(ye,n,{7:122}),e(ve,n,{41:123,7:124}),e(ie,n,{43:125,7:126}),e(ie,n,{7:126,43:127}),e(fe,n,{46:128,7:129}),e(ye,n,{7:130}),{5:[1,132],52:[1,131]},{5:[1,134],52:[1,133]},e(W,qe,{74:135,75:[1,136]}),{5:[1,138],52:[1,137]},{5:[1,140],52:[1,139]},{5:[1,141]},{23:145,70:[1,142],71:[1,143],72:[1,144],73:O},{69:146,78:F,79:V,80:H,81:j,82:U,83:Q,84:Y,85:ae,86:J,87:te,88:re,89:ee,90:Te,91:ue,92:De,93:Ie,94:Ee,95:we,96:Me,97:$e,98:de,99:rt,100:ke,101:Fe,102:He,103:at},e(ce,[2,79]),e(ce,[2,80]),e(ce,[2,81]),e(ce,[2,82]),e(ce,[2,83]),e(ce,[2,84]),e(ce,[2,85]),e(ce,[2,86]),e(ce,[2,87]),e(ce,[2,88]),e(ce,[2,89]),e(ce,[2,90]),e(ce,[2,91]),e(ce,[2,92]),e(ce,[2,93]),e(ce,[2,94]),e(ce,[2,95]),e(ce,[2,96]),e(ce,[2,97]),e(ce,[2,98]),e(ce,[2,99]),e(ce,[2,100]),e(ce,[2,101]),e(ce,[2,102]),e(ce,[2,103]),e(ce,[2,104]),{23:147,73:O},{23:149,60:148,73:O},{73:[2,68]},{73:[2,69]},{58:150,104:K},{58:152,104:K},{58:153,104:K},{58:154,104:K},{4:[1,157],5:[1,159],12:156,13:158,17:[1,155],51:P,53:I,54:D},{5:[1,160]},e($,[2,20]),e($,[2,21]),e($,[2,23]),e($,[2,24]),{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[1,161],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[1,162],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[1,163],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{17:[1,164]},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[2,47],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,50:[1,165],51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{17:[1,166]},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[2,45],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,49:[1,167],51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{17:[1,168]},{17:[1,169]},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[2,43],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,48:[1,170],51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{4:a,5:s,8:8,9:10,10:l,13:13,14:u,15:h,17:[1,171],18:16,19:d,22:f,23:41,24:p,25:20,26:21,27:22,28:23,29:24,30:m,31:g,32:y,34:v,36:x,37:b,38:T,39:w,40:C,42:k,44:E,45:A,47:N,51:P,53:I,54:D,56:_,61:R,62:M,63:L,64:B,73:O},{16:[1,172]},e($,[2,50]),{16:[1,173]},e($,[2,55]),e(W,[2,76]),{76:[1,174]},{16:[1,175]},e($,[2,52]),{16:[1,176]},e($,[2,57]),e($,[2,53]),{23:177,73:O},{23:178,73:O},{23:179,73:O},{58:180,104:K},{23:181,72:[1,182],73:O},{58:183,104:K},{58:184,104:K},{66:[1,185],104:[2,67]},{5:[2,60]},{5:[2,105]},{5:[2,61]},{5:[2,62]},{5:[2,63]},e($,[2,17]),e(Ue,[2,11]),{13:186,51:P,53:I,54:D},e(Ue,[2,13]),e(Ue,[2,14]),e($,[2,19]),e($,[2,35]),e($,[2,36]),e($,[2,37]),e($,[2,38]),{16:[1,187]},e($,[2,39]),{16:[1,188]},e($,[2,40]),e($,[2,41]),{16:[1,189]},e($,[2,42]),{5:[1,190]},{5:[1,191]},{77:[1,192]},{5:[1,193]},{5:[1,194]},{58:195,104:K},{58:196,1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dt,Ft,Rt,$t,lr,pt,Se={},it,xt,rr,_r;;){if(Rt=Ve[Ve.length-1],this.defaultActions[Rt]?$t=this.defaultActions[Rt]:((dt===null||typeof dt>"u")&&(dt=nt()),$t=st[Rt]&&st[Rt][dt]),typeof $t>"u"||!$t.length||!$t[0]){var Ur="";_r=[];for(it in st[Rt])this.terminals_[it]&&it>Tt&&_r.push("'"+this.terminals_[it]+"'");oe.showPosition?Ur="Parse error on line "+(ot+1)+`:
+`+oe.showPosition()+`
+Expecting `+_r.join(", ")+", got '"+(this.terminals_[dt]||dt)+"'":Ur="Parse error on line "+(ot+1)+": Unexpected "+(dt==Et?"end of input":"'"+(this.terminals_[dt]||dt)+"'"),this.parseError(Ur,{text:oe.match,token:this.terminals_[dt]||dt,line:oe.yylineno,loc:et,expected:_r})}if($t[0]instanceof Array&&$t.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Rt+", token: "+dt);switch($t[0]){case 1:Ve.push(dt),Ye.push(oe.yytext),le.push(oe.yylloc),Ve.push($t[1]),dt=null,Ft?(dt=Ft,Ft=null):(kt=oe.yyleng,me=oe.yytext,ot=oe.yylineno,et=oe.yylloc,Gt>0&&Gt--);break;case 2:if(xt=this.productions_[$t[1]][1],Se.$=Ye[Ye.length-xt],Se._$={first_line:le[le.length-(xt||1)].first_line,last_line:le[le.length-1].last_line,first_column:le[le.length-(xt||1)].first_column,last_column:le[le.length-1].last_column},pe&&(Se._$.range=[le[le.length-(xt||1)].range[0],le[le.length-1].range[1]]),pt=this.performAction.apply(Se,[me,kt,ot,ht.yy,$t[1],Ye,le].concat(yt)),typeof pt<"u")return pt;xt&&(Ve=Ve.slice(0,-1*xt*2),Ye=Ye.slice(0,-1*xt),le=le.slice(0,-1*xt)),Ve.push(this.productions_[$t[1]][0]),Ye.push(Se.$),le.push(Se._$),rr=st[Ve[Ve.length-2]][Ve[Ve.length-1]],Ve.push(rr);break;case 3:return!0}}return!0},"parse")},xe=(function(){var be={EOF:1,parseError:o(function(Ae,Ve){if(this.yy.parser)this.yy.parser.parseError(Ae,Ve);else throw new Error(Ae)},"parseError"),setInput:o(function(Be,Ae){return this.yy=Ae||this.yy||{},this._input=Be,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var Be=this._input[0];this.yytext+=Be,this.yyleng++,this.offset++,this.match+=Be,this.matched+=Be;var Ae=Be.match(/(?:\r\n?|\n).*/g);return Ae?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Be},"input"),unput:o(function(Be){var Ae=Be.length,Ve=Be.split(/(?:\r\n?|\n)/g);this._input=Be+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-Ae),this.offset-=Ae;var Pe=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Ve.length-1&&(this.yylineno-=Ve.length-1);var Ye=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Ve?(Ve.length===Pe.length?this.yylloc.first_column:0)+Pe[Pe.length-Ve.length].length-Ve[0].length:this.yylloc.first_column-Ae},this.options.ranges&&(this.yylloc.range=[Ye[0],Ye[0]+this.yyleng-Ae]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(Be){this.unput(this.match.slice(Be))},"less"),pastInput:o(function(){var Be=this.matched.substr(0,this.matched.length-this.match.length);return(Be.length>20?"...":"")+Be.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var Be=this.match;return Be.length<20&&(Be+=this._input.substr(0,20-Be.length)),(Be.substr(0,20)+(Be.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var Be=this.pastInput(),Ae=new Array(Be.length+1).join("-");return Be+this.upcomingInput()+`
+`+Ae+"^"},"showPosition"),test_match:o(function(Be,Ae){var Ve,Pe,Ye;if(this.options.backtrack_lexer&&(Ye={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Ye.yylloc.range=this.yylloc.range.slice(0))),Pe=Be[0].match(/(?:\r\n?|\n).*/g),Pe&&(this.yylineno+=Pe.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:Pe?Pe[Pe.length-1].length-Pe[Pe.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+Be[0].length},this.yytext+=Be[0],this.match+=Be[0],this.matches=Be,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(Be[0].length),this.matched+=Be[0],Ve=this.performAction.call(this,this.yy,this,Ae,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Ve)return Ve;if(this._backtrack){for(var le in Ye)this[le]=Ye[le];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var Be,Ae,Ve,Pe;this._more||(this.yytext="",this.match="");for(var Ye=this._currentRules(),le=0;le<Ye.length;le++)if(Ve=this._input.match(this.rules[Ye[le]]),Ve&&(!Ae||Ve[0].length>Ae[0].length)){if(Ae=Ve,Pe=le,this.options.backtrack_lexer){if(Be=this.test_match(Ve,Ye[le]),Be!==!1)return Be;if(this._backtrack){Ae=!1;continue}else return!1}else if(!this.options.flex)break}return Ae?(Be=this.test_match(Ae,Ye[Pe]),Be!==!1?Be:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var Ae=this.next();return Ae||this.lex()},"lex"),begin:o(function(Ae){this.conditionStack.push(Ae)},"begin"),popState:o(function(){var Ae=this.conditionStack.length-1;return Ae>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(Ae){return Ae=this.conditionStack.length-1-Math.abs(Ae||0),Ae>=0?this.conditionStack[Ae]:"INITIAL"},"topState"),pushState:o(function(Ae){this.begin(Ae)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(Ae,Ve,Pe,Ye){var le=Ye;switch(Pe){case 0:return 5;case 1:break;case 2:break;case 3:break;case 4:break;case 5:break;case 6:return 20;case 7:return this.begin("CONFIG"),75;break;case 8:return 76;case 9:return this.popState(),this.begin("ALIAS"),77;break;case 10:return this.popState(),this.popState(),77;break;case 11:return Ve.yytext=Ve.yytext.trim(),73;break;case 12:return Ve.yytext=Ve.yytext.trim(),this.begin("ALIAS"),73;break;case 13:return Ve.yytext=Ve.yytext.trim(),this.popState(),73;break;case 14:return this.popState(),10;break;case 15:return Ve.yytext=Ve.yytext.trim(),this.popState(),10;break;case 16:return this.begin("LINE"),15;break;case 17:return this.begin("ID"),51;break;case 18:return this.begin("ID"),53;break;case 19:return 14;case 20:return this.begin("ID"),54;break;case 21:return this.popState(),this.popState(),this.begin("LINE"),52;break;case 22:return this.popState(),this.popState(),5;break;case 23:return this.begin("LINE"),37;break;case 24:return this.begin("LINE"),38;break;case 25:return this.begin("LINE"),39;break;case 26:return this.begin("LINE"),40;break;case 27:return this.begin("LINE"),50;break;case 28:return this.begin("LINE"),42;break;case 29:return this.begin("LINE"),44;break;case 30:return this.begin("LINE"),49;break;case 31:return this.begin("LINE"),45;break;case 32:return this.begin("LINE"),48;break;case 33:return this.begin("LINE"),47;break;case 34:return this.popState(),16;break;case 35:return 17;case 36:return 67;case 37:return 68;case 38:return 61;case 39:return 62;case 40:return 63;case 41:return 64;case 42:return 59;case 43:return 56;case 44:return this.begin("ID"),22;break;case 45:return this.begin("ID"),24;break;case 46:return 30;case 47:return 31;case 48:return this.begin("acc_title"),32;break;case 49:return this.popState(),"acc_title_value";break;case 50:return this.begin("acc_descr"),34;break;case 51:return this.popState(),"acc_descr_value";break;case 52:this.begin("acc_descr_multiline");break;case 53:this.popState();break;case 54:return"acc_descr_multiline_value";case 55:return 6;case 56:return 19;case 57:return 21;case 58:return 66;case 59:return 5;case 60:return Ve.yytext=Ve.yytext.trim(),73;break;case 61:return 80;case 62:return 97;case 63:return 98;case 64:return 99;case 65:return 78;case 66:return 79;case 67:return 100;case 68:return 101;case 69:return 102;case 70:return 103;case 71:return 85;case 72:return 86;case 73:return 87;case 74:return 88;case 75:return 93;case 76:return 94;case 77:return 95;case 78:return 96;case 79:return 81;case 80:return 82;case 81:return 83;case 82:return 84;case 83:return 89;case 84:return 90;case 85:return 91;case 86:return 92;case 87:return 104;case 88:return 104;case 89:return 70;case 90:return 71;case 91:return 72;case 92:return 5;case 93:return 10}},"anonymous"),rules:[/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:([0-9]+(\.[0-9]{1,2})?|\.[0-9]{1,2})(?=[ \n]+))/i,/^(?:@\{)/i,/^(?:[^\}]+)/i,/^(?:\}(?=\s+as\s))/i,/^(?:\})/i,/^(?:[^\<->\->:\n,;@\s]+(?=@\{))/i,/^(?:[^<>:\n,;@\s]+(?=\s+as\s))/i,/^(?:[^<>:\n,;@]+(?=\s*[\n;#]|$))/i,/^(?:[^<>:\n,;@]*<[^\n]*)/i,/^(?:[^\n]+)/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:create\b)/i,/^(?:destroy\b)/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:par_over\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\/\\\+\()\+<\->\->:\n,;]+((?!(-x|--x|-\)|--\)|-\|\\|-\\|-\/|-\/\/|-\|\/|\/\|-|\\\|-|\/\/-|\\\\-|\/\|-|--\|\\|--|\(\)))[\-]*[^\+<\->\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:<<->>)/i,/^(?:-->>)/i,/^(?:<<-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?:--\|\\)/i,/^(?:--\|\/)/i,/^(?:--\\\\)/i,/^(?:--\/\/)/i,/^(?:\/\|--)/i,/^(?:\\\|--)/i,/^(?:\/\/--)/i,/^(?:\\\\--)/i,/^(?:-\|\\)/i,/^(?:-\|\/)/i,/^(?:-\\\\)/i,/^(?:-\/\/)/i,/^(?:\/\|-)/i,/^(?:\\\|-)/i,/^(?:\/\/-)/i,/^(?:\\\\-)/i,/^(?::(?:(?:no)?wrap)?[^#\n;]*)/i,/^(?::)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:\(\))/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[53,54],inclusive:!1},acc_descr:{rules:[51],inclusive:!1},acc_title:{rules:[49],inclusive:!1},ID:{rules:[2,3,7,11,12,13,14,15],inclusive:!1},ALIAS:{rules:[2,3,21,22],inclusive:!1},LINE:{rules:[2,3,34],inclusive:!1},CONFIG:{rules:[8,9,10],inclusive:!1},CONFIG_DATA:{rules:[],inclusive:!1},INITIAL:{rules:[0,1,3,4,5,6,16,17,18,19,20,23,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,50,52,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93],inclusive:!0}}};return be})();Re.lexer=xe;function Oe(){this.yy={}}return o(Oe,"Parser"),Oe.prototype=Re,Re.Parser=Oe,new Oe})();nW.parser=nW;a6e=nW});var kTt,wTt,STt,jT,W6,aW=z(()=>{"use strict";Ut();Wx();St();T6();Ir();Ci();kTt={SOLID:0,DOTTED:1,NOTE:2,SOLID_CROSS:3,DOTTED_CROSS:4,SOLID_OPEN:5,DOTTED_OPEN:6,LOOP_START:10,LOOP_END:11,ALT_START:12,ALT_ELSE:13,ALT_END:14,OPT_START:15,OPT_END:16,ACTIVE_START:17,ACTIVE_END:18,PAR_START:19,PAR_AND:20,PAR_END:21,RECT_START:22,RECT_END:23,SOLID_POINT:24,DOTTED_POINT:25,AUTONUMBER:26,CRITICAL_START:27,CRITICAL_OPTION:28,CRITICAL_END:29,BREAK_START:30,BREAK_END:31,PAR_OVER_START:32,BIDIRECTIONAL_SOLID:33,BIDIRECTIONAL_DOTTED:34,SOLID_TOP:41,SOLID_BOTTOM:42,STICK_TOP:43,STICK_BOTTOM:44,SOLID_ARROW_TOP_REVERSE:45,SOLID_ARROW_BOTTOM_REVERSE:46,STICK_ARROW_TOP_REVERSE:47,STICK_ARROW_BOTTOM_REVERSE:48,SOLID_TOP_DOTTED:51,SOLID_BOTTOM_DOTTED:52,STICK_TOP_DOTTED:53,STICK_BOTTOM_DOTTED:54,SOLID_ARROW_TOP_REVERSE_DOTTED:55,SOLID_ARROW_BOTTOM_REVERSE_DOTTED:56,STICK_ARROW_TOP_REVERSE_DOTTED:57,STICK_ARROW_BOTTOM_REVERSE_DOTTED:58,CENTRAL_CONNECTION:59,CENTRAL_CONNECTION_REVERSE:60,CENTRAL_CONNECTION_DUAL:61},wTt={FILLED:0,OPEN:1},STt={LEFTOF:0,RIGHTOF:1,OVER:2},jT={ACTOR:"actor",BOUNDARY:"boundary",COLLECTIONS:"collections",CONTROL:"control",DATABASE:"database",ENTITY:"entity",PARTICIPANT:"participant",QUEUE:"queue"},W6=class{constructor(){this.state=new wf(()=>({prevActor:void 0,actors:new Map,createdActors:new Map,destroyedActors:new Map,boxes:[],messages:[],notes:[],sequenceNumbersEnabled:!1,wrapEnabled:void 0,currentBox:void 0,lastCreated:void 0,lastDestroyed:void 0}));this.setAccTitle=Cr;this.setAccDescription=wr;this.setDiagramTitle=Ar;this.getAccTitle=kr;this.getAccDescription=Sr;this.getDiagramTitle=Er;this.apply=this.apply.bind(this),this.parseBoxData=this.parseBoxData.bind(this),this.parseMessage=this.parseMessage.bind(this),this.clear(),this.setWrap(ge().wrap),this.LINETYPE=kTt,this.ARROWTYPE=wTt,this.PLACEMENT=STt}static{o(this,"SequenceDB")}addBox(t){this.state.records.boxes.push({name:t.text,wrap:t.wrap??this.autoWrap(),fill:t.color,actorKeys:[]}),this.state.records.currentBox=this.state.records.boxes.slice(-1)[0]}addActor(t,r,i,n,a){let s=this.state.records.currentBox,l;if(a!==void 0){let h;a.includes(`
+`)?h=a+`
+`:h=`{
+`+a+`
+}`,l=ld(h,{schema:od})}n=l?.type??n,l?.alias&&(!i||i.text===r)&&(i={text:l.alias,wrap:i?.wrap,type:n});let u=this.state.records.actors.get(t);if(u){if(this.state.records.currentBox&&u.box&&this.state.records.currentBox!==u.box)throw new Error(`A same participant should only be defined in one Box: ${u.name} can't be in '${u.box.name}' and in '${this.state.records.currentBox.name}' at the same time.`);if(s=u.box?u.box:this.state.records.currentBox,u.box=s,u&&r===u.name&&i==null)return}if(i?.text==null&&(i={text:r,type:n}),(n==null||i.text==null)&&(i={text:r,type:n}),this.state.records.actors.set(t,{box:s,name:r,description:i.text,wrap:i.wrap??this.autoWrap(),prevActor:this.state.records.prevActor,links:{},properties:{},actorCnt:null,rectData:null,type:n??"participant"}),this.state.records.prevActor){let h=this.state.records.actors.get(this.state.records.prevActor);h&&(h.nextActor=t)}this.state.records.currentBox&&this.state.records.currentBox.actorKeys.push(t),this.state.records.prevActor=t}activationCount(t){let r,i=0;if(!t)return 0;for(r=0;r<this.state.records.messages.length;r++)this.state.records.messages[r].type===this.LINETYPE.ACTIVE_START&&this.state.records.messages[r].from===t&&i++,this.state.records.messages[r].type===this.LINETYPE.ACTIVE_END&&this.state.records.messages[r].from===t&&i--;return i}addMessage(t,r,i,n){this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:t,to:r,message:i.text,wrap:i.wrap??this.autoWrap(),answer:n})}addSignal(t,r,i,n,a=!1,s){if(n===this.LINETYPE.ACTIVE_END&&this.activationCount(t??"")<1){let u=new Error("Trying to inactivate an inactive participant ("+t+")");throw u.hash={text:"->>-",token:"->>-",line:"1",loc:{first_line:1,last_line:1,first_column:1,last_column:1},expected:["'ACTIVE_PARTICIPANT'"]},u}return this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:t,to:r,message:i?.text??"",wrap:i?.wrap??this.autoWrap(),type:n,activate:a,centralConnection:s??0}),!0}hasAtLeastOneBox(){return this.state.records.boxes.length>0}hasAtLeastOneBoxWithTitle(){return this.state.records.boxes.some(t=>t.name)}getMessages(){return this.state.records.messages}getBoxes(){return this.state.records.boxes}getActors(){return this.state.records.actors}getCreatedActors(){return this.state.records.createdActors}getDestroyedActors(){return this.state.records.destroyedActors}getActor(t){return this.state.records.actors.get(t)}getActorKeys(){return[...this.state.records.actors.keys()]}enableSequenceNumbers(){this.state.records.sequenceNumbersEnabled=!0}disableSequenceNumbers(){this.state.records.sequenceNumbersEnabled=!1}showSequenceNumbers(){return this.state.records.sequenceNumbersEnabled}setWrap(t){this.state.records.wrapEnabled=t}extractWrap(t){if(t===void 0)return{};t=t.trim();let r=/^:?wrap:/.exec(t)!==null?!0:/^:?nowrap:/.exec(t)!==null?!1:void 0;return{cleanedText:(r===void 0?t:t.replace(/^:?(?:no)?wrap:/,"")).trim(),wrap:r}}autoWrap(){return this.state.records.wrapEnabled!==void 0?this.state.records.wrapEnabled:ge().sequence?.wrap??!1}clear(){this.state.reset(),gr()}parseMessage(t){let r=t.trim(),{wrap:i,cleanedText:n}=this.extractWrap(r),a={text:n,wrap:i};return Z.debug(`parseMessage: ${JSON.stringify(a)}`),a}parseBoxData(t){let r=/^((?:rgba?|hsla?)\s*\(.*\)|\w*)(.*)$/.exec(t),i=r?.[1]?r[1].trim():"transparent",n=r?.[2]?r[2].trim():void 0;if(window?.CSS)window.CSS.supports("color",i)||(i="transparent",n=t.trim());else{let l=new Option().style;l.color=i,l.color!==i&&(i="transparent",n=t.trim())}let{wrap:a,cleanedText:s}=this.extractWrap(n);return{text:s?fr(s,ge()):void 0,color:i,wrap:a}}addNote(t,r,i){let n={actor:t,placement:r,message:i.text,wrap:i.wrap??this.autoWrap()},a=[].concat(t,t);this.state.records.notes.push(n),this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:a[0],to:a[1],message:i.text,wrap:i.wrap??this.autoWrap(),type:this.LINETYPE.NOTE,placement:r})}addLinks(t,r){let i=this.getActor(t);try{let n=fr(r.text,ge());n=n.replace(/&equals;/g,"="),n=n.replace(/&amp;/g,"&");let a=JSON.parse(n);this.insertLinks(i,a)}catch(n){Z.error("error while parsing actor link text",n)}}addALink(t,r){let i=this.getActor(t);try{let n={},a=fr(r.text,ge()),s=a.indexOf("@");a=a.replace(/&equals;/g,"="),a=a.replace(/&amp;/g,"&");let l=a.slice(0,s-1).trim(),u=a.slice(s+1).trim();n[l]=u,this.insertLinks(i,n)}catch(n){Z.error("error while parsing actor link text",n)}}insertLinks(t,r){if(t.links==null)t.links=r;else for(let i in r)t.links[i]=r[i]}addProperties(t,r){let i=this.getActor(t);try{let n=fr(r.text,ge()),a=JSON.parse(n);this.insertProperties(i,a)}catch(n){Z.error("error while parsing actor properties text",n)}}insertProperties(t,r){if(t.properties==null)t.properties=r;else for(let i in r)t.properties[i]=r[i]}boxEnd(){this.state.records.currentBox=void 0}addDetails(t,r){let i=this.getActor(t),n=document.getElementById(r.text);try{let a=n.innerHTML,s=JSON.parse(a);s.properties&&this.insertProperties(i,s.properties),s.links&&this.insertLinks(i,s.links)}catch(a){Z.error("error while parsing actor details text",a)}}getActorProperty(t,r){if(t?.properties!==void 0)return t.properties[r]}apply(t){if(Array.isArray(t))t.forEach(r=>{this.apply(r)});else switch(t.type){case"sequenceIndex":this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:void 0,to:void 0,message:{start:t.sequenceIndex,step:t.sequenceIndexStep,visible:t.sequenceVisible},wrap:!1,type:t.signalType});break;case"addParticipant":this.addActor(t.actor,t.actor,t.description,t.draw,t.config);break;case"createParticipant":if(this.state.records.actors.has(t.actor))throw new Error("It is not possible to have actors with the same id, even if one is destroyed before the next is created. Use 'AS' aliases to simulate the behavior");this.state.records.lastCreated=t.actor,this.addActor(t.actor,t.actor,t.description,t.draw,t.config),this.state.records.createdActors.set(t.actor,this.state.records.messages.length);break;case"destroyParticipant":this.state.records.lastDestroyed=t.actor,this.state.records.destroyedActors.set(t.actor,this.state.records.messages.length);break;case"activeStart":this.addSignal(t.actor,void 0,void 0,t.signalType);break;case"centralConnection":this.addSignal(t.actor,void 0,void 0,t.signalType);break;case"centralConnectionReverse":this.addSignal(t.actor,void 0,void 0,t.signalType);break;case"activeEnd":this.addSignal(t.actor,void 0,void 0,t.signalType);break;case"addNote":this.addNote(t.actor,t.placement,t.text);break;case"addLinks":this.addLinks(t.actor,t.text);break;case"addALink":this.addALink(t.actor,t.text);break;case"addProperties":this.addProperties(t.actor,t.text);break;case"addDetails":this.addDetails(t.actor,t.text);break;case"addMessage":if(this.state.records.lastCreated){if(t.to!==this.state.records.lastCreated)throw new Error("The created participant "+this.state.records.lastCreated.name+" does not have an associated creating message after its declaration. Please check the sequence diagram.");this.state.records.lastCreated=void 0}else if(this.state.records.lastDestroyed){if(t.to!==this.state.records.lastDestroyed&&t.from!==this.state.records.lastDestroyed)throw new Error("The destroyed participant "+this.state.records.lastDestroyed.name+" does not have an associated destroying message after its declaration. Please check the sequence diagram.");this.state.records.lastDestroyed=void 0}this.addSignal(t.from,t.to,t.msg,t.signalType,t.activate,t.centralConnection);break;case"boxStart":this.addBox(t.boxData);break;case"boxEnd":this.boxEnd();break;case"loopStart":this.addSignal(void 0,void 0,t.loopText,t.signalType);break;case"loopEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"rectStart":this.addSignal(void 0,void 0,t.color,t.signalType);break;case"rectEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"optStart":this.addSignal(void 0,void 0,t.optText,t.signalType);break;case"optEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"altStart":this.addSignal(void 0,void 0,t.altText,t.signalType);break;case"else":this.addSignal(void 0,void 0,t.altText,t.signalType);break;case"altEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"setAccTitle":Cr(t.text);break;case"parStart":this.addSignal(void 0,void 0,t.parText,t.signalType);break;case"and":this.addSignal(void 0,void 0,t.parText,t.signalType);break;case"parEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"criticalStart":this.addSignal(void 0,void 0,t.criticalText,t.signalType);break;case"option":this.addSignal(void 0,void 0,t.optionText,t.signalType);break;case"criticalEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break;case"breakStart":this.addSignal(void 0,void 0,t.breakText,t.signalType);break;case"breakEnd":this.addSignal(void 0,void 0,void 0,t.signalType);break}}getConfig(){return ge().sequence}}});var ETt,o6e,l6e=z(()=>{"use strict";Ut();ETt=o(e=>{let t=e.dropShadow??"none",{look:r}=ge();return`.actor {
+    stroke: ${e.actorBorder};
+    fill: ${e.actorBkg};
+    stroke-width: ${e.strokeWidth??1};
+  }
+
+  rect.actor.outer-path[data-look="neo"] {
+      filter: ${t};
+  }
+
+  rect.note[data-look="neo"] {
+      stroke:${e.noteBorderColor};
+      fill:${e.noteBkgColor};
+      filter: ${t};
+  }
+
+  text.actor > tspan {
+    fill: ${e.actorTextColor};
+    stroke: none;
+  }
+
+  .actor-line {
+    stroke: ${e.actorLineColor};
+  }
+
+  .innerArc {
+    stroke-width: 1.5;
+    stroke-dasharray: none;
+  }
+
+  .messageLine0 {
+    stroke-width: 1.5;
+    stroke-dasharray: none;
+    stroke: ${e.signalColor};
+  }
+
+  .messageLine1 {
+    stroke-width: 1.5;
+    stroke-dasharray: 2, 2;
+    stroke: ${e.signalColor};
+  }
+
+  [id$="-arrowhead"] path {
+    fill: ${e.signalColor};
+    stroke: ${e.signalColor};
+  }
+
+  .sequenceNumber {
+    fill: ${e.sequenceNumberColor};
+  }
+
+  [id$="-sequencenumber"] {
+    fill: ${e.signalColor};
+  }
+
+  [id$="-crosshead"] path {
+    fill: ${e.signalColor};
+    stroke: ${e.signalColor};
+  }
+
+  .messageText {
+    fill: ${e.signalTextColor};
+    stroke: none;
+  }
+
+  .labelBox {
+    stroke: ${e.labelBoxBorderColor};
+    fill: ${e.labelBoxBkgColor};
+    filter: ${r==="neo"?t:"none"};
+  }
+
+  .labelText, .labelText > tspan {
+    fill: ${e.labelTextColor};
+    stroke: none;
+  }
+
+  .loopText, .loopText > tspan {
+    fill: ${e.loopTextColor};
+    stroke: none;
+  }
+
+  .sectionTitle, .sectionTitle > tspan {
+    fill: ${e.loopTextColor};
+    stroke: none;
+  }
+
+  .loopLine {
+    stroke-width: 2px;
+    stroke-dasharray: 2, 2;
+    stroke: ${e.labelBoxBorderColor};
+    fill: ${e.labelBoxBorderColor};
+  }
+
+  .note {
+    //stroke: #decc93;
+    stroke: ${e.noteBorderColor};
+    fill: ${e.noteBkgColor};
+  }
+
+  .noteText, .noteText > tspan {
+    fill: ${e.noteTextColor};
+    stroke: none;
+    ${e.noteFontWeight?`font-weight: ${e.noteFontWeight};`:""}
+  }
+
+  .activation0 {
+    fill: ${e.activationBkgColor};
+    stroke: ${e.activationBorderColor};
+  }
+
+  .activation1 {
+    fill: ${e.activationBkgColor};
+    stroke: ${e.activationBorderColor};
+  }
+
+  .activation2 {
+    fill: ${e.activationBkgColor};
+    stroke: ${e.activationBorderColor};
+  }
+
+  .actorPopupMenu {
+    position: absolute;
+  }
+
+  .actorPopupMenuPanel {
+    position: absolute;
+    fill: ${e.actorBkg};
+    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+    filter: drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));
+}
+  .actor-man circle, line {
+    fill: ${e.actorBkg};
+    stroke-width: 2px;
+  }
+
+  g rect.rect {
+    filter: ${t};
+    stroke: ${e.nodeBorder};
+  }
+`},"getStyles"),o6e=ETt});var sW,Af,_f,Df,q6,mm,Sh,XT,ATt,U6,KT,gm,c6e,Wr,oW,_Tt,DTt,RTt,LTt,MTt,NTt,ITt,OTt,PTt,BTt,FTt,$Tt,zTt,u6e,GTt,VTt,WTt,qTt,UTt,HTt,YTt,jTt,h6e,XTt,Eh,KTt,ZTt,QTt,JTt,eCt,Ei,d6e=z(()=>{"use strict";sW=ys(fg(),1);br();Xt();Ir();gp();Af=36,_f="actor-top",Df="actor-bottom",q6="actor-box",mm="actor-man",Sh=new Set(["redux-color","redux-dark-color"]),XT=o(function(e,t){let r=mp(e,t);return Nt().look==="neo"&&r.attr("data-look","neo"),r},"drawRect"),ATt=o(function(e,t,r,i,n){if(t.links===void 0||t.links===null||Object.keys(t.links).length===0)return{height:0,width:0};let a=t.links,s=t.actorCnt,l=t.rectData;var u="none";n&&(u="block !important");let h=e.append("g");h.attr("id","actor"+s+"_popup"),h.attr("class","actorPopupMenu"),h.attr("display",u);var d="";l.class!==void 0&&(d=" "+l.class);let f=l.width>r?l.width:r,p=h.append("rect");if(p.attr("class","actorPopupMenuPanel"+d),p.attr("x",l.x),p.attr("y",l.height),p.attr("fill",l.fill),p.attr("stroke",l.stroke),p.attr("width",f),p.attr("height",l.height),p.attr("rx",l.rx),p.attr("ry",l.ry),a!=null){var m=20;for(let v in a){var g=h.append("a"),y=(0,sW.sanitizeUrl)(a[v]);g.attr("xlink:href",y),g.attr("target","_blank"),KTt(i)(v,g,l.x+10,l.height+m,f,20,{class:"actor"},i),m+=30}}return p.attr("height",m),{height:l.height+m,width:f}},"drawPopup"),U6=o(function(e){return"var pu = document.getElementById('"+e+"'); if (pu != null) { pu.style.display = pu.style.display == 'block' ? 'none' : 'block'; }"},"popupMenuToggle"),KT=o(async function(e,t,r=null){let i=e.append("foreignObject"),n=await Fm(t.text,Nt()),s=i.append("xhtml:div").attr("style","width: fit-content;").attr("xmlns","http://www.w3.org/1999/xhtml").html(n).node().getBoundingClientRect();if(i.attr("height",Math.round(s.height)).attr("width",Math.round(s.width)),t.class==="noteText"){let l=e.node().firstChild;l.setAttribute("height",s.height+2*t.textMargin);let u=l.getBBox();i.attr("x",Math.round(u.x+u.width/2-s.width/2)).attr("y",Math.round(u.y+u.height/2-s.height/2))}else if(r){let{startx:l,stopx:u,starty:h}=r;if(l>u){let d=l;l=u,u=d}i.attr("x",Math.round(l+Math.abs(l-u)/2-s.width/2)),t.class==="loopText"?i.attr("y",Math.round(h)):i.attr("y",Math.round(h-s.height))}return[i]},"drawKatex"),gm=o(function(e,t){let r=0,i=0,n=t.text.split(vt.lineBreakRegex),[a,s]=Za(t.fontSize),l=[],u=0,h=o(()=>t.y,"yfunc");if(t.valign!==void 0&&t.textMargin!==void 0&&t.textMargin>0)switch(t.valign){case"top":case"start":h=o(()=>Math.round(t.y+t.textMargin),"yfunc");break;case"middle":case"center":h=o(()=>Math.round(t.y+(r+i+t.textMargin)/2),"yfunc");break;case"bottom":case"end":h=o(()=>Math.round(t.y+(r+i+2*t.textMargin)-t.textMargin),"yfunc");break}if(t.anchor!==void 0&&t.textMargin!==void 0&&t.width!==void 0)switch(t.anchor){case"left":case"start":t.x=Math.round(t.x+t.textMargin),t.anchor="start",t.dominantBaseline="middle",t.alignmentBaseline="middle";break;case"middle":case"center":t.x=Math.round(t.x+t.width/2),t.anchor="middle",t.dominantBaseline="middle",t.alignmentBaseline="middle";break;case"right":case"end":t.x=Math.round(t.x+t.width-t.textMargin),t.anchor="end",t.dominantBaseline="middle",t.alignmentBaseline="middle";break}for(let[d,f]of n.entries()){t.textMargin!==void 0&&t.textMargin===0&&a!==void 0&&(u=d*a);let p=e.append("text");p.attr("x",t.x),p.attr("y",h()),t.anchor!==void 0&&p.attr("text-anchor",t.anchor).attr("dominant-baseline",t.dominantBaseline).attr("alignment-baseline",t.alignmentBaseline),t.fontFamily!==void 0&&p.style("font-family",t.fontFamily),s!==void 0&&p.style("font-size",s),t.fontWeight!==void 0&&p.style("font-weight",t.fontWeight),t.fill!==void 0&&p.attr("fill",t.fill),t.class!==void 0&&p.attr("class",t.class),t.dy!==void 0?p.attr("dy",t.dy):u!==0&&p.attr("dy",u);let m=f||SM;if(t.tspan){let g=p.append("tspan");g.attr("x",t.x),t.fill!==void 0&&g.attr("fill",t.fill),g.text(m)}else p.text(m);t.valign!==void 0&&t.textMargin!==void 0&&t.textMargin>0&&(i+=(p._groups||p)[0][0].getBBox().height,r=i),l.push(p)}return l},"drawText"),c6e=o(function(e,t){function r(n,a,s,l,u){return n+","+a+" "+(n+s)+","+a+" "+(n+s)+","+(a+l-u)+" "+(n+s-u*1.2)+","+(a+l)+" "+n+","+(a+l)}o(r,"genPoints");let i=e.append("polygon");return i.attr("points",r(t.x,t.y,t.width,t.height,7)),i.attr("class","labelBox"),t.y=t.y+t.height/2,gm(e,t),i},"drawLabel"),Wr=-1,oW=o((e,t,r,i)=>{e.select&&r.forEach(n=>{let a=t.get(n),s=e.select("#actor"+a.actorCnt);!i.mirrorActors&&a.stopy?s.attr("y2",a.stopy+a.height/2):i.mirrorActors&&s.attr("y2",a.stopy)})},"fixLifeLineHeights"),_Tt=o(function(e,t,r,i,n){let a=i?t.stopy:t.starty,s=t.x+t.width/2,l=a+t.height,{look:u,theme:h,themeVariables:d}=r,{bkgColorArray:f,borderColorArray:p}=d,m=e.append("g").lower();var g=m;i||(Wr++,Object.keys(t.links||{}).length&&!r.forceMenus&&g.attr("onclick",U6(`actor${Wr}_popup`)).attr("cursor","pointer"),g.append("line").attr("id","actor"+Wr).attr("x1",s).attr("y1",l).attr("x2",s).attr("y2",2e3).attr("class","actor-line 200").attr("stroke-width","0.5px").attr("stroke","#999").attr("name",t.name).attr("data-et","life-line").attr("data-id",t.name),g=m.append("g"),t.actorCnt=Wr,t.links!=null&&g.attr("id","root-"+Wr),u==="neo"&&g.attr("data-look","neo"));let y=fa();var v="actor";t.properties?.class?v=t.properties.class:y.fill="#eaeaea",i?v+=` ${Df}`:v+=` ${_f}`,y.x=t.x,y.y=a,y.width=t.width,y.height=t.height,y.class=v,y.rx=3,y.ry=3,y.name=t.name,u==="neo"&&(y.rx=6,y.ry=6);let x=XT(g,y),b=n.get(t.name)??0;if(Sh.has(h)&&(x.style("stroke",p[b%p.length]),x.style("fill",f[b%p.length])),u==="neo"&&x.attr("filter","url(#drop-shadow)"),t.rectData=y,t.properties?.icon){let w=t.properties.icon.trim();w.charAt(0)==="@"?$w(g,y.x+y.width-20,y.y+10,w.substr(1)):Fw(g,y.x+y.width-20,y.y+10,w)}i||(g.attr("data-et","participant"),g.attr("data-type","participant"),g.attr("data-id",t.name)),Eh(r,Bi(t.description))(t.description,g,y.x,y.y,y.width,y.height,{class:`actor ${q6}`},r);let T=t.height;if(x.node){let w=x.node().getBBox();t.height=w.height,T=w.height}return T},"drawActorTypeParticipant"),DTt=o(function(e,t,r,i,n){let a=i?t.stopy:t.starty,s=t.x+t.width/2,l=a+t.height,{look:u,theme:h,themeVariables:d}=r,{bkgColorArray:f,borderColorArray:p}=d,m=e.append("g").lower();var g=m;i||(Wr++,Object.keys(t.links||{}).length&&!r.forceMenus&&g.attr("onclick",U6(`actor${Wr}_popup`)).attr("cursor","pointer"),g.append("line").attr("id","actor"+Wr).attr("x1",s).attr("y1",l).attr("x2",s).attr("y2",2e3).attr("class","actor-line 200").attr("stroke-width","0.5px").attr("stroke","#999").attr("name",t.name).attr("data-et","life-line").attr("data-id",t.name),g=m.append("g"),t.actorCnt=Wr,t.links!=null&&g.attr("id","root-"+Wr),u==="neo"&&g.attr("data-look","neo"));let y=fa();var v="actor";t.properties?.class?v=t.properties.class:y.fill="#eaeaea",i?v+=` ${Df}`:v+=` ${_f}`,y.x=t.x,y.y=a,y.width=t.width,y.height=t.height,y.class=v,y.name=t.name;let x=6,b={...y,x:y.x+-x,y:y.y+ +x,class:"actor"},T=XT(g,y),w=XT(g,b);t.rectData=y,u==="neo"&&g.attr("filter","url(#drop-shadow)");let C=n.get(t.name)??0;if(Sh.has(h)&&(T.style("stroke",p[C%p.length]),T.style("fill",f[C%p.length]),w.style("stroke",p[C%p.length]),w.style("fill",f[C%p.length])),t.properties?.icon){let E=t.properties.icon.trim();E.charAt(0)==="@"?$w(g,y.x+y.width-20,y.y+10,E.substr(1)):Fw(g,y.x+y.width-20,y.y+10,E)}Eh(r,Bi(t.description))(t.description,g,y.x-x,y.y+x,y.width,y.height,{class:`actor ${q6}`},r);let k=t.height;if(T.node){let E=T.node().getBBox();t.height=E.height,k=E.height}return i||(g.attr("data-et","participant"),g.attr("data-type","collections"),g.attr("data-id",t.name)),k},"drawActorTypeCollections"),RTt=o(function(e,t,r,i,n){let a=i?t.stopy:t.starty,s=t.x+t.width/2,l=a+t.height,{look:u,theme:h,themeVariables:d}=r,{bkgColorArray:f,borderColorArray:p}=d,m=e.append("g").lower(),g=m;i||(Wr++,Object.keys(t.links||{}).length&&!r.forceMenus&&g.attr("onclick",U6(`actor${Wr}_popup`)).attr("cursor","pointer"),g.append("line").attr("id","actor"+Wr).attr("x1",s).attr("y1",l).attr("x2",s).attr("y2",2e3).attr("class","actor-line 200").attr("stroke-width","0.5px").attr("stroke","#999").attr("name",t.name).attr("data-et","life-line").attr("data-id",t.name),g=m.append("g"),t.actorCnt=Wr,t.links!=null&&g.attr("id","root-"+Wr),u==="neo"&&g.attr("data-look","neo"));let y=fa(),v="actor";t.properties?.class?v=t.properties.class:y.fill="#eaeaea",i?v+=` ${Df}`:v+=` ${_f}`,g.attr("class",v),y.x=t.x,y.y=a,y.width=t.width,y.height=t.height,y.name=t.name;let x=y.height/2,b=x/(2.5+y.height/50),T=g.append("g"),w=g.append("g"),C=`M ${y.x},${y.y+x}
+    a ${b},${x} 0 0 0 0,${y.height}
+    h ${y.width-2*b}
+    a ${b},${x} 0 0 0 0,-${y.height}
+    Z
+  `;T.append("path").attr("d",C),w.append("path").attr("d",`M ${y.x},${y.y+x}
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x="";if(We.arrowMarkerAbsolute&&(x=Hf(!0)),v.attr("stroke-width",2),v.attr("stroke","none"),v.style("fill","none"),(d===i.db.LINETYPE.SOLID_TOP||d===i.db.LINETYPE.SOLID_TOP_DOTTED)&&v.attr("marker-end","url("+x+"#"+a+"-solidTopArrowHead)"),(d===i.db.LINETYPE.SOLID_BOTTOM||d===i.db.LINETYPE.SOLID_BOTTOM_DOTTED)&&v.attr("marker-end","url("+x+"#"+a+"-solidBottomArrowHead)"),(d===i.db.LINETYPE.STICK_TOP||d===i.db.LINETYPE.STICK_TOP_DOTTED)&&v.attr("marker-end","url("+x+"#"+a+"-stickTopArrowHead)"),(d===i.db.LINETYPE.STICK_BOTTOM||d===i.db.LINETYPE.STICK_BOTTOM_DOTTED)&&v.attr("marker-end","url("+x+"#"+a+"-stickBottomArrowHead)"),(d===i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE||d===i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE_DOTTED)&&v.attr("marker-start","url("+x+"#"+a+"-solidBottomArrowHead)"),(d===i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE||d===i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE_DOTTED)&&v.attr("marker-start","url("+x+"#"+a+"-solidTopArrowHead)"),(d===i.db.LINETYPE.STICK_ARROW_TOP_REVERSE||d===i.db.LINETYPE.STICK_ARROW_TOP_REVERSE_DOTTED)&&v.attr("marker-start","url("+x+"#"+a+"-stickBottomArrowHead)"),(d===i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE||d===i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE_DOTTED)&&v.attr("marker-start","url("+x+"#"+a+"-stickTopArrowHead)"),(d===i.db.LINETYPE.SOLID||d===i.db.LINETYPE.DOTTED)&&v.attr("marker-end","url("+x+"#"+a+"-arrowhead)"),(d===i.db.LINETYPE.BIDIRECTIONAL_SOLID||d===i.db.LINETYPE.BIDIRECTIONAL_DOTTED)&&(v.attr("marker-start","url("+x+"#"+a+"-arrowhead)"),v.attr("marker-end","url("+x+"#"+a+"-arrowhead)")),(d===i.db.LINETYPE.SOLID_POINT||d===i.db.LINETYPE.DOTTED_POINT)&&v.attr("marker-end","url("+x+"#"+a+"-filled-head)"),(d===i.db.LINETYPE.SOLID_CROSS||d===i.db.LINETYPE.DOTTED_CROSS)&&v.attr("marker-end","url("+x+"#"+a+"-crosshead)"),p||We.showSequenceNumbers){let b=d===i.db.LINETYPE.BIDIRECTIONAL_SOLID||d===i.db.LINETYPE.BIDIRECTIONAL_DOTTED,T=d===i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE||d===i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE_DOTTED||d===i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE||d===i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE_DOTTED||d===i.db.LINETYPE.STICK_ARROW_TOP_REVERSE||d===i.db.LINETYPE.STICK_ARROW_TOP_REVERSE_DOTTED||d===i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE||d===i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE_DOTTED,w=6,C=lW(n,i),k=s,E=l;b?(s<l?k=s+w*2:(k=s-w+(C?-5:0),k+=n?.centralConnection===i.db.LINETYPE.CENTRAL_CONNECTION_DUAL||n?.centralConnection===i.db.LINETYPE.CENTRAL_CONNECTION_REVERSE?-7.5:0),v.attr("x1",k)):T?(l>s?E=l-2*w:(E=l-w,k+=n?.centralConnection===i.db.LINETYPE.CENTRAL_CONNECTION_DUAL||n?.centralConnection===i.db.LINETYPE.CENTRAL_CONNECTION_REVERSE?-7.5:0),E+=C?15:0,v.attr("x2",E),v.attr("x1",k)):v.attr("x1",s+w);let A=0,N=s===l,P=s<=l;N?A=t.fromBounds+1:T?A=P?t.toBounds-1:t.fromBounds+1:A=P?t.fromBounds+1:t.toBounds-1;let I="12px",D=f.toString().length;D>5?I="7px":D>3&&(I="9px"),e.append("line").attr("x1",A).attr("y1",r).attr("x2",A).attr("y2",r).attr("stroke-width",0).attr("marker-start","url("+x+"#"+a+"-sequencenumber)"),e.append("text").attr("x",A).attr("y",r+4).attr("font-family","sans-serif").attr("font-size",I).attr("text-anchor","middle").attr("class","sequenceNumber").text(f)}},"drawMessage"),nCt=o(function(e,t,r,i,n,a,s){let l=0,u=0,h,d=0;for(let f of i){let p=t.get(f),m=p.box;h&&h!=m&&(s||bt.models.addBox(h),u+=We.boxMargin+h.margin),m&&m!=h&&(s||(m.x=l+u,m.y=n),u+=m.margin),p.width=vt.getMax(p.width||We.width,We.width),p.height=vt.getMax(p.height||We.height,We.height),p.margin=p.margin||We.actorMargin,d=vt.getMax(d,p.height),r.get(p.name)&&(u+=p.width/2),p.x=l+u,p.starty=bt.getVerticalPos(),bt.insert(p.x,n,p.x+p.width,p.height),l+=p.width+u,p.box&&(p.box.width=l+m.margin-p.box.x),u=p.margin,h=p.box,bt.models.addActor(p)}h&&!s&&bt.models.addBox(h),bt.bumpVerticalPos(d)},"addActorRenderingData"),uW=o(async function(e,t,r,i,n,a,s){if(i){let l=0;bt.bumpVerticalPos(We.boxMargin*2);for(let u of r){let h=t.get(u);h.stopy||(h.stopy=bt.getVerticalPos());let d=await Ei.drawActor(e,h,We,!0,n,a,s);l=vt.getMax(l,d)}bt.bumpVerticalPos(l+We.boxMargin)}else for(let l of r){let u=t.get(l);await Ei.drawActor(e,u,We,!1,n,a,s)}},"drawActors"),m6e=o(function(e,t,r,i){let n=0,a=0;for(let s of r){let l=t.get(s),u=lCt(l),h=Ei.drawPopup(e,l,u,We,We.forceMenus,i);h.height>n&&(n=h.height),h.width+l.x>a&&(a=h.width+l.x)}return{maxHeight:n,maxWidth:a}},"drawActorsPopup"),g6e=o(function(e){Ai(We,e),e.fontFamily&&(We.actorFontFamily=We.noteFontFamily=We.messageFontFamily=e.fontFamily),e.fontSize&&(We.actorFontSize=We.noteFontSize=We.messageFontSize=e.fontSize),e.fontWeight&&(We.actorFontWeight=We.noteFontWeight=We.messageFontWeight=e.fontWeight)},"setConf"),H6=o(function(e){return bt.activations.filter(function(t){return t.actor===e})},"actorActivations"),p6e=o(function(e,t){let r=t.get(e),i=H6(e),n=i.reduce(function(s,l){return vt.getMin(s,l.startx)},r.x+r.width/2-1),a=i.reduce(function(s,l){return vt.getMax(s,l.stopx)},r.x+r.width/2+1);return[n,a]},"activationBounds");o(lu,"adjustLoopHeightForWrap");o(aCt,"adjustCreatedDestroyedData");sCt=o(async function(e,t,r,i){let{securityLevel:n,sequence:a,look:s}=ge();We=a;let l;n==="sandbox"&&(l=Je("#i"+t));let u=n==="sandbox"?Je(l.nodes()[0].contentDocument.body):Je("body"),h=n==="sandbox"?l.nodes()[0].contentDocument:document;bt.init(),Z.debug(i.db);let d=n==="sandbox"?u.select(`[id="${t}"]`):Je(`[id="${t}"]`),f=i.db.getActors(),p=i.db.getCreatedActors(),m=i.db.getDestroyedActors(),g=i.db.getBoxes(),y=i.db.getActorKeys(),v=i.db.getMessages(),x=i.db.getDiagramTitle(),b=i.db.hasAtLeastOneBox(),T=i.db.hasAtLeastOneBoxWithTitle(),w=await oCt(f,v,i);if(We.height=await cCt(f,w,g),Ei.insertComputerIcon(d,t),Ei.insertDatabaseIcon(d,t),Ei.insertClockIcon(d,t),b&&(bt.bumpVerticalPos(We.boxMargin),T&&bt.bumpVerticalPos(g[0].textMaxHeight)),We.hideUnusedParticipants===!0){let F=new Set;v.forEach(V=>{F.add(V.from),F.add(V.to)}),y=y.filter(V=>F.has(V))}let C=new Map(y.map((F,V)=>[f.get(F)?.name??F,V]));nCt(d,f,p,y,0,v,!1);let k=await gCt(v,f,w,i);Ei.insertArrowHead(d,t),Ei.insertArrowCrossHead(d,t),Ei.insertArrowFilledHead(d,t),Ei.insertSequenceNumber(d,t),Ei.insertSolidTopArrowHead(d,t),Ei.insertSolidBottomArrowHead(d,t),Ei.insertStickTopArrowHead(d,t),Ei.insertStickBottomArrowHead(d,t),s==="neo"&&Ei.insertDropShadow(d,We);function E(F,V){let H=bt.endActivation(F);H.starty+18>V&&(H.starty=V-6,V+=12),Ei.drawActivation(d,H,V,We,H6(F.from).length,i,C),bt.insert(H.startx,V-10,H.stopx,V)}o(E,"activeEnd");let A=1,N=1,P=[],I=[],D=0;for(let F of v){let V,H,j;switch(F.type){case i.db.LINETYPE.NOTE:bt.resetVerticalPos(),H=F.noteModel,await tCt(d,H,F.id);break;case i.db.LINETYPE.ACTIVE_START:bt.newActivation(F,d,f);break;case i.db.LINETYPE.CENTRAL_CONNECTION:bt.newActivation(F,d,f);break;case i.db.LINETYPE.CENTRAL_CONNECTION_REVERSE:bt.newActivation(F,d,f);break;case i.db.LINETYPE.ACTIVE_END:E(F,bt.getVerticalPos());break;case i.db.LINETYPE.LOOP_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U));break;case i.db.LINETYPE.LOOP_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"loop",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;case i.db.LINETYPE.RECT_START:lu(k,F,We.boxMargin,We.boxMargin,U=>bt.newLoop(void 0,U.message));break;case i.db.LINETYPE.RECT_END:V=bt.endLoop(),I.push(V),bt.models.addLoop(V),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos());break;case i.db.LINETYPE.OPT_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U));break;case i.db.LINETYPE.OPT_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"opt",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;case i.db.LINETYPE.ALT_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U));break;case i.db.LINETYPE.ALT_ELSE:lu(k,F,We.boxMargin+We.boxTextMargin,We.boxMargin,U=>bt.addSectionToLoop(U));break;case i.db.LINETYPE.ALT_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"alt",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;case i.db.LINETYPE.PAR_START:case i.db.LINETYPE.PAR_OVER_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U)),bt.saveVerticalPos();break;case i.db.LINETYPE.PAR_AND:lu(k,F,We.boxMargin+We.boxTextMargin,We.boxMargin,U=>bt.addSectionToLoop(U));break;case i.db.LINETYPE.PAR_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"par",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;case i.db.LINETYPE.AUTONUMBER:A=F.message.start||A,N=F.message.step||N,F.message.visible?i.db.enableSequenceNumbers():i.db.disableSequenceNumbers();break;case i.db.LINETYPE.CRITICAL_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U));break;case i.db.LINETYPE.CRITICAL_OPTION:lu(k,F,We.boxMargin+We.boxTextMargin,We.boxMargin,U=>bt.addSectionToLoop(U));break;case i.db.LINETYPE.CRITICAL_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"critical",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;case i.db.LINETYPE.BREAK_START:lu(k,F,We.boxMargin,We.boxMargin+We.boxTextMargin,U=>bt.newLoop(U));break;case i.db.LINETYPE.BREAK_END:V=bt.endLoop(),await Ei.drawLoop(d,V,"break",We,F),bt.bumpVerticalPos(V.stopy-bt.getVerticalPos()),bt.models.addLoop(V);break;default:try{j=F.msgModel,j.starty=bt.getVerticalPos(),j.sequenceIndex=A,j.sequenceVisible=i.db.showSequenceNumbers(),j.id=F.id,j.from=F.from,j.to=F.to;let U=await rCt(d,j);aCt(F,j,U,D,f,p,m),P.push({messageModel:j,lineStartY:U,msg:F}),bt.models.addMessage(j)}catch(U){Z.error("error while drawing message",U)}}[i.db.LINETYPE.SOLID_OPEN,i.db.LINETYPE.DOTTED_OPEN,i.db.LINETYPE.SOLID,i.db.LINETYPE.SOLID_TOP,i.db.LINETYPE.SOLID_BOTTOM,i.db.LINETYPE.STICK_TOP,i.db.LINETYPE.STICK_BOTTOM,i.db.LINETYPE.SOLID_TOP_DOTTED,i.db.LINETYPE.SOLID_BOTTOM_DOTTED,i.db.LINETYPE.STICK_TOP_DOTTED,i.db.LINETYPE.STICK_BOTTOM_DOTTED,i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE,i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE,i.db.LINETYPE.STICK_ARROW_TOP_REVERSE,i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE,i.db.LINETYPE.SOLID_ARROW_TOP_REVERSE_DOTTED,i.db.LINETYPE.SOLID_ARROW_BOTTOM_REVERSE_DOTTED,i.db.LINETYPE.STICK_ARROW_TOP_REVERSE_DOTTED,i.db.LINETYPE.STICK_ARROW_BOTTOM_REVERSE_DOTTED,i.db.LINETYPE.DOTTED,i.db.LINETYPE.SOLID_CROSS,i.db.LINETYPE.DOTTED_CROSS,i.db.LINETYPE.SOLID_POINT,i.db.LINETYPE.DOTTED_POINT,i.db.LINETYPE.BIDIRECTIONAL_SOLID,i.db.LINETYPE.BIDIRECTIONAL_DOTTED].includes(F.type)&&(A=Math.round((A+N)*100)/100),D++}Z.debug("createdActors",p),Z.debug("destroyedActors",m),await uW(d,f,y,!1,t,i,C);for(let F 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88:this.$=ie.relationType.LOLLIPOP;break;case 89:this.$=ie.lineType.LINE;break;case 90:this.$=ie.lineType.DOTTED_LINE;break;case 91:case 97:this.$=W[K-2],ie.setClickEvent(W[K-1],W[K]);break;case 92:case 98:this.$=W[K-3],ie.setClickEvent(W[K-2],W[K-1]),ie.setTooltip(W[K-2],W[K]);break;case 93:this.$=W[K-2],ie.setLink(W[K-1],W[K]);break;case 94:this.$=W[K-3],ie.setLink(W[K-2],W[K-1],W[K]);break;case 95:this.$=W[K-3],ie.setLink(W[K-2],W[K-1]),ie.setTooltip(W[K-2],W[K]);break;case 96:this.$=W[K-4],ie.setLink(W[K-3],W[K-2],W[K]),ie.setTooltip(W[K-3],W[K-1]);break;case 99:this.$=W[K-3],ie.setClickEvent(W[K-2],W[K-1],W[K]);break;case 100:this.$=W[K-4],ie.setClickEvent(W[K-3],W[K-2],W[K-1]),ie.setTooltip(W[K-3],W[K]);break;case 101:this.$=W[K-3],ie.setLink(W[K-2],W[K]);break;case 102:this.$=W[K-4],ie.setLink(W[K-3],W[K-1],W[K]);break;case 103:this.$=W[K-4],ie.setLink(W[K-3],W[K-1]),ie.setTooltip(W[K-3],W[K]);break;case 104:this.$=W[K-5],ie.setLink(W[K-4],W[K-2],W[K]),ie.setTooltip(W[K-4],W[K-1]);break;case 105:this.$=W[K-2],ie.setCssStyle(W[K-1],W[K]);break;case 106:ie.setCssClass(W[K-1],W[K]);break;case 107:this.$=[W[K]];break;case 108:W[K-2].push(W[K]),this.$=W[K-2];break;case 110:this.$=W[K-1]+W[K];break}},"anonymous"),table:[{3:1,4:2,5:3,6:4,7:[1,6],10:5,16:39,17:40,19:21,21:7,23:8,24:9,25:10,26:11,27:12,28:13,29:14,30:15,31:16,32:17,33:t,35:r,37:i,38:22,42:n,43:23,46:a,48:s,51:l,52:u,54:h,56:d,57:f,60:p,62:m,63:g,64:y,65:v,75:x,76:b,78:T,82:w,83:C,86:k,100:E,102:A,103:N},{1:[3]},{1:[2,1]},{1:[2,2]},{1:[2,3]},e(P,[2,5],{8:[1,48]}),{8:[1,49]},e(I,[2,19],{22:[1,50]}),e(I,[2,21]),e(I,[2,22]),e(I,[2,23]),e(I,[2,24]),e(I,[2,25]),e(I,[2,26]),e(I,[2,27]),e(I,[2,28]),e(I,[2,29]),e(I,[2,30]),{34:[1,51]},{36:[1,52]},e(I,[2,33]),e(I,[2,63],{53:53,66:56,67:57,13:[1,54],22:[1,55],68:D,69:_,70:R,71:M,72:L,73:B,74:O}),{39:[1,65]},e($,[2,47],{39:[1,67],44:[1,66],46:[1,68]}),e(I,[2,65]),e(I,[2,66]),{16:69,60:p,86:k,100:E,102:A},{16:39,17:40,19:70,60:p,86:k,100:E,102:A,103:N},{16:39,17:40,19:71,60:p,86:k,100:E,102:A,103:N},{16:39,17:40,19:72,60:p,86:k,100:E,102:A,103:N},{60:[1,73]},{13:[1,74]},{16:39,17:40,19:75,60:p,86:k,100:E,102:A,103:N},{13:G,55:76},{58:78,60:[1,79]},e(I,[2,76]),e(I,[2,77]),e(I,[2,78]),e(I,[2,79]),e(F,[2,13],{16:39,17:40,19:81,18:[1,80],20:[1,82],60:p,86:k,100:E,102:A,103:N}),e(F,[2,15],{20:[1,83]}),{15:84,16:85,17:86,60:p,86:k,100:E,102:A,103:N},{16:39,17:40,19:87,60:p,86:k,100:E,102:A,103:N},e(V,[2,133]),e(V,[2,134]),e(V,[2,135]),e(V,[2,136]),e([1,8,9,12,13,20,22,39,41,44,46,68,69,70,71,72,73,74,79,81],[2,137]),e(P,[2,6],{10:5,21:7,23:8,24:9,25:10,26:11,27:12,28:13,29:14,30:15,31:16,32:17,19:21,38:22,43:23,16:39,17:40,5:88,33:t,35:r,37:i,42:n,46:a,48:s,51:l,52:u,54:h,56:d,57:f,60:p,62:m,63:g,64:y,65:v,75:x,76:b,78:T,82:w,83:C,86:k,100:E,102:A,103:N}),{5:89,10:5,16:39,17:40,19:21,21:7,23:8,24:9,25:10,26:11,27:12,28:13,29:14,30:15,31:16,32:17,33:t,35:r,37:i,38:22,42:n,43:23,46:a,48:s,51:l,52:u,54:h,56:d,57:f,60:p,62:m,63:g,64:y,65:v,75:x,76:b,78:T,82:w,83:C,86:k,100:E,102:A,103:N},e(I,[2,20]),e(I,[2,31]),e(I,[2,32]),{13:[1,91],16:39,17:40,19:90,60:p,86:k,100:E,102:A,103:N},{53:92,66:56,67:57,68:D,69:_,70:R,71:M,72:L,73:B,74:O},e(I,[2,64]),{67:93,73:B,74:O},e(H,[2,83],{66:94,68:D,69:_,70:R,71:M,72:L}),e(j,[2,84]),e(j,[2,85]),e(j,[2,86]),e(j,[2,87]),e(j,[2,88]),e(U,[2,89]),e(U,[2,90]),{8:[1,96],23:99,24:97,30:98,38:22,40:95,42:n,43:23,48:s,54:h,56:d},{16:100,60:p,86:k,100:E,102:A},{41:[1,102],45:101,51:Q},{16:104,60:p,86:k,100:E,102:A},{47:[1,105]},{13:[1,106]},{13:[1,107]},{79:[1,108],81:[1,109]},{22:Y,50:ae,59:110,60:J,82:te,84:111,85:112,86:re,87:ee,88:Te,89:ue,90:De},{60:[1,122]},{13:G,55:123},e($,[2,72]),e($,[2,138]),{22:Y,50:ae,59:124,60:J,61:[1,125],82:te,84:111,85:112,86:re,87:ee,88:Te,89:ue,90:De},e(Ie,[2,74]),{16:39,17:40,19:126,60:p,86:k,100:E,102:A,103:N},e(F,[2,16]),e(F,[2,17]),e(F,[2,18]),{11:127,12:Ee,39:[2,36]},e(we,[2,9],{16:85,17:86,15:130,18:[1,129],60:p,86:k,100:E,102:A,103:N}),e(we,[2,10]),e(Me,[2,55],{11:131,12:Ee}),e(P,[2,7]),{9:[1,132]},e($e,[2,67]),{16:39,17:40,19:133,60:p,86:k,100:E,102:A,103:N},{13:[1,135],16:39,17:40,19:134,60:p,86:k,100:E,102:A,103:N},e(H,[2,82],{66:136,68:D,69:_,70:R,71:M,72:L}),e(H,[2,81]),{41:[1,137]},{23:99,24:97,30:98,38:22,40:138,42:n,43:23,48:s,54:h,56:d},{8:[1,139],41:[2,38]},{8:[1,140],41:[2,41]},{8:[1,141],41:[2,44]},e($,[2,48],{39:[1,142]}),{41:[1,143]},e($,[2,50]),{41:[2,61],45:144,51:Q},{47:[1,145]},{16:39,17:40,19:146,60:p,86:k,100:E,102:A,103:N},e(I,[2,91],{13:[1,147]}),e(I,[2,93],{13:[1,149],77:[1,148]}),e(I,[2,97],{13:[1,150],80:[1,151]}),{13:[1,152]},e(I,[2,105],{61:de}),e(rt,[2,107],{85:154,22:Y,50:ae,60:J,82:te,86:re,87:ee,88:Te,89:ue,90:De}),e(ke,[2,109]),e(ke,[2,111]),e(ke,[2,112]),e(ke,[2,113]),e(ke,[2,114]),e(ke,[2,115]),e(ke,[2,116]),e(ke,[2,117]),e(ke,[2,118]),e(ke,[2,119]),e(I,[2,106]),e($,[2,71]),e(I,[2,73],{61:de}),{60:[1,155]},e(F,[2,14]),{39:[2,37]},{13:[1,156]},{15:157,16:85,17:86,60:p,86:k,100:E,102:A,103:N},e(we,[2,12]),e(Me,[2,56]),{1:[2,4]},e($e,[2,69]),e($e,[2,68]),{16:39,17:40,19:158,60:p,86:k,100:E,102:A,103:N},e(H,[2,80]),e($,[2,34]),{41:[1,159]},{23:99,24:97,30:98,38:22,40:160,41:[2,39],42:n,43:23,48:s,54:h,56:d},{23:99,24:97,30:98,38:22,40:161,41:[2,42],42:n,43:23,48:s,54:h,56:d},{23:99,24:97,30:98,38:22,40:162,41:[2,45],42:n,43:23,48:s,54:h,56:d},{45:163,51:Q},e($,[2,49]),{41:[2,62]},e($,[2,52],{39:[1,164]}),e(I,[2,60]),e(I,[2,92]),e(I,[2,94]),e(I,[2,95],{77:[1,165]}),e(I,[2,98]),e(I,[2,99],{13:[1,166]}),e(I,[2,101],{13:[1,168],77:[1,167]}),{22:Y,50:ae,60:J,82:te,84:169,85:112,86:re,87:ee,88:Te,89:ue,90:De},e(ke,[2,110]),e(Ie,[2,75]),{14:[1,170]},e(we,[2,11]),e($e,[2,70]),e($,[2,35]),{41:[2,40]},{41:[2,43]},{41:[2,46]},{41:[1,171]},{41:[1,173],45:172,51:Q},e(I,[2,96]),e(I,[2,100]),e(I,[2,102]),e(I,[2,103],{77:[1,174]}),e(rt,[2,108],{85:154,22:Y,50:ae,60:J,82:te,86:re,87:ee,88:Te,89:ue,90:De}),e(Me,[2,8]),e($,[2,51]),{41:[1,175]},e($,[2,54]),e(I,[2,104]),e($,[2,53])],defaultActions:{2:[2,1],3:[2,2],4:[2,3],127:[2,37],132:[2,4],144:[2,62],160:[2,40],161:[2,43],162:[2,46]},parseError:o(function(Ue,ye){if(ye.recoverable)this.trace(Ue);else{var ve=new Error(Ue);throw ve.hash=ye,ve}},"parseError"),parse:o(function(Ue){var ye=this,ve=[0],ie=[],fe=[null],W=[],ce=this.table,K="",Re=0,xe=0,Oe=0,be=2,Be=1,Ae=W.slice.call(arguments,1),Ve=Object.create(this.lexer),Pe={yy:{}};for(var Ye in this.yy)Object.prototype.hasOwnProperty.call(this.yy,Ye)&&(Pe.yy[Ye]=this.yy[Ye]);Ve.setInput(Ue,Pe.yy),Pe.yy.lexer=Ve,Pe.yy.parser=this,typeof Ve.yylloc>"u"&&(Ve.yylloc={});var le=Ve.yylloc;W.push(le);var st=Ve.options&&Ve.options.ranges;typeof Pe.yy.parseError=="function"?this.parseError=Pe.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function me(dt){ve.length=ve.length-2*dt,fe.length=fe.length-dt,W.length=W.length-dt}o(me,"popStack");function ot(){var dt;return dt=ie.pop()||Ve.lex()||Be,typeof dt!="number"&&(dt instanceof Array&&(ie=dt,dt=ie.pop()),dt=ye.symbols_[dt]||dt),dt}o(ot,"lex");for(var kt,Gt,Tt,Et,yt,oe,ht={},gt,et,pe,Qe;;){if(Tt=ve[ve.length-1],this.defaultActions[Tt]?Et=this.defaultActions[Tt]:((kt===null||typeof kt>"u")&&(kt=ot()),Et=ce[Tt]&&ce[Tt][kt]),typeof Et>"u"||!Et.length||!Et[0]){var nt="";Qe=[];for(gt in ce[Tt])this.terminals_[gt]&&gt>be&&Qe.push("'"+this.terminals_[gt]+"'");Ve.showPosition?nt="Parse error on line "+(Re+1)+`:
+`+Ve.showPosition()+`
+Expecting `+Qe.join(", ")+", got '"+(this.terminals_[kt]||kt)+"'":nt="Parse error on line "+(Re+1)+": Unexpected "+(kt==Be?"end of input":"'"+(this.terminals_[kt]||kt)+"'"),this.parseError(nt,{text:Ve.match,token:this.terminals_[kt]||kt,line:Ve.yylineno,loc:le,expected:Qe})}if(Et[0]instanceof Array&&Et.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Tt+", token: "+kt);switch(Et[0]){case 1:ve.push(kt),fe.push(Ve.yytext),W.push(Ve.yylloc),ve.push(Et[1]),kt=null,Gt?(kt=Gt,Gt=null):(xe=Ve.yyleng,K=Ve.yytext,Re=Ve.yylineno,le=Ve.yylloc,Oe>0&&Oe--);break;case 2:if(et=this.productions_[Et[1]][1],ht.$=fe[fe.length-et],ht._$={first_line:W[W.length-(et||1)].first_line,last_line:W[W.length-1].last_line,first_column:W[W.length-(et||1)].first_column,last_column:W[W.length-1].last_column},st&&(ht._$.range=[W[W.length-(et||1)].range[0],W[W.length-1].range[1]]),oe=this.performAction.apply(ht,[K,xe,Re,Pe.yy,Et[1],fe,W].concat(Ae)),typeof oe<"u")return oe;et&&(ve=ve.slice(0,-1*et*2),fe=fe.slice(0,-1*et),W=W.slice(0,-1*et)),ve.push(this.productions_[Et[1]][0]),fe.push(ht.$),W.push(ht._$),pe=ce[ve[ve.length-2]][ve[ve.length-1]],ve.push(pe);break;case 3:return!0}}return!0},"parse")},He=(function(){var qe={EOF:1,parseError:o(function(ye,ve){if(this.yy.parser)this.yy.parser.parseError(ye,ve);else throw new Error(ye)},"parseError"),setInput:o(function(Ue,ye){return this.yy=ye||this.yy||{},this._input=Ue,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var Ue=this._input[0];this.yytext+=Ue,this.yyleng++,this.offset++,this.match+=Ue,this.matched+=Ue;var ye=Ue.match(/(?:\r\n?|\n).*/g);return ye?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Ue},"input"),unput:o(function(Ue){var ye=Ue.length,ve=Ue.split(/(?:\r\n?|\n)/g);this._input=Ue+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-ye),this.offset-=ye;var ie=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),ve.length-1&&(this.yylineno-=ve.length-1);var fe=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:ve?(ve.length===ie.length?this.yylloc.first_column:0)+ie[ie.length-ve.length].length-ve[0].length:this.yylloc.first_column-ye},this.options.ranges&&(this.yylloc.range=[fe[0],fe[0]+this.yyleng-ye]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(Ue){this.unput(this.match.slice(Ue))},"less"),pastInput:o(function(){var Ue=this.matched.substr(0,this.matched.length-this.match.length);return(Ue.length>20?"...":"")+Ue.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var Ue=this.match;return Ue.length<20&&(Ue+=this._input.substr(0,20-Ue.length)),(Ue.substr(0,20)+(Ue.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var Ue=this.pastInput(),ye=new Array(Ue.length+1).join("-");return Ue+this.upcomingInput()+`
+`+ye+"^"},"showPosition"),test_match:o(function(Ue,ye){var ve,ie,fe;if(this.options.backtrack_lexer&&(fe={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(fe.yylloc.range=this.yylloc.range.slice(0))),ie=Ue[0].match(/(?:\r\n?|\n).*/g),ie&&(this.yylineno+=ie.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:ie?ie[ie.length-1].length-ie[ie.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+Ue[0].length},this.yytext+=Ue[0],this.match+=Ue[0],this.matches=Ue,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(Ue[0].length),this.matched+=Ue[0],ve=this.performAction.call(this,this.yy,this,ye,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),ve)return ve;if(this._backtrack){for(var W in fe)this[W]=fe[W];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var Ue,ye,ve,ie;this._more||(this.yytext="",this.match="");for(var fe=this._currentRules(),W=0;W<fe.length;W++)if(ve=this._input.match(this.rules[fe[W]]),ve&&(!ye||ve[0].length>ye[0].length)){if(ye=ve,ie=W,this.options.backtrack_lexer){if(Ue=this.test_match(ve,fe[W]),Ue!==!1)return Ue;if(this._backtrack){ye=!1;continue}else return!1}else if(!this.options.flex)break}return ye?(Ue=this.test_match(ye,fe[ie]),Ue!==!1?Ue:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var ye=this.next();return ye||this.lex()},"lex"),begin:o(function(ye){this.conditionStack.push(ye)},"begin"),popState:o(function(){var ye=this.conditionStack.length-1;return ye>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(ye){return ye=this.conditionStack.length-1-Math.abs(ye||0),ye>=0?this.conditionStack[ye]:"INITIAL"},"topState"),pushState:o(function(ye){this.begin(ye)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(ye,ve,ie,fe){var W=fe;switch(ie){case 0:return 62;case 1:return 63;case 2:return 64;case 3:return 65;case 4:break;case 5:break;case 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36:return 8;case 37:break;case 38:return"EDGE_STATE";case 39:return this.begin("class"),48;break;case 40:return this.popState(),8;break;case 41:break;case 42:return this.popState(),this.popState(),41;break;case 43:return this.begin("class-body"),39;break;case 44:return this.popState(),41;break;case 45:return"EOF_IN_STRUCT";case 46:return"EDGE_STATE";case 47:return"OPEN_IN_STRUCT";case 48:break;case 49:return"MEMBER";case 50:return 83;case 51:return 75;case 52:return 76;case 53:return 78;case 54:return 54;case 55:return 56;case 56:return 46;case 57:return 47;case 58:return 81;case 59:this.popState();break;case 60:return"GENERICTYPE";case 61:this.begin("generic");break;case 62:this.popState();break;case 63:return"BQUOTE_STR";case 64:this.begin("bqstring");break;case 65:return 77;case 66:return 77;case 67:return 77;case 68:return 77;case 69:return 69;case 70:return 69;case 71:return 71;case 72:return 71;case 73:return 70;case 74:return 68;case 75:return 72;case 76:return 73;case 77:return 74;case 78:return 22;case 79:return 44;case 80:return 100;case 81:return 18;case 82:return"PLUS";case 83:return 87;case 84:return 61;case 85:return 89;case 86:return 89;case 87:return 90;case 88:return"EQUALS";case 89:return"EQUALS";case 90:return 60;case 91:return 12;case 92:return 14;case 93:return"PUNCTUATION";case 94:return 86;case 95:return 102;case 96:return 50;case 97:return 50;case 98:return 9}},"anonymous"),rules:[/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:%%(?!\{)*[^\n]*(\r?\n?)+)/,/^(?:%%[^\n]*(\r?\n)*)/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:classDiagram-v2\b)/,/^(?:classDiagram\b)/,/^(?:\[\*\])/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:["])/,/^(?:[^"]*)/,/^(?:["])/,/^(?:style\b)/,/^(?:classDef\b)/,/^(?:namespace\b)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:[{])/,/^(?:[}])/,/^(?:[}])/,/^(?:$)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:\[\*\])/,/^(?:class\b)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:[}])/,/^(?:[{])/,/^(?:[}])/,/^(?:$)/,/^(?:\[\*\])/,/^(?:[{])/,/^(?:[\n])/,/^(?:[^{}\n]*)/,/^(?:cssClass\b)/,/^(?:callback\b)/,/^(?:link\b)/,/^(?:click\b)/,/^(?:note 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,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},namespace:{rules:[26,29,30,31,32,33,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},"class-body":{rules:[26,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},class:{rules:[26,40,41,42,43,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},acc_descr_multiline:{rules:[11,12,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},acc_descr:{rules:[9,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},acc_title:{rules:[7,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},callback_args:{rules:[22,23,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},callback_name:{rules:[19,20,21,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},href:{rules:[26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},struct:{rules:[26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},generic:{rules:[26,50,51,52,53,54,55,56,57,58,59,60,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},bqstring:{rules:[26,50,51,52,53,54,55,56,57,58,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},string:{rules:[24,25,26,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,87,88,89,90,91,92,93,94,95,96,98],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,8,10,13,14,15,16,17,18,26,27,28,29,39,50,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],inclusive:!0}}};return 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+  fill: ${e.nodeBorder||e.classText};
+  stroke: none;
+  font-family: ${e.fontFamily};
+  font-size: 10px;
+
+  .title {
+    font-weight: bolder;
+  }
+
+}
+
+  .cluster-label text {
+    fill: ${e.titleColor};
+  }
+  .cluster-label span {
+    color: ${e.titleColor};
+  }
+  .cluster-label span p {
+    background-color: transparent;
+  }
+
+  .cluster rect {
+    fill: ${e.clusterBkg};
+    stroke: ${e.clusterBorder};
+    stroke-width: 1px;
+  }
+
+  .cluster text {
+    fill: ${e.titleColor};
+  }
+
+  .cluster span {
+    color: ${e.titleColor};
+  }
+
+.nodeLabel, .edgeLabel {
+  color: ${e.classText};
+}
+
+.noteLabel .nodeLabel, .noteLabel .edgeLabel {
+  color: ${e.noteTextColor};
+}
+.edgeLabel .label rect {
+  fill: ${e.mainBkg};
+}
+.label text {
+  fill: ${e.classText};
+}
+
+.labelBkg {
+  background: ${e.mainBkg};
+}
+.edgeLabel .label span {
+  background: ${e.mainBkg};
+}
+
+.classTitle {
+  font-weight: bolder;
+}
+.node rect,
+  .node circle,
+  .node ellipse,
+  .node polygon,
+  .node path {
+    fill: ${e.mainBkg};
+    stroke: ${e.nodeBorder};
+    stroke-width: ${e.strokeWidth};
+  }
+
+
+.divider {
+  stroke: ${e.nodeBorder};
+  stroke-width: 1;
+}
+
+g.clickable {
+  cursor: pointer;
+}
+
+g.classGroup rect {
+  fill: ${e.mainBkg};
+  stroke: ${e.nodeBorder};
+}
+
+g.classGroup line {
+  stroke: ${e.nodeBorder};
+  stroke-width: 1;
+}
+
+.classLabel .box {
+  stroke: none;
+  stroke-width: 0;
+  fill: ${e.mainBkg};
+  opacity: 0.5;
+}
+
+.classLabel .label {
+  fill: ${e.nodeBorder};
+  font-size: 10px;
+}
+
+.relation {
+  stroke: ${e.lineColor};
+  stroke-width: ${e.strokeWidth};
+  fill: none;
+}
+
+.dashed-line{
+  stroke-dasharray: 3;
+}
+
+.dotted-line{
+  stroke-dasharray: 1 2;
+}
+
+[id$="-compositionStart"], .composition {
+  fill: ${e.lineColor} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-compositionEnd"], .composition {
+  fill: ${e.lineColor} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-dependencyStart"], .dependency {
+  fill: ${e.lineColor} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-dependencyEnd"], .dependency {
+  fill: ${e.lineColor} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-extensionStart"], .extension {
+  fill: transparent !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-extensionEnd"], .extension {
+  fill: transparent !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-aggregationStart"], .aggregation {
+  fill: transparent !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-aggregationEnd"], .aggregation {
+  fill: transparent !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-lollipopStart"], .lollipop {
+  fill: ${e.mainBkg} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+[id$="-lollipopEnd"], .lollipop {
+  fill: ${e.mainBkg} !important;
+  stroke: ${e.lineColor} !important;
+  stroke-width: 1;
+}
+
+.edgeTerminals {
+  font-size: 11px;
+  line-height: initial;
+}
+
+.classTitleText {
+  text-anchor: middle;
+  font-size: 18px;
+  fill: ${e.textColor};
+}
+
+.edgeLabel[data-look="neo"] {
+  background-color: ${e.edgeLabelBackground};
+  p {
+    background-color: ${e.edgeLabelBackground};
+  }
+  rect {
+    opacity: 0.5;
+    background-color: ${e.edgeLabelBackground};
+    fill: ${e.edgeLabelBackground};
+  }
+  text-align: center;
+}
+  ${Vc()}
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n=this.startIdIfNeeded(t.id.trim()),a=this.startTypeIfNeeded(t.id.trim(),t.type),s=this.startIdIfNeeded(r.id.trim()),l=this.startTypeIfNeeded(r.id.trim(),r.type);this.addState(n,a,t.doc,t.description,t.note,t.classes,t.styles,t.textStyles),this.addState(s,l,r.doc,r.description,r.note,r.classes,r.styles,r.textStyles),this.currentDocument.relations.push({id1:n,id2:s,relationTitle:vt.sanitizeText(i,ge())})}addRelation(t,r,i){if(typeof t=="object"&&typeof r=="object")this.addRelationObjs(t,r,i);else if(typeof t=="string"&&typeof r=="string"){let n=this.startIdIfNeeded(t.trim()),a=this.startTypeIfNeeded(t),s=this.endIdIfNeeded(r.trim()),l=this.endTypeIfNeeded(r);this.addState(n,a),this.addState(s,l),this.currentDocument.relations.push({id1:n,id2:s,relationTitle:i?vt.sanitizeText(i,ge()):void 0})}}addDescription(t,r){let i=this.currentDocument.states.get(t),n=r.startsWith(":")?r.replace(":","").trim():r;i?.descriptions?.push(vt.sanitizeText(n,ge()))}cleanupLabel(t){return t.startsWith(":")?t.slice(2).trim():t.trim()}getDividerId(){return this.dividerCnt++,`divider-id-${this.dividerCnt}`}addStyleClass(t,r=""){this.classes.has(t)||this.classes.set(t,{id:t,styles:[],textStyles:[]});let i=this.classes.get(t);r&&i&&r.split(gs.STYLECLASS_SEP).forEach(n=>{let a=n.replace(/([^;]*);/,"$1").trim();if(RegExp(gs.COLOR_KEYWORD).exec(n)){let l=a.replace(gs.FILL_KEYWORD,gs.BG_FILL).replace(gs.COLOR_KEYWORD,gs.FILL_KEYWORD);i.textStyles.push(l)}i.styles.push(a)})}getClasses(){return this.classes}setCssClass(t,r){t.split(",").forEach(i=>{let n=this.getState(i);if(!n){let a=i.trim();this.addState(a),n=this.getState(a)}n?.classes?.push(r)})}setStyle(t,r){this.getState(t)?.styles?.push(r)}setTextStyle(t,r){this.getState(t)?.textStyles?.push(r)}getDirectionStatement(){return this.rootDoc.find(t=>t.stmt==="dir")}getDirection(){return this.getDirectionStatement()?.value??"TB"}setDirection(t){let r=this.getDirectionStatement();r?r.value=t:this.rootDoc.unshift({stmt:"dir",value:t})}trimColon(t){return t.startsWith(":")?t.slice(1).trim():t.trim()}getData(){let t=ge();return{nodes:this.nodes,edges:this.edges,other:{},config:t,direction:RW(this.getRootDocV2())}}getConfig(){return ge().state}}});var qCt,o_,MW=z(()=>{"use strict";qCt=o(e=>`
+defs [id$="-barbEnd"] {
+    fill: ${e.transitionColor};
+    stroke: ${e.transitionColor};
+  }
+g.stateGroup text {
+  fill: ${e.nodeBorder};
+  stroke: none;
+  font-size: 10px;
+}
+g.stateGroup text {
+  fill: ${e.textColor};
+  stroke: none;
+  font-size: 10px;
+
+}
+g.stateGroup .state-title {
+  font-weight: bolder;
+  fill: ${e.stateLabelColor};
+}
+
+g.stateGroup rect {
+  fill: ${e.mainBkg};
+  stroke: ${e.nodeBorder};
+}
+
+g.stateGroup line {
+  stroke: ${e.lineColor};
+  stroke-width: ${e.strokeWidth||1};
+}
+
+.transition {
+  stroke: ${e.transitionColor};
+  stroke-width: ${e.strokeWidth||1};
+  fill: none;
+}
+
+.stateGroup .composit {
+  fill: ${e.background};
+  border-bottom: 1px
+}
+
+.stateGroup .alt-composit {
+  fill: #e0e0e0;
+  border-bottom: 1px
+}
+
+.state-note {
+  stroke: ${e.noteBorderColor};
+  fill: ${e.noteBkgColor};
+
+  text {
+    fill: ${e.noteTextColor};
+    stroke: none;
+    font-size: 10px;
+  }
+}
+
+.stateLabel .box {
+  stroke: none;
+  stroke-width: 0;
+  fill: ${e.mainBkg};
+  opacity: 0.5;
+}
+
+.edgeLabel .label rect {
+  fill: ${e.labelBackgroundColor};
+  opacity: 0.5;
+}
+.edgeLabel {
+  background-color: ${e.edgeLabelBackground};
+  p {
+    background-color: ${e.edgeLabelBackground};
+  }
+  rect {
+    opacity: 0.5;
+    background-color: ${e.edgeLabelBackground};
+    fill: ${e.edgeLabelBackground};
+  }
+  text-align: center;
+}
+.edgeLabel .label text {
+  fill: ${e.transitionLabelColor||e.tertiaryTextColor};
+}
+.label div .edgeLabel {
+  color: ${e.transitionLabelColor||e.tertiaryTextColor};
+}
+
+.stateLabel text {
+  fill: ${e.stateLabelColor};
+  font-size: 10px;
+  font-weight: bold;
+}
+
+.node circle.state-start {
+  fill: ${e.specialStateColor};
+  stroke: ${e.specialStateColor};
+}
+
+.node .fork-join {
+  fill: ${e.specialStateColor};
+  stroke: ${e.specialStateColor};
+}
+
+.node circle.state-end {
+  fill: ${e.innerEndBackground};
+  stroke: ${e.background};
+  stroke-width: 1.5
+}
+.end-state-inner {
+  fill: ${e.compositeBackground||e.background};
+  // stroke: ${e.background};
+  stroke-width: 1.5
+}
+
+.node rect {
+  fill: ${e.stateBkg||e.mainBkg};
+  stroke: ${e.stateBorder||e.nodeBorder};
+  stroke-width: ${e.strokeWidth||1}px;
+}
+.node polygon {
+  fill: ${e.mainBkg};
+  stroke: ${e.stateBorder||e.nodeBorder};;
+  stroke-width: ${e.strokeWidth||1}px;
+}
+[id$="-barbEnd"] {
+  fill: ${e.lineColor};
+}
+
+.statediagram-cluster rect {
+  fill: ${e.compositeTitleBackground};
+  stroke: ${e.stateBorder||e.nodeBorder};
+  stroke-width: ${e.strokeWidth||1}px;
+}
+
+.cluster-label, .nodeLabel {
+  color: ${e.stateLabelColor};
+  // line-height: 1;
+}
+
+.statediagram-cluster rect.outer {
+  rx: 5px;
+  ry: 5px;
+}
+.statediagram-state .divider {
+  stroke: ${e.stateBorder||e.nodeBorder};
+}
+
+.statediagram-state .title-state {
+  rx: 5px;
+  ry: 5px;
+}
+.statediagram-cluster.statediagram-cluster .inner {
+  fill: ${e.compositeBackground||e.background};
+}
+.statediagram-cluster.statediagram-cluster-alt .inner {
+  fill: ${e.altBackground?e.altBackground:"#efefef"};
+}
+
+.statediagram-cluster .inner {
+  rx:0;
+  ry:0;
+}
+
+.statediagram-state rect.basic {
+  rx: 5px;
+  ry: 5px;
+}
+.statediagram-state rect.divider {
+  stroke-dasharray: 10,10;
+  fill: ${e.altBackground?e.altBackground:"#efefef"};
+}
+
+.note-edge {
+  stroke-dasharray: 5;
+}
+
+.statediagram-note rect {
+  fill: ${e.noteBkgColor};
+  stroke: ${e.noteBorderColor};
+  stroke-width: 1px;
+  rx: 0;
+  ry: 0;
+}
+.statediagram-note rect {
+  fill: ${e.noteBkgColor};
+  stroke: ${e.noteBorderColor};
+  stroke-width: 1px;
+  rx: 0;
+  ry: 0;
+}
+
+.statediagram-note text {
+  fill: ${e.noteTextColor};
+}
+
+.statediagram-note .nodeLabel {
+  color: ${e.noteTextColor};
+}
+.statediagram .edgeLabel {
+  color: red; // ${e.noteTextColor};
+}
+
+[id$="-dependencyStart"], [id$="-dependencyEnd"] {
+  fill: ${e.lineColor};
+  stroke: ${e.lineColor};
+  stroke-width: ${e.strokeWidth||1};
+}
+
+.statediagramTitleText {
+  text-anchor: middle;
+  font-size: 18px;
+  fill: ${e.textColor};
+}
+
+[data-look="neo"].statediagram-cluster rect {
+  fill: ${e.mainBkg};
+  stroke: ${e.useGradient?"url("+e.svgId+"-gradient)":e.stateBorder||e.nodeBorder};
+  stroke-width: ${e.strokeWidth??1};
+}
+[data-look="neo"].statediagram-cluster rect.outer {
+  rx: ${e.radius}px;
+  ry: ${e.radius}px;
+  filter: ${e.dropShadow?e.dropShadow.replace("url(#drop-shadow)",`url(${e.svgId}-drop-shadow)`):"none"}
+}
+`,"getStyles"),o_=qCt});var UCt,HCt,YCt,jCt,n_e,XCt,KCt,ZCt,QCt,NW,i_e,a_e,s_e=z(()=>{"use strict";Rr();s_();Xt();Ir();Ut();St();UCt=o(e=>e.append("circle").attr("class","start-state").attr("r",ge().state.sizeUnit).attr("cx",ge().state.padding+ge().state.sizeUnit).attr("cy",ge().state.padding+ge().state.sizeUnit),"drawStartState"),HCt=o(e=>e.append("line").style("stroke","grey").style("stroke-dasharray","3").attr("x1",ge().state.textHeight).attr("class","divider").attr("x2",ge().state.textHeight*2).attr("y1",0).attr("y2",0),"drawDivider"),YCt=o((e,t)=>{let r=e.append("text").attr("x",2*ge().state.padding).attr("y",ge().state.textHeight+2*ge().state.padding).attr("font-size",ge().state.fontSize).attr("class","state-title").text(t.id),i=r.node().getBBox();return 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e.insert("rect",":first-child").attr("x",p).attr("y",g).attr("class",r?"alt-composit":"composit").attr("width",f).attr("height",m.height+ge().state.textHeight+ge().state.titleShift+1).attr("rx","0"),u.attr("x",p+i),d<=s&&u.attr("x",l+(f-n)/2-d/2+i),e.insert("rect",":first-child").attr("x",p).attr("y",ge().state.titleShift-ge().state.textHeight-ge().state.padding).attr("width",f).attr("height",ge().state.textHeight*3).attr("rx",ge().state.radius),e.insert("rect",":first-child").attr("x",p).attr("y",ge().state.titleShift-ge().state.textHeight-ge().state.padding).attr("width",f).attr("height",m.height+3+2*ge().state.textHeight).attr("rx",ge().state.radius),e},"addTitleAndBox"),XCt=o(e=>(e.append("circle").attr("class","end-state-outer").attr("r",ge().state.sizeUnit+ge().state.miniPadding).attr("cx",ge().state.padding+ge().state.sizeUnit+ge().state.miniPadding).attr("cy",ge().state.padding+ge().state.sizeUnit+ge().state.miniPadding),e.append("circle").attr("class","end-state-inner").attr("r",ge().state.sizeUnit).attr("cx",ge().state.padding+ge().state.sizeUnit+2).attr("cy",ge().state.padding+ge().state.sizeUnit+2)),"drawEndState"),KCt=o((e,t)=>{let 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r.attr("height",a+2*ge().state.noteMargin),r.attr("width",n+ge().state.noteMargin*2),r},"drawNote"),NW=o(function(e,t){let r=t.id,i={id:r,label:t.id,width:0,height:0},n=e.append("g").attr("id",r).attr("class","stateGroup");t.type==="start"&&UCt(n),t.type==="end"&&XCt(n),(t.type==="fork"||t.type==="join")&&KCt(n,t),t.type==="note"&&QCt(t.note.text,n),t.type==="divider"&&HCt(n),t.type==="default"&&t.descriptions.length===0&&YCt(n,t),t.type==="default"&&t.descriptions.length>0&&jCt(n,t);let a=n.node().getBBox();return i.width=a.width+2*ge().state.padding,i.height=a.height+2*ge().state.padding,i},"drawState"),i_e=0,a_e=o(function(e,t,r){let i=o(function(u){switch(u){case ol.relationType.AGGREGATION:return"aggregation";case ol.relationType.EXTENSION:return"extension";case ol.relationType.COMPOSITION:return"composition";case ol.relationType.DEPENDENCY:return"dependency"}},"getRelationType");t.points=t.points.filter(u=>!Number.isNaN(u.y));let n=t.points,a=_l().x(function(u){return u.x}).y(function(u){return u.y}).curve(Dl),s=e.append("path").attr("d",a(n)).attr("id","edge"+i_e).attr("class","transition"),l="";if(ge().state.arrowMarkerAbsolute&&(l=Hf(!0)),s.attr("marker-end","url("+l+"#"+i(ol.relationType.DEPENDENCY)+"End)"),r.title!==void 0){let u=e.append("g").attr("class","stateLabel"),{x:h,y:d}=Zt.calcLabelPosition(t.points),f=vt.getRows(r.title),p=0,m=[],g=0,y=0;for(let b=0;b<=f.length;b++){let T=u.append("text").attr("text-anchor","middle").text(f[b]).attr("x",h).attr("y",d+p),w=T.node().getBBox();g=Math.max(g,w.width),y=Math.min(y,w.x),Z.info(w.x,h,d+p),p===0&&(p=T.node().getBBox().height,Z.info("Title height",p,d)),m.push(T)}let v=p*f.length;if(f.length>1){let b=(f.length-1)*p*.5;m.forEach((T,w)=>T.attr("y",d+w*p-b)),v=p*f.length}let 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+  ${Vc()}
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+
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+
+    .section-${l-1} line {
+      stroke: ${e["cScaleInv"+l]};
+      stroke-width: 3;
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+
+    .lineWrapper line {
+      stroke: ${e.nodeBorder};
+      stroke-width:${e.strokeWidth}
+    }
+
+    .disabled,
+    .disabled circle,
+    .disabled text {
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+    }
+
+    .disabled text {
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+    .section-${r-1} text {
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+    .node-icon-${r-1} {
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+    .edge-depth-${r-1}{
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+    .section-${r-1} line {
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+
+    .lineWrapper line{
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+
+    .disabled, .disabled circle, .disabled text {
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+    .disabled text {
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+  .section-root text {
+    fill: ${e.gitBranchLabel0};
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+  .icon-container {
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+    display: flex;
+    justify-content: center;
+    align-items: center;
+  }
+  .edge {
+    fill: none;
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+  .eventWrapper  {
+   filter: brightness(120%);
+  }
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+
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+      stroke: ${t==="redux"||t==="redux-dark"?e.nodeBorder:e["cScale"+n]};
+      stroke-width: ${e.strokeWidth??2}px;
+    }
+    [data-look="neo"].section-edge-${n-1}{
+      stroke: ${t?.includes("redux")||t==="neo-dark"?e.nodeBorder:e["cScale"+n]};
+    }
+    [data-look="neo"].mindmap-node.section-${n-1} text {
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+    }
+    `}return i},"genSections"),ywt=o((e,t,r)=>{let i="";for(let n=0;n<e;n++)i+=`
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+      stroke: url(${t}-gradient);
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+    .section-${n-1} line {
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+  ${gwt(e)}
+  .section-root rect, .section-root path, .section-root circle, .section-root polygon  {
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+  }
+  .section-root text {
+    fill: ${e.gitBranchLabel0};
+  }
+  .section-root span {
+    color: ${t?.includes("redux")?e.nodeBorder:e.gitBranchLabel0};
+  }
+  .icon-container {
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+    display: flex;
+    justify-content: center;
+    align-items: center;
+  }
+  .edge {
+    fill: none;
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+  .mindmap-node-label {
+    dy: 1em;
+    alignment-baseline: middle;
+    text-anchor: middle;
+    dominant-baseline: middle;
+    text-align: center;
+  }
+  [data-look="neo"].mindmap-node  {
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+  [data-look="neo"].mindmap-node.section-root rect, [data-look="neo"].mindmap-node.section-root path, [data-look="neo"].mindmap-node.section-root circle, [data-look="neo"].mindmap-node.section-root polygon  {
+    fill: ${t?.includes("redux")?e.mainBkg:e.git0};
+  }
+  [data-look="neo"].mindmap-node.section-root .text-inner-tspan {
+    fill:  ${t?.includes("redux")?e.nodeBorder:e["cScaleLabel"+(t==="neutral"?1:0)]};
+  }
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+Expecting `+Me.join(", ")+", got '"+(this.terminals_[J]||J)+"'":$e="Parse error on line "+(L+1)+": Unexpected "+(J==G?"end of input":"'"+(this.terminals_[J]||J)+"'"),this.parseError($e,{text:V.match,token:this.terminals_[J]||J,line:V.yylineno,loc:U,expected:Me})}if(ee[0]instanceof Array&&ee.length>1)throw new Error("Parse Error: multiple actions possible at state: "+re+", token: "+J);switch(ee[0]){case 1:P.push(J),D.push(V.yytext),_.push(V.yylloc),P.push(ee[1]),J=null,te?(J=te,te=null):(B=V.yyleng,M=V.yytext,L=V.yylineno,U=V.yylloc,O>0&&O--);break;case 2:if(Ee=this.productions_[ee[1]][1],De.$=D[D.length-Ee],De._$={first_line:_[_.length-(Ee||1)].first_line,last_line:_[_.length-1].last_line,first_column:_[_.length-(Ee||1)].first_column,last_column:_[_.length-1].last_column},Q&&(De._$.range=[_[_.length-(Ee||1)].range[0],_[_.length-1].range[1]]),ue=this.performAction.apply(De,[M,B,L,H.yy,ee[1],D,_].concat(F)),typeof ue<"u")return ue;Ee&&(P=P.slice(0,-1*Ee*2),D=D.slice(0,-1*Ee),_=_.slice(0,-1*Ee)),P.push(this.productions_[ee[1]][0]),D.push(De.$),_.push(De._$),we=R[P[P.length-2]][P[P.length-1]],P.push(we);break;case 3:return!0}}return!0},"parse")},C=(function(){var E={EOF:1,parseError:o(function(N,P){if(this.yy.parser)this.yy.parser.parseError(N,P);else throw new Error(N)},"parseError"),setInput:o(function(A,N){return this.yy=N||this.yy||{},this._input=A,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var A=this._input[0];this.yytext+=A,this.yyleng++,this.offset++,this.match+=A,this.matched+=A;var N=A.match(/(?:\r\n?|\n).*/g);return N?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),A},"input"),unput:o(function(A){var N=A.length,P=A.split(/(?:\r\n?|\n)/g);this._input=A+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-N),this.offset-=N;var I=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),P.length-1&&(this.yylineno-=P.length-1);var D=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:P?(P.length===I.length?this.yylloc.first_column:0)+I[I.length-P.length].length-P[0].length:this.yylloc.first_column-N},this.options.ranges&&(this.yylloc.range=[D[0],D[0]+this.yyleng-N]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(A){this.unput(this.match.slice(A))},"less"),pastInput:o(function(){var A=this.matched.substr(0,this.matched.length-this.match.length);return(A.length>20?"...":"")+A.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var A=this.match;return A.length<20&&(A+=this._input.substr(0,20-A.length)),(A.substr(0,20)+(A.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var A=this.pastInput(),N=new Array(A.length+1).join("-");return A+this.upcomingInput()+`
+`+N+"^"},"showPosition"),test_match:o(function(A,N){var P,I,D;if(this.options.backtrack_lexer&&(D={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(D.yylloc.range=this.yylloc.range.slice(0))),I=A[0].match(/(?:\r\n?|\n).*/g),I&&(this.yylineno+=I.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:I?I[I.length-1].length-I[I.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+A[0].length},this.yytext+=A[0],this.match+=A[0],this.matches=A,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(A[0].length),this.matched+=A[0],P=this.performAction.call(this,this.yy,this,N,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),P)return P;if(this._backtrack){for(var _ in D)this[_]=D[_];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var A,N,P,I;this._more||(this.yytext="",this.match="");for(var D=this._currentRules(),_=0;_<D.length;_++)if(P=this._input.match(this.rules[D[_]]),P&&(!N||P[0].length>N[0].length)){if(N=P,I=_,this.options.backtrack_lexer){if(A=this.test_match(P,D[_]),A!==!1)return A;if(this._backtrack){N=!1;continue}else return!1}else if(!this.options.flex)break}return N?(A=this.test_match(N,D[I]),A!==!1?A:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var N=this.next();return N||this.lex()},"lex"),begin:o(function(N){this.conditionStack.push(N)},"begin"),popState:o(function(){var N=this.conditionStack.length-1;return N>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(N){return N=this.conditionStack.length-1-Math.abs(N||0),N>=0?this.conditionStack[N]:"INITIAL"},"topState"),pushState:o(function(N){this.begin(N)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(N,P,I,D){var _=D;switch(I){case 0:return this.pushState("shapeData"),P.yytext="",24;break;case 1:return this.pushState("shapeDataStr"),24;break;case 2:return this.popState(),24;break;case 3:let R=/\n\s*/g;return P.yytext=P.yytext.replace(R,"<br/>"),24;break;case 4:return 24;case 5:this.popState();break;case 6:return N.getLogger().trace("Found comment",P.yytext),6;break;case 7:return 8;case 8:this.begin("CLASS");break;case 9:return this.popState(),17;break;case 10:this.popState();break;case 11:N.getLogger().trace("Begin icon"),this.begin("ICON");break;case 12:return N.getLogger().trace("SPACELINE"),6;break;case 13:return 7;case 14:return 16;case 15:N.getLogger().trace("end icon"),this.popState();break;case 16:return N.getLogger().trace("Exploding node"),this.begin("NODE"),20;break;case 17:return N.getLogger().trace("Cloud"),this.begin("NODE"),20;break;case 18:return N.getLogger().trace("Explosion Bang"),this.begin("NODE"),20;break;case 19:return N.getLogger().trace("Cloud Bang"),this.begin("NODE"),20;break;case 20:return this.begin("NODE"),20;break;case 21:return this.begin("NODE"),20;break;case 22:return this.begin("NODE"),20;break;case 23:return this.begin("NODE"),20;break;case 24:return 13;case 25:return 23;case 26:return 11;case 27:this.begin("NSTR2");break;case 28:return"NODE_DESCR";case 29:this.popState();break;case 30:N.getLogger().trace("Starting NSTR"),this.begin("NSTR");break;case 31:return N.getLogger().trace("description:",P.yytext),"NODE_DESCR";break;case 32:this.popState();break;case 33:return this.popState(),N.getLogger().trace("node end ))"),"NODE_DEND";break;case 34:return this.popState(),N.getLogger().trace("node end )"),"NODE_DEND";break;case 35:return this.popState(),N.getLogger().trace("node end ...",P.yytext),"NODE_DEND";break;case 36:return this.popState(),N.getLogger().trace("node end (("),"NODE_DEND";break;case 37:return this.popState(),N.getLogger().trace("node end (-"),"NODE_DEND";break;case 38:return this.popState(),N.getLogger().trace("node end (-"),"NODE_DEND";break;case 39:return this.popState(),N.getLogger().trace("node end (("),"NODE_DEND";break;case 40:return this.popState(),N.getLogger().trace("node end (("),"NODE_DEND";break;case 41:return N.getLogger().trace("Long description:",P.yytext),21;break;case 42:return N.getLogger().trace("Long description:",P.yytext),21;break}},"anonymous"),rules:[/^(?:@\{)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^\"]+)/i,/^(?:[^}^"]+)/i,/^(?:\})/i,/^(?:\s*%%.*)/i,/^(?:kanban\b)/i,/^(?::::)/i,/^(?:.+)/i,/^(?:\n)/i,/^(?:::icon\()/i,/^(?:[\s]+[\n])/i,/^(?:[\n]+)/i,/^(?:[^\)]+)/i,/^(?:\))/i,/^(?:-\))/i,/^(?:\(-)/i,/^(?:\)\))/i,/^(?:\))/i,/^(?:\(\()/i,/^(?:\{\{)/i,/^(?:\()/i,/^(?:\[)/i,/^(?:[\s]+)/i,/^(?:[^\(\[\n\)\{\}@]+)/i,/^(?:$)/i,/^(?:["][`])/i,/^(?:[^`"]+)/i,/^(?:[`]["])/i,/^(?:["])/i,/^(?:[^"]+)/i,/^(?:["])/i,/^(?:[\)]\))/i,/^(?:[\)])/i,/^(?:[\]])/i,/^(?:\}\})/i,/^(?:\(-)/i,/^(?:-\))/i,/^(?:\(\()/i,/^(?:\()/i,/^(?:[^\)\]\(\}]+)/i,/^(?:.+(?!\(\())/i],conditions:{shapeDataEndBracket:{rules:[],inclusive:!1},shapeDataStr:{rules:[2,3],inclusive:!1},shapeData:{rules:[1,4,5],inclusive:!1},CLASS:{rules:[9,10],inclusive:!1},ICON:{rules:[14,15],inclusive:!1},NSTR2:{rules:[28,29],inclusive:!1},NSTR:{rules:[31,32],inclusive:!1},NODE:{rules:[27,30,33,34,35,36,37,38,39,40,41,42],inclusive:!1},INITIAL:{rules:[0,6,7,8,11,12,13,16,17,18,19,20,21,22,23,24,25,26],inclusive:!0}}};return E})();w.lexer=C;function k(){this.yy={}}return o(k,"Parser"),k.prototype=w,w.Parser=k,new k})();eq.parser=eq;F7e=eq});var cl,rq,tq,iq,kwt,wwt,z7e,Swt,Ewt,Xn,Awt,_wt,Dwt,Rwt,Lwt,Mwt,Nwt,G7e,V7e=z(()=>{"use strict";Ut();Ir();St();Bn();Wx();cl=[],rq=[],tq=0,iq={},kwt=o(()=>{cl=[],rq=[],tq=0,iq={}},"clear"),wwt=o(e=>{if(cl.length===0)return null;let t=cl[0].level,r=null;for(let i=cl.length-1;i>=0;i--)if(cl[i].level===t&&!r&&(r=cl[i]),cl[i].level<t)throw new Error('Items without section detected, found section ("'+cl[i].label+'")');return e===r?.level?null:r},"getSection"),z7e=o(function(){return rq},"getSections"),Swt=o(function(){let e=[],t=[],r=z7e(),i=ge();for(let n of r){let a={id:n.id,label:fr(n.label??"",i),labelType:"markdown",isGroup:!0,ticket:n.ticket,shape:"kanbanSection",level:n.level,look:i.look};t.push(a);let s=cl.filter(l=>l.parentId===n.id);for(let l of s){let u={id:l.id,parentId:n.id,label:fr(l.label??"",i),labelType:"markdown",isGroup:!1,ticket:l?.ticket,priority:l?.priority,assigned:l?.assigned,icon:l?.icon,shape:"kanbanItem",level:l.level,rx:5,ry:5,cssStyles:["text-align: left"]};t.push(u)}}return{nodes:t,edges:e,other:{},config:ge()}},"getData"),Ewt=o((e,t,r,i,n)=>{let a=ge(),s=a.mindmap?.padding??or.mindmap.padding;switch(i){case Xn.ROUNDED_RECT:case Xn.RECT:case Xn.HEXAGON:s*=2}let l={id:fr(t,a)||"kbn"+tq++,level:e,label:fr(r,a),width:a.mindmap?.maxNodeWidth??or.mindmap.maxNodeWidth,padding:s,isGroup:!1};if(n!==void 0){let h;n.includes(`
+`)?h=n+`
+`:h=`{
+`+n+`
+}`;let d=ld(h,{schema:od});if(d.shape&&(d.shape!==d.shape.toLowerCase()||d.shape.includes("_")))throw new Error(`No such shape: ${d.shape}. Shape names should be lowercase.`);d?.shape&&d.shape==="kanbanItem"&&(l.shape=d?.shape),d?.label&&(l.label=d?.label),d?.icon&&(l.icon=d?.icon.toString()),d?.assigned&&(l.assigned=d?.assigned.toString()),d?.ticket&&(l.ticket=d?.ticket.toString()),d?.priority&&(l.priority=d?.priority)}let u=wwt(e);u?l.parentId=u.id||"kbn"+tq++:rq.push(l),cl.push(l)},"addNode"),Xn={DEFAULT:0,NO_BORDER:0,ROUNDED_RECT:1,RECT:2,CIRCLE:3,CLOUD:4,BANG:5,HEXAGON:6},Awt=o((e,t)=>{switch(Z.debug("In get type",e,t),e){case"[":return Xn.RECT;case"(":return t===")"?Xn.ROUNDED_RECT:Xn.CLOUD;case"((":return Xn.CIRCLE;case")":return Xn.CLOUD;case"))":return Xn.BANG;case"{{":return Xn.HEXAGON;default:return Xn.DEFAULT}},"getType"),_wt=o((e,t)=>{iq[e]=t},"setElementForId"),Dwt=o(e=>{if(!e)return;let t=ge(),r=cl[cl.length-1];e.icon&&(r.icon=fr(e.icon,t)),e.class&&(r.cssClasses=fr(e.class,t))},"decorateNode"),Rwt=o(e=>{switch(e){case Xn.DEFAULT:return"no-border";case Xn.RECT:return"rect";case Xn.ROUNDED_RECT:return"rounded-rect";case Xn.CIRCLE:return"circle";case Xn.CLOUD:return"cloud";case Xn.BANG:return"bang";case Xn.HEXAGON:return"hexgon";default:return"no-border"}},"type2Str"),Lwt=o(()=>Z,"getLogger"),Mwt=o(e=>iq[e],"getElementById"),Nwt={clear:kwt,addNode:Ewt,getSections:z7e,getData:Swt,nodeType:Xn,getType:Awt,setElementForId:_wt,decorateNode:Dwt,type2Str:Rwt,getLogger:Lwt,getElementById:Mwt},G7e=Nwt});var Iwt,W7e,q7e=z(()=>{"use strict";Ut();St();ps();Di();Bn();W4();J4();Iwt=o(async(e,t,r,i)=>{Z.debug(`Rendering kanban diagram
+`+e);let a=i.db.getData(),s=ge();s.htmlLabels=!1;let l=bi(t);for(let v of a.nodes)v.domId=`${t}-${v.id}`;let u=l.append("g");u.attr("class","sections");let h=l.append("g");h.attr("class","items");let d=a.nodes.filter(v=>v.isGroup),f=0,p=10,m=[],g=25;for(let v of d){let x=s?.kanban?.sectionWidth||200;f=f+1,v.x=x*f+(f-1)*p/2,v.width=x,v.y=0,v.height=x*3,v.rx=5,v.ry=5,v.cssClasses=v.cssClasses+" section-"+f;let b=await wg(u,v);g=Math.max(g,b?.labelBBox?.height),m.push(b)}let y=0;for(let v of d){let x=m[y];y=y+1;let b=s?.kanban?.sectionWidth||200,T=-b*3/2+g,w=T,C=a.nodes.filter(A=>A.parentId===v.id);for(let A of C){if(A.isGroup)throw new Error("Groups within groups are not allowed in Kanban diagrams");A.x=v.x,A.width=b-1.5*p;let P=(await Sg(h,A,{config:s})).node().getBBox();A.y=w+P.height/2,await nb(A),w=A.y+P.height/2+p/2}let k=x.cluster.select("rect"),E=Math.max(w-T+3*p,50)+(g-25);k.attr("height",E)}Io(void 0,l,s.mindmap?.padding??or.kanban.padding,s.mindmap?.useMaxWidth??or.kanban.useMaxWidth)},"draw"),W7e={draw:Iwt}});var Owt,Pwt,U7e,H7e=z(()=>{"use strict";Cn();k1();Owt=o(e=>{let t="";for(let i=0;i<e.THEME_COLOR_LIMIT;i++)e["lineColor"+i]=e["lineColor"+i]||e["cScaleInv"+i],ri(e["lineColor"+i])?e["lineColor"+i]=je(e["lineColor"+i],20):e["lineColor"+i]=Xe(e["lineColor"+i],20);let r=o((i,n)=>e.darkMode?Xe(i,n):je(i,n),"adjuster");for(let i=0;i<e.THEME_COLOR_LIMIT;i++){let n=""+(17-3*i);t+=`
+    .section-${i-1} rect, .section-${i-1} path, .section-${i-1} circle, .section-${i-1} polygon, .section-${i-1} path  {
+      fill: ${r(e["cScale"+i],10)};
+      stroke: ${r(e["cScale"+i],10)};
+
+    }
+    .section-${i-1} text {
+     fill: ${e["cScaleLabel"+i]};
+    }
+    .node-icon-${i-1} {
+      font-size: 40px;
+      color: ${e["cScaleLabel"+i]};
+    }
+    .section-edge-${i-1}{
+      stroke: ${e["cScale"+i]};
+    }
+    .edge-depth-${i-1}{
+      stroke-width: ${n};
+    }
+    .section-${i-1} line {
+      stroke: ${e["cScaleInv"+i]} ;
+      stroke-width: 3;
+    }
+
+    .disabled, .disabled circle, .disabled text {
+      fill: lightgray;
+    }
+    .disabled text {
+      fill: #efefef;
+    }
+
+  .node rect,
+  .node circle,
+  .node ellipse,
+  .node polygon,
+  .node path {
+    fill: ${e.background};
+    stroke: ${e.nodeBorder};
+    stroke-width: 1px;
+  }
+
+  .kanban-ticket-link {
+    fill: ${e.background};
+    stroke: ${e.nodeBorder};
+    text-decoration: underline;
+  }
+    `}return t},"genSections"),Pwt=o(e=>`
+  .edge {
+    stroke-width: 3;
+  }
+  ${Owt(e)}
+  .section-root rect, .section-root path, .section-root circle, .section-root polygon  {
+    fill: ${e.git0};
+  }
+  .section-root text {
+    fill: ${e.gitBranchLabel0};
+  }
+  .icon-container {
+    height:100%;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+  }
+  .edge {
+    fill: none;
+  }
+  .cluster-label, .label {
+    color: ${e.textColor};
+    fill: ${e.textColor};
+    }
+  .kanban-label {
+    dy: 1em;
+    alignment-baseline: middle;
+    text-anchor: middle;
+    dominant-baseline: middle;
+    text-align: center;
+  }
+    ${Vc()}
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+  /* Foreground label text */
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+	.packetByte {
+		font-size: ${t.byteFontSize};
+	}
+	.packetByte.start {
+		fill: ${t.startByteColor};
+	}
+	.packetByte.end {
+		fill: ${t.endByteColor};
+	}
+	.packetLabel {
+		fill: ${t.labelColor};
+		font-size: ${t.labelFontSize};
+	}
+	.packetTitle {
+		fill: ${t.titleColor};
+		font-size: ${t.titleFontSize};
+	}
+	.packetBlock {
+		stroke: ${t.blockStrokeColor};
+		stroke-width: ${t.blockStrokeWidth};
+		fill: ${t.blockFillColor};
+	}
+	`},"styles")});var FDe={};xr(FDe,{diagram:()=>b4t});var b4t,$De=z(()=>{"use strict";vq();NDe();ODe();BDe();b4t={parser:xq,get db(){return new My},renderer:IDe,styles:PDe}});var Ny,VDe,Cm,k4t,w4t,WDe,S4t,E4t,A4t,_4t,D4t,R4t,L4t,km,bq=z(()=>{"use strict";br();Bn();Xt();Ci();Ny={showLegend:!0,ticks:5,max:null,min:0,graticule:"circle"},VDe={axes:[],curves:[],options:Ny},Cm=structuredClone(VDe),k4t=or.radar,w4t=o(()=>Gr({...k4t,...Nt().radar}),"getConfig"),WDe=o(()=>Cm.axes,"getAxes"),S4t=o(()=>Cm.curves,"getCurves"),E4t=o(()=>Cm.options,"getOptions"),A4t=o(e=>{Cm.axes=e.map(t=>({name:t.name,label:t.label??t.name}))},"setAxes"),_4t=o(e=>{Cm.curves=e.map(t=>({name:t.name,label:t.label??t.name,entries:D4t(t.entries)}))},"setCurves"),D4t=o(e=>{if(e[0].axis==null)return e.map(r=>r.value);let t=WDe();if(t.length===0)throw new Error("Axes must be populated before curves for reference entries");return t.map(r=>{let i=e.find(n=>n.axis?.$refText===r.name);if(i===void 0)throw new Error("Missing entry for axis "+r.label);return i.value})},"computeCurveEntries"),R4t=o(e=>{let t=e.reduce((r,i)=>(r[i.name]=i,r),{});Cm.options={showLegend:t.showLegend?.value??Ny.showLegend,ticks:t.ticks?.value??Ny.ticks,max:t.max?.value??Ny.max,min:t.min?.value??Ny.min,graticule:t.graticule?.value??Ny.graticule}},"setOptions"),L4t=o(()=>{gr(),Cm=structuredClone(VDe)},"clear"),km={getAxes:WDe,getCurves:S4t,getOptions:E4t,setAxes:A4t,setCurves:_4t,setOptions:R4t,getConfig:w4t,clear:L4t,setAccTitle:Cr,getAccTitle:kr,setDiagramTitle:Ar,getDiagramTitle:Er,getAccDescription:Sr,setAccDescription:wr}});var M4t,qDe,UDe=z(()=>{"use strict";sl();St();au();bq();M4t=o(e=>{ca(e,km);let{axes:t,curves:r,options:i}=e;km.setAxes(t),km.setCurves(r),km.setOptions(i)},"populate"),qDe={parse:o(async e=>{let t=await bn("radar",e);Z.debug(t),M4t(t)},"parse")}});function B4t(e,t,r,i,n,a,s){let l=t.length,u=Math.min(s.width,s.height)/2;r.forEach((h,d)=>{if(h.entries.length!==l)return;let f=h.entries.map((p,m)=>{let g=2*Math.PI*m/l-Math.PI/2,y=F4t(p,i,n,u),v=y*Math.cos(g),x=y*Math.sin(g);return{x:v,y:x}});a==="circle"?e.append("path").attr("d",$4t(f,s.curveTension)).attr("class",`radarCurve-${d}`):a==="polygon"&&e.append("polygon").attr("points",f.map(p=>`${p.x},${p.y}`).join(" ")).attr("class",`radarCurve-${d}`)})}function F4t(e,t,r,i){let n=Math.min(Math.max(e,t),r);return i*(n-t)/(r-t)}function $4t(e,t){let r=e.length,i=`M${e[0].x},${e[0].y}`;for(let n=0;n<r;n++){let a=e[(n-1+r)%r],s=e[n],l=e[(n+1)%r],u=e[(n+2)%r],h={x:s.x+(l.x-a.x)*t,y:s.y+(l.y-a.y)*t},d={x:l.x-(u.x-s.x)*t,y:l.y-(u.y-s.y)*t};i+=` C${h.x},${h.y} ${d.x},${d.y} ${l.x},${l.y}`}return`${i} Z`}function z4t(e,t,r,i){if(!r)return;let n=(i.width/2+i.marginRight)*3/4,a=-(i.height/2+i.marginTop)*3/4,s=20;t.forEach((l,u)=>{let h=e.append("g").attr("transform",`translate(${n}, ${a+u*s})`);h.append("rect").attr("width",12).attr("height",12).attr("class",`radarLegendBox-${u}`),h.append("text").attr("x",16).attr("y",0).attr("class","radarLegendText").text(l.label)})}var N4t,I4t,O4t,P4t,HDe,YDe=z(()=>{"use strict";ps();Di();N4t=o((e,t,r,i)=>{let n=i.db,a=n.getAxes(),s=n.getCurves(),l=n.getOptions(),u=n.getConfig(),h=n.getDiagramTitle(),d=bi(t),f=I4t(d,u),p=l.max??Math.max(...s.map(y=>Math.max(...y.entries))),m=l.min,g=Math.min(u.width,u.height)/2;O4t(f,a,g,l.ticks,l.graticule),P4t(f,a,g,u),B4t(f,a,s,m,p,l.graticule,u),z4t(f,s,l.showLegend,u),f.append("text").attr("class","radarTitle").text(h).attr("x",0).attr("y",-u.height/2-u.marginTop)},"draw"),I4t=o((e,t)=>{let r=t.width+t.marginLeft+t.marginRight,i=t.height+t.marginTop+t.marginBottom,n={x:t.marginLeft+t.width/2,y:t.marginTop+t.height/2};return Br(e,i,r,t.useMaxWidth??!0),e.attr("viewBox",`0 0 ${r} ${i}`),e.append("g").attr("transform",`translate(${n.x}, ${n.y})`)},"drawFrame"),O4t=o((e,t,r,i,n)=>{if(n==="circle")for(let a=0;a<i;a++){let s=r*(a+1)/i;e.append("circle").attr("r",s).attr("class","radarGraticule")}else if(n==="polygon"){let a=t.length;for(let s=0;s<i;s++){let l=r*(s+1)/i,u=t.map((h,d)=>{let f=2*d*Math.PI/a-Math.PI/2,p=l*Math.cos(f),m=l*Math.sin(f);return`${p},${m}`}).join(" ");e.append("polygon").attr("points",u).attr("class","radarGraticule")}}},"drawGraticule"),P4t=o((e,t,r,i)=>{let n=t.length;for(let a=0;a<n;a++){let s=t[a].label,l=2*a*Math.PI/n-Math.PI/2;e.append("line").attr("x1",0).attr("y1",0).attr("x2",r*i.axisScaleFactor*Math.cos(l)).attr("y2",r*i.axisScaleFactor*Math.sin(l)).attr("class","radarAxisLine"),e.append("text").text(s).attr("x",r*i.axisLabelFactor*Math.cos(l)).attr("y",r*i.axisLabelFactor*Math.sin(l)).attr("class","radarAxisLabel")}},"drawAxes");o(B4t,"drawCurves");o(F4t,"relativeRadius");o($4t,"closedRoundCurve");o(z4t,"drawLegend");HDe={draw:N4t}});var G4t,V4t,jDe,XDe=z(()=>{"use strict";Xt();$f();br();G4t=o((e,t)=>{let r="";for(let i=0;i<e.THEME_COLOR_LIMIT;i++){let n=e[`cScale${i}`];r+=`
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+			fill: ${n};
+			fill-opacity: ${t.curveOpacity};
+			stroke: ${n};
+			stroke-width: ${t.curveStrokeWidth};
+		}
+		.radarLegendBox-${i} {
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+			fill-opacity: ${t.curveOpacity};
+			stroke: ${n};
+		}
+		`}return r},"genIndexStyles"),V4t=o(e=>{let t=Ao(),r=Nt(),i=Gr(t,r.themeVariables),n=Gr(i.radar,e);return{themeVariables:i,radarOptions:n}},"buildRadarStyleOptions"),jDe=o(({radar:e}={})=>{let{themeVariables:t,radarOptions:r}=V4t(e);return`
+	.radarTitle {
+		font-size: ${t.fontSize};
+		color: ${t.titleColor};
+		dominant-baseline: hanging;
+		text-anchor: middle;
+	}
+	.radarAxisLine {
+		stroke: ${r.axisColor};
+		stroke-width: ${r.axisStrokeWidth};
+	}
+	.radarAxisLabel {
+		dominant-baseline: middle;
+		text-anchor: middle;
+		font-size: ${r.axisLabelFontSize}px;
+		color: ${r.axisColor};
+	}
+	.radarGraticule {
+		fill: ${r.graticuleColor};
+		fill-opacity: ${r.graticuleOpacity};
+		stroke: ${r.graticuleColor};
+		stroke-width: ${r.graticuleStrokeWidth};
+	}
+	.radarLegendText {
+		text-anchor: start;
+		font-size: ${r.legendFontSize}px;
+		dominant-baseline: hanging;
+	}
+	${G4t(t,r)}
+	`},"styles")});var KDe={};xr(KDe,{diagram:()=>W4t});var W4t,ZDe=z(()=>{"use strict";bq();UDe();YDe();XDe();W4t={parser:qDe,db:km,renderer:HDe,styles:jDe}});var Tq,e8e,t8e=z(()=>{"use strict";Tq=(function(){var e=o(function(T,w,C,k){for(C=C||{},k=T.length;k--;C[T[k]]=w);return 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+`+O.showPosition()+`
+Expecting `+De.join(", ")+", got '"+(this.terminals_[U]||U)+"'":Ie="Parse error on line "+(D+1)+": Unexpected "+(U==L?"end of input":"'"+(this.terminals_[U]||U)+"'"),this.parseError(Ie,{text:O.match,token:this.terminals_[U]||U,line:O.yylineno,loc:F,expected:De})}if(ae[0]instanceof Array&&ae.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Y+", token: "+U);switch(ae[0]){case 1:k.push(U),A.push(O.yytext),N.push(O.yylloc),k.push(ae[1]),U=null,Q?(U=Q,Q=null):(_=O.yyleng,I=O.yytext,D=O.yylineno,F=O.yylloc,R>0&&R--);break;case 2:if(Te=this.productions_[ae[1]][1],re.$=A[A.length-Te],re._$={first_line:N[N.length-(Te||1)].first_line,last_line:N[N.length-1].last_line,first_column:N[N.length-(Te||1)].first_column,last_column:N[N.length-1].last_column},V&&(re._$.range=[N[N.length-(Te||1)].range[0],N[N.length-1].range[1]]),te=this.performAction.apply(re,[I,_,D,$.yy,ae[1],A,N].concat(B)),typeof te<"u")return te;Te&&(k=k.slice(0,-1*Te*2),A=A.slice(0,-1*Te),N=N.slice(0,-1*Te)),k.push(this.productions_[ae[1]][0]),A.push(re.$),N.push(re._$),ue=P[k[k.length-2]][k[k.length-1]],k.push(ue);break;case 3:return!0}}return!0},"parse")},x=(function(){var T={EOF:1,parseError:o(function(C,k){if(this.yy.parser)this.yy.parser.parseError(C,k);else throw new Error(C)},"parseError"),setInput:o(function(w,C){return this.yy=C||this.yy||{},this._input=w,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var w=this._input[0];this.yytext+=w,this.yyleng++,this.offset++,this.match+=w,this.matched+=w;var C=w.match(/(?:\r\n?|\n).*/g);return C?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),w},"input"),unput:o(function(w){var C=w.length,k=w.split(/(?:\r\n?|\n)/g);this._input=w+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-C),this.offset-=C;var E=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),k.length-1&&(this.yylineno-=k.length-1);var A=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:k?(k.length===E.length?this.yylloc.first_column:0)+E[E.length-k.length].length-k[0].length:this.yylloc.first_column-C},this.options.ranges&&(this.yylloc.range=[A[0],A[0]+this.yyleng-C]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(w){this.unput(this.match.slice(w))},"less"),pastInput:o(function(){var w=this.matched.substr(0,this.matched.length-this.match.length);return(w.length>20?"...":"")+w.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var w=this.match;return w.length<20&&(w+=this._input.substr(0,20-w.length)),(w.substr(0,20)+(w.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var w=this.pastInput(),C=new Array(w.length+1).join("-");return w+this.upcomingInput()+`
+`+C+"^"},"showPosition"),test_match:o(function(w,C){var k,E,A;if(this.options.backtrack_lexer&&(A={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(A.yylloc.range=this.yylloc.range.slice(0))),E=w[0].match(/(?:\r\n?|\n).*/g),E&&(this.yylineno+=E.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:E?E[E.length-1].length-E[E.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+w[0].length},this.yytext+=w[0],this.match+=w[0],this.matches=w,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(w[0].length),this.matched+=w[0],k=this.performAction.call(this,this.yy,this,C,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),k)return k;if(this._backtrack){for(var N in A)this[N]=A[N];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var w,C,k,E;this._more||(this.yytext="",this.match="");for(var A=this._currentRules(),N=0;N<A.length;N++)if(k=this._input.match(this.rules[A[N]]),k&&(!C||k[0].length>C[0].length)){if(C=k,E=N,this.options.backtrack_lexer){if(w=this.test_match(k,A[N]),w!==!1)return w;if(this._backtrack){C=!1;continue}else return!1}else if(!this.options.flex)break}return C?(w=this.test_match(C,A[E]),w!==!1?w:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var C=this.next();return C||this.lex()},"lex"),begin:o(function(C){this.conditionStack.push(C)},"begin"),popState:o(function(){var C=this.conditionStack.length-1;return C>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(C){return C=this.conditionStack.length-1-Math.abs(C||0),C>=0?this.conditionStack[C]:"INITIAL"},"topState"),pushState:o(function(C){this.begin(C)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(C,k,E,A){var N=A;switch(E){case 0:return C.getLogger().debug("Found block-beta"),10;break;case 1:return C.getLogger().debug("Found id-block"),29;break;case 2:return C.getLogger().debug("Found block"),10;break;case 3:C.getLogger().debug(".",k.yytext);break;case 4:C.getLogger().debug("_",k.yytext);break;case 5:return 5;case 6:return k.yytext=-1,28;break;case 7:return k.yytext=k.yytext.replace(/columns\s+/,""),C.getLogger().debug("COLUMNS (LEX)",k.yytext),28;break;case 8:this.pushState("md_string");break;case 9:return"MD_STR";case 10:this.popState();break;case 11:this.pushState("string");break;case 12:C.getLogger().debug("LEX: POPPING STR:",k.yytext),this.popState();break;case 13:return C.getLogger().debug("LEX: STR end:",k.yytext),"STR";break;case 14:return k.yytext=k.yytext.replace(/space\:/,""),C.getLogger().debug("SPACE NUM (LEX)",k.yytext),21;break;case 15:return k.yytext="1",C.getLogger().debug("COLUMNS (LEX)",k.yytext),21;break;case 16:return 42;case 17:return"LINKSTYLE";case 18:return"INTERPOLATE";case 19:return this.pushState("CLASSDEF"),39;break;case 20:return this.popState(),this.pushState("CLASSDEFID"),"DEFAULT_CLASSDEF_ID";break;case 21:return this.popState(),this.pushState("CLASSDEFID"),40;break;case 22:return this.popState(),41;break;case 23:return this.pushState("CLASS"),43;break;case 24:return this.popState(),this.pushState("CLASS_STYLE"),44;break;case 25:return this.popState(),45;break;case 26:return this.pushState("STYLE_STMNT"),46;break;case 27:return this.popState(),this.pushState("STYLE_DEFINITION"),47;break;case 28:return this.popState(),48;break;case 29:return this.pushState("acc_title"),"acc_title";break;case 30:return this.popState(),"acc_title_value";break;case 31:return this.pushState("acc_descr"),"acc_descr";break;case 32:return this.popState(),"acc_descr_value";break;case 33:this.pushState("acc_descr_multiline");break;case 34:this.popState();break;case 35:return"acc_descr_multiline_value";case 36:return 30;case 37:return this.popState(),C.getLogger().debug("Lex: (("),"NODE_DEND";break;case 38:return this.popState(),C.getLogger().debug("Lex: (("),"NODE_DEND";break;case 39:return this.popState(),C.getLogger().debug("Lex: ))"),"NODE_DEND";break;case 40:return this.popState(),C.getLogger().debug("Lex: (("),"NODE_DEND";break;case 41:return this.popState(),C.getLogger().debug("Lex: (("),"NODE_DEND";break;case 42:return this.popState(),C.getLogger().debug("Lex: (-"),"NODE_DEND";break;case 43:return this.popState(),C.getLogger().debug("Lex: -)"),"NODE_DEND";break;case 44:return this.popState(),C.getLogger().debug("Lex: (("),"NODE_DEND";break;case 45:return this.popState(),C.getLogger().debug("Lex: ]]"),"NODE_DEND";break;case 46:return this.popState(),C.getLogger().debug("Lex: ("),"NODE_DEND";break;case 47:return this.popState(),C.getLogger().debug("Lex: ])"),"NODE_DEND";break;case 48:return this.popState(),C.getLogger().debug("Lex: /]"),"NODE_DEND";break;case 49:return this.popState(),C.getLogger().debug("Lex: /]"),"NODE_DEND";break;case 50:return this.popState(),C.getLogger().debug("Lex: )]"),"NODE_DEND";break;case 51:return this.popState(),C.getLogger().debug("Lex: )"),"NODE_DEND";break;case 52:return this.popState(),C.getLogger().debug("Lex: ]>"),"NODE_DEND";break;case 53:return this.popState(),C.getLogger().debug("Lex: ]"),"NODE_DEND";break;case 54:return C.getLogger().debug("Lexa: -)"),this.pushState("NODE"),35;break;case 55:return C.getLogger().debug("Lexa: (-"),this.pushState("NODE"),35;break;case 56:return C.getLogger().debug("Lexa: ))"),this.pushState("NODE"),35;break;case 57:return C.getLogger().debug("Lexa: )"),this.pushState("NODE"),35;break;case 58:return C.getLogger().debug("Lex: ((("),this.pushState("NODE"),35;break;case 59:return C.getLogger().debug("Lexa: )"),this.pushState("NODE"),35;break;case 60:return C.getLogger().debug("Lexa: )"),this.pushState("NODE"),35;break;case 61:return C.getLogger().debug("Lexa: )"),this.pushState("NODE"),35;break;case 62:return C.getLogger().debug("Lexc: >"),this.pushState("NODE"),35;break;case 63:return C.getLogger().debug("Lexa: (["),this.pushState("NODE"),35;break;case 64:return C.getLogger().debug("Lexa: )"),this.pushState("NODE"),35;break;case 65:return this.pushState("NODE"),35;break;case 66:return this.pushState("NODE"),35;break;case 67:return this.pushState("NODE"),35;break;case 68:return this.pushState("NODE"),35;break;case 69:return this.pushState("NODE"),35;break;case 70:return this.pushState("NODE"),35;break;case 71:return this.pushState("NODE"),35;break;case 72:return C.getLogger().debug("Lexa: ["),this.pushState("NODE"),35;break;case 73:return this.pushState("BLOCK_ARROW"),C.getLogger().debug("LEX ARR START"),37;break;case 74:return C.getLogger().debug("Lex: NODE_ID",k.yytext),31;break;case 75:return C.getLogger().debug("Lex: EOF",k.yytext),8;break;case 76:this.pushState("md_string");break;case 77:this.pushState("md_string");break;case 78:return"NODE_DESCR";case 79:this.popState();break;case 80:C.getLogger().debug("Lex: Starting string"),this.pushState("string");break;case 81:C.getLogger().debug("LEX ARR: Starting string"),this.pushState("string");break;case 82:return C.getLogger().debug("LEX: NODE_DESCR:",k.yytext),"NODE_DESCR";break;case 83:C.getLogger().debug("LEX POPPING"),this.popState();break;case 84:C.getLogger().debug("Lex: =>BAE"),this.pushState("ARROW_DIR");break;case 85:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (right): dir:",k.yytext),"DIR";break;case 86:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (left):",k.yytext),"DIR";break;case 87:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (x):",k.yytext),"DIR";break;case 88:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (y):",k.yytext),"DIR";break;case 89:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (up):",k.yytext),"DIR";break;case 90:return k.yytext=k.yytext.replace(/^,\s*/,""),C.getLogger().debug("Lex (down):",k.yytext),"DIR";break;case 91:return k.yytext="]>",C.getLogger().debug("Lex (ARROW_DIR end):",k.yytext),this.popState(),this.popState(),"BLOCK_ARROW_END";break;case 92:return C.getLogger().debug("Lex: LINK","#"+k.yytext+"#"),15;break;case 93:return C.getLogger().debug("Lex: LINK",k.yytext),15;break;case 94:return C.getLogger().debug("Lex: LINK",k.yytext),15;break;case 95:return C.getLogger().debug("Lex: LINK",k.yytext),15;break;case 96:return C.getLogger().debug("Lex: START_LINK",k.yytext),this.pushState("LLABEL"),16;break;case 97:return C.getLogger().debug("Lex: START_LINK",k.yytext),this.pushState("LLABEL"),16;break;case 98:return C.getLogger().debug("Lex: START_LINK",k.yytext),this.pushState("LLABEL"),16;break;case 99:this.pushState("md_string");break;case 100:return C.getLogger().debug("Lex: Starting string"),this.pushState("string"),"LINK_LABEL";break;case 101:return this.popState(),C.getLogger().debug("Lex: LINK","#"+k.yytext+"#"),15;break;case 102:return this.popState(),C.getLogger().debug("Lex: LINK",k.yytext),15;break;case 103:return this.popState(),C.getLogger().debug("Lex: LINK",k.yytext),15;break;case 104:return C.getLogger().debug("Lex: COLON",k.yytext),k.yytext=k.yytext.slice(1),27;break}},"anonymous"),rules:[/^(?:block-beta\b)/,/^(?:block:)/,/^(?:block\b)/,/^(?:[\s]+)/,/^(?:[\n]+)/,/^(?:((\u000D\u000A)|(\u000A)))/,/^(?:columns\s+auto\b)/,/^(?:columns\s+[\d]+)/,/^(?:["][`])/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:space[:]\d+)/,/^(?:space\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\s+)/,/^(?:DEFAULT\s+)/,/^(?:\w+\s+)/,/^(?:[^\n]*)/,/^(?:class\s+)/,/^(?:(\w+)+((,\s*\w+)*))/,/^(?:[^\n]*)/,/^(?:style\s+)/,/^(?:(\w+)+((,\s*\w+)*))/,/^(?:[^\n]*)/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:end\b\s*)/,/^(?:\(\(\()/,/^(?:\)\)\))/,/^(?:[\)]\))/,/^(?:\}\})/,/^(?:\})/,/^(?:\(-)/,/^(?:-\))/,/^(?:\(\()/,/^(?:\]\])/,/^(?:\()/,/^(?:\]\))/,/^(?:\\\])/,/^(?:\/\])/,/^(?:\)\])/,/^(?:[\)])/,/^(?:\]>)/,/^(?:[\]])/,/^(?:-\))/,/^(?:\(-)/,/^(?:\)\))/,/^(?:\))/,/^(?:\(\(\()/,/^(?:\(\()/,/^(?:\{\{)/,/^(?:\{)/,/^(?:>)/,/^(?:\(\[)/,/^(?:\()/,/^(?:\[\[)/,/^(?:\[\|)/,/^(?:\[\()/,/^(?:\)\)\))/,/^(?:\[\\)/,/^(?:\[\/)/,/^(?:\[\\)/,/^(?:\[)/,/^(?:<\[)/,/^(?:[^\(\[\n\-\)\{\}\s\<\>:=]+)/,/^(?:$)/,/^(?:["][`])/,/^(?:["][`])/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["])/,/^(?:["])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:\]>\s*\()/,/^(?:,?\s*right\s*)/,/^(?:,?\s*left\s*)/,/^(?:,?\s*x\s*)/,/^(?:,?\s*y\s*)/,/^(?:,?\s*up\s*)/,/^(?:,?\s*down\s*)/,/^(?:\)\s*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*~~[\~]+\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:["][`])/,/^(?:["])/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?::\d+)/],conditions:{STYLE_DEFINITION:{rules:[28],inclusive:!1},STYLE_STMNT:{rules:[27],inclusive:!1},CLASSDEFID:{rules:[22],inclusive:!1},CLASSDEF:{rules:[20,21],inclusive:!1},CLASS_STYLE:{rules:[25],inclusive:!1},CLASS:{rules:[24],inclusive:!1},LLABEL:{rules:[99,100,101,102,103],inclusive:!1},ARROW_DIR:{rules:[85,86,87,88,89,90,91],inclusive:!1},BLOCK_ARROW:{rules:[76,81,84],inclusive:!1},NODE:{rules:[37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,77,80],inclusive:!1},md_string:{rules:[9,10,78,79],inclusive:!1},space:{rules:[],inclusive:!1},string:{rules:[12,13,82,83],inclusive:!1},acc_descr_multiline:{rules:[34,35],inclusive:!1},acc_descr:{rules:[32],inclusive:!1},acc_title:{rules:[30],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,7,8,11,14,15,16,17,18,19,23,26,29,31,33,36,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,92,93,94,95,96,97,98,104],inclusive:!0}}};return T})();v.lexer=x;function b(){this.yy={}}return o(b,"Parser"),b.prototype=v,v.Parser=b,new b})();Tq.parser=Tq;e8e=Tq});function J4t(e){switch(Z.debug("typeStr2Type",e),e){case"[]":return"square";case"()":return Z.debug("we have a round"),"round";case"(())":return"circle";case">]":return"rect_left_inv_arrow";case"{}":return"diamond";case"{{}}":return"hexagon";case"([])":return"stadium";case"[[]]":return"subroutine";case"[()]":return"cylinder";case"((()))":return"doublecircle";case"[//]":return"lean_right";case"[\\\\]":return"lean_left";case"[/\\]":return"trapezoid";case"[\\/]":return"inv_trapezoid";case"<[]>":return"block_arrow";default:return"na"}}function eSt(e){switch(Z.debug("typeStr2Type",e),e){case"==":return"thick";default:return"normal"}}function tSt(e){switch(e.trim().slice(-1)){case"x":return"arrow_cross";case"o":return"arrow_circle";case">":return"arrow_point";default:return""}}function 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+  .cluster-label text {
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+  }
+  .cluster-label span,p {
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+
+
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+  /* For html labels only */
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+  }
+
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+    fill: ${Eq(e.clusterBkg,.5)};
+    stroke: ${Eq(e.clusterBorder,.2)};
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+    stroke-width: 1px;
+  }
+
+  .cluster text {
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+  }
+
+  .cluster span,p {
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+  }
+  /* .cluster div {
+    color: ${e.titleColor};
+  } */
+
+  div.mermaidTooltip {
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+    text-align: center;
+    max-width: 200px;
+    padding: 2px;
+    font-family: ${e.fontFamily};
+    font-size: 12px;
+    background: ${e.tertiaryColor};
+    border: 1px solid ${e.border2};
+    border-radius: 2px;
+    pointer-events: none;
+    z-index: 100;
+  }
+
+  .flowchartTitleText {
+    text-anchor: middle;
+    font-size: 18px;
+    fill: ${e.textColor};
+  }
+  ${Vc()}
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+  .edge {
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+    stroke: ${e.archEdgeColor};
+    fill: none;
+  }
+
+  .arrow {
+    fill: ${e.archEdgeArrowColor};
+  }
+
+  .node-bkg {
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+    stroke: ${e.archGroupBorderColor};
+    stroke-width: ${e.archGroupBorderWidth};
+    stroke-dasharray: 8;
+  }
+  .node-icon-text {
+    display: flex; 
+    align-items: center;
+  }
+  
+  .node-icon-text > div {
+    color: #fff;
+    margin: 1px;
+    height: fit-content;
+    text-align: center;
+    overflow: hidden;
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+  }
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i(){}o(i,"LayoutConstants"),i.QUALITY=1,i.DEFAULT_CREATE_BENDS_AS_NEEDED=!1,i.DEFAULT_INCREMENTAL=!1,i.DEFAULT_ANIMATION_ON_LAYOUT=!0,i.DEFAULT_ANIMATION_DURING_LAYOUT=!1,i.DEFAULT_ANIMATION_PERIOD=50,i.DEFAULT_UNIFORM_LEAF_NODE_SIZES=!1,i.DEFAULT_GRAPH_MARGIN=15,i.NODE_DIMENSIONS_INCLUDE_LABELS=!1,i.SIMPLE_NODE_SIZE=40,i.SIMPLE_NODE_HALF_SIZE=i.SIMPLE_NODE_SIZE/2,i.EMPTY_COMPOUND_NODE_SIZE=40,i.MIN_EDGE_LENGTH=1,i.WORLD_BOUNDARY=1e6,i.INITIAL_WORLD_BOUNDARY=i.WORLD_BOUNDARY/1e3,i.WORLD_CENTER_X=1200,i.WORLD_CENTER_Y=900,e.exports=i}),(function(e,t,r){"use strict";var i=r(2),n=r(8),a=r(9);function s(u,h,d){i.call(this,d),this.isOverlapingSourceAndTarget=!1,this.vGraphObject=d,this.bendpoints=[],this.source=u,this.target=h}o(s,"LEdge"),s.prototype=Object.create(i.prototype);for(var l in i)s[l]=i[l];s.prototype.getSource=function(){return this.source},s.prototype.getTarget=function(){return this.target},s.prototype.isInterGraph=function(){return this.isInterGraph},s.prototype.getLength=function(){return this.length},s.prototype.isOverlapingSourceAndTarget=function(){return this.isOverlapingSourceAndTarget},s.prototype.getBendpoints=function(){return this.bendpoints},s.prototype.getLca=function(){return this.lca},s.prototype.getSourceInLca=function(){return this.sourceInLca},s.prototype.getTargetInLca=function(){return this.targetInLca},s.prototype.getOtherEnd=function(u){if(this.source===u)return this.target;if(this.target===u)return this.source;throw"Node is not incident with this edge"},s.prototype.getOtherEndInGraph=function(u,h){for(var d=this.getOtherEnd(u),f=h.getGraphManager().getRoot();;){if(d.getOwner()==h)return d;if(d.getOwner()==f)break;d=d.getOwner().getParent()}return null},s.prototype.updateLength=function(){var u=new Array(4);this.isOverlapingSourceAndTarget=n.getIntersection(this.target.getRect(),this.source.getRect(),u),this.isOverlapingSourceAndTarget||(this.lengthX=u[0]-u[2],this.lengthY=u[1]-u[3],Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY))},s.prototype.updateLengthSimple=function(){this.lengthX=this.target.getCenterX()-this.source.getCenterX(),this.lengthY=this.target.getCenterY()-this.source.getCenterY(),Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY)},e.exports=s}),(function(e,t,r){"use strict";function i(n){this.vGraphObject=n}o(i,"LGraphObject"),e.exports=i}),(function(e,t,r){"use strict";var i=r(2),n=r(10),a=r(13),s=r(0),l=r(16),u=r(5);function h(f,p,m,g){m==null&&g==null&&(g=p),i.call(this,g),f.graphManager!=null&&(f=f.graphManager),this.estimatedSize=n.MIN_VALUE,this.inclusionTreeDepth=n.MAX_VALUE,this.vGraphObject=g,this.edges=[],this.graphManager=f,m!=null&&p!=null?this.rect=new a(p.x,p.y,m.width,m.height):this.rect=new a}o(h,"LNode"),h.prototype=Object.create(i.prototype);for(var d in i)h[d]=i[d];h.prototype.getEdges=function(){return this.edges},h.prototype.getChild=function(){return this.child},h.prototype.getOwner=function(){return this.owner},h.prototype.getWidth=function(){return this.rect.width},h.prototype.setWidth=function(f){this.rect.width=f},h.prototype.getHeight=function(){return this.rect.height},h.prototype.setHeight=function(f){this.rect.height=f},h.prototype.getCenterX=function(){return this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(f,p){this.rect.x=f.x,this.rect.y=f.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(f,p){this.rect.x=f-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(f,p){this.rect.x=f,this.rect.y=p},h.prototype.moveBy=function(f,p){this.rect.x+=f,this.rect.y+=p},h.prototype.getEdgeListToNode=function(f){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==f){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(f){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==f||y.source==f)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var f=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)f.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";f.add(m.source)}}),f},h.prototype.withChildren=function(){var f=new Set,p,m;if(f.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;y<g.length;y++)p=g[y],m=p.withChildren(),m.forEach(function(v){f.add(v)});return f},h.prototype.getNoOfChildren=function(){var f=0,p;if(this.child==null)f=1;else for(var m=this.child.getNodes(),g=0;g<m.length;g++)p=m[g],f+=p.getNoOfChildren();return f==0&&(f=1),f},h.prototype.getEstimatedSize=function(){if(this.estimatedSize==n.MIN_VALUE)throw"assert failed";return this.estimatedSize},h.prototype.calcEstimatedSize=function(){return this.child==null?this.estimatedSize=(this.rect.width+this.rect.height)/2:(this.estimatedSize=this.child.calcEstimatedSize(),this.rect.width=this.estimatedSize,this.rect.height=this.estimatedSize,this.estimatedSize)},h.prototype.scatter=function(){var f,p,m=-s.INITIAL_WORLD_BOUNDARY,g=s.INITIAL_WORLD_BOUNDARY;f=s.WORLD_CENTER_X+l.nextDouble()*(g-m)+m;var y=-s.INITIAL_WORLD_BOUNDARY,v=s.INITIAL_WORLD_BOUNDARY;p=s.WORLD_CENTER_Y+l.nextDouble()*(v-y)+y,this.rect.x=f,this.rect.y=p},h.prototype.updateBounds=function(){if(this.getChild()==null)throw"assert failed";if(this.getChild().getNodes().length!=0){var f=this.getChild();if(f.updateBounds(!0),this.rect.x=f.getLeft(),this.rect.y=f.getTop(),this.setWidth(f.getRight()-f.getLeft()),this.setHeight(f.getBottom()-f.getTop()),s.NODE_DIMENSIONS_INCLUDE_LABELS){var p=f.getRight()-f.getLeft(),m=f.getBottom()-f.getTop();this.labelWidth&&(this.labelPosHorizontal=="left"?(this.rect.x-=this.labelWidth,this.setWidth(p+this.labelWidth)):this.labelPosHorizontal=="center"&&this.labelWidth>p?(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)):this.labelPosHorizontal=="right"&&this.setWidth(p+this.labelWidth)),this.labelHeight&&(this.labelPosVertical=="top"?(this.rect.y-=this.labelHeight,this.setHeight(m+this.labelHeight)):this.labelPosVertical=="center"&&this.labelHeight>m?(this.rect.y-=(this.labelHeight-m)/2,this.setHeight(this.labelHeight)):this.labelPosVertical=="bottom"&&this.setHeight(m+this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==n.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(f){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=f.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},e.exports=h}),(function(e,t,r){"use strict";var i=r(0);function n(){}o(n,"FDLayoutConstants");for(var a in i)n[a]=i[a];n.MAX_ITERATIONS=2500,n.DEFAULT_EDGE_LENGTH=50,n.DEFAULT_SPRING_STRENGTH=.45,n.DEFAULT_REPULSION_STRENGTH=4500,n.DEFAULT_GRAVITY_STRENGTH=.4,n.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,n.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,n.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,n.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,n.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,n.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,n.COOLING_ADAPTATION_FACTOR=.33,n.ADAPTATION_LOWER_NODE_LIMIT=1e3,n.ADAPTATION_UPPER_NODE_LIMIT=5e3,n.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,n.MAX_NODE_DISPLACEMENT=n.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,n.MIN_REPULSION_DIST=n.DEFAULT_EDGE_LENGTH/10,n.CONVERGENCE_CHECK_PERIOD=100,n.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,n.MIN_EDGE_LENGTH=1,n.GRID_CALCULATION_CHECK_PERIOD=10,e.exports=n}),(function(e,t,r){"use strict";function i(n,a){n==null&&a==null?(this.x=0,this.y=0):(this.x=n,this.y=a)}o(i,"PointD"),i.prototype.getX=function(){return this.x},i.prototype.getY=function(){return this.y},i.prototype.setX=function(n){this.x=n},i.prototype.setY=function(n){this.y=n},i.prototype.getDifference=function(n){return new DimensionD(this.x-n.x,this.y-n.y)},i.prototype.getCopy=function(){return new i(this.x,this.y)},i.prototype.translate=function(n){return this.x+=n.width,this.y+=n.height,this},e.exports=i}),(function(e,t,r){"use strict";var i=r(2),n=r(10),a=r(0),s=r(7),l=r(3),u=r(1),h=r(13),d=r(12),f=r(11);function p(g,y,v){i.call(this,v),this.estimatedSize=n.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(i.prototype);for(var m in i)p[m]=i[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,T=0;T<b;T++)x=v[T],x.isInterGraph?this.graphManager.remove(x):x.source.owner.remove(x);var w=this.nodes.indexOf(y);if(w==-1)throw"Node not in owner node list!";this.nodes.splice(w,1)}else if(g instanceof u){var x=g;if(x==null)throw"Edge is null!";if(!(x.source!=null&&x.target!=null))throw"Source and/or target is null!";if(!(x.source.owner!=null&&x.target.owner!=null&&x.source.owner==this&&x.target.owner==this))throw"Source and/or target owner is invalid!";var C=x.source.edges.indexOf(x),k=x.target.edges.indexOf(x);if(!(C>-1&&k>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(C,1),x.target!=x.source&&x.target.edges.splice(k,1);var w=x.source.owner.getEdges().indexOf(x);if(w==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(w,1)}},p.prototype.updateLeftTop=function(){for(var g=n.MAX_VALUE,y=n.MAX_VALUE,v,x,b,T=this.getNodes(),w=T.length,C=0;C<w;C++){var k=T[C];v=k.getTop(),x=k.getLeft(),g>v&&(g=v),y>x&&(y=x)}return g==n.MAX_VALUE?null:(T[0].getParent().paddingLeft!=null?b=T[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new d(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=n.MAX_VALUE,v=-n.MAX_VALUE,x=n.MAX_VALUE,b=-n.MAX_VALUE,T,w,C,k,E,A=this.nodes,N=A.length,P=0;P<N;P++){var I=A[P];g&&I.child!=null&&I.updateBounds(),T=I.getLeft(),w=I.getRight(),C=I.getTop(),k=I.getBottom(),y>T&&(y=T),v<w&&(v=w),x>C&&(x=C),b<k&&(b=k)}var D=new h(y,x,v-y,b-x);y==n.MAX_VALUE&&(this.left=this.parent.getLeft(),this.right=this.parent.getRight(),this.top=this.parent.getTop(),this.bottom=this.parent.getBottom()),A[0].getParent().paddingLeft!=null?E=A[0].getParent().paddingLeft:E=this.margin,this.left=D.x-E,this.right=D.x+D.width+E,this.top=D.y-E,this.bottom=D.y+D.height+E},p.calculateBounds=function(g){for(var y=n.MAX_VALUE,v=-n.MAX_VALUE,x=n.MAX_VALUE,b=-n.MAX_VALUE,T,w,C,k,E=g.length,A=0;A<E;A++){var N=g[A];T=N.getLeft(),w=N.getRight(),C=N.getTop(),k=N.getBottom(),y>T&&(y=T),v<w&&(v=w),x>C&&(x=C),b<k&&(b=k)}var P=new h(y,x,v-y,b-x);return P},p.prototype.getInclusionTreeDepth=function(){return this==this.graphManager.getRoot()?1:this.parent.getInclusionTreeDepth()},p.prototype.getEstimatedSize=function(){if(this.estimatedSize==n.MIN_VALUE)throw"assert failed";return this.estimatedSize},p.prototype.calcEstimatedSize=function(){for(var g=0,y=this.nodes,v=y.length,x=0;x<v;x++){var b=y[x];g+=b.calcEstimatedSize()}return g==0?this.estimatedSize=a.EMPTY_COMPOUND_NODE_SIZE:this.estimatedSize=g/Math.sqrt(this.nodes.length),this.estimatedSize},p.prototype.updateConnected=function(){var g=this;if(this.nodes.length==0){this.isConnected=!0;return}var y=new f,v=new Set,x=this.nodes[0],b,T,w=x.withChildren();for(w.forEach(function(P){y.push(P),v.add(P)});y.length!==0;){x=y.shift(),b=x.getEdges();for(var C=b.length,k=0;k<C;k++){var E=b[k];if(T=E.getOtherEndInGraph(x,this),T!=null&&!v.has(T)){var A=T.withChildren();A.forEach(function(P){y.push(P),v.add(P)})}}}if(this.isConnected=!1,v.size>=this.nodes.length){var N=0;v.forEach(function(P){P.owner==g&&N++}),N==this.nodes.length&&(this.isConnected=!0)}},e.exports=p}),(function(e,t,r){"use strict";var i,n=r(1);function a(s){i=r(6),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,d){if(u==null&&h==null&&d==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{d=u,h=l,u=s;var f=h.getOwner(),p=d.getOwner();if(!(f!=null&&f.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(f==p)return u.isInterGraph=!1,f.add(u,h,d);if(u.isInterGraph=!0,u.source=h,u.target=d,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof i){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,d=u.length,f=0;f<d;f++)h=u[f],l.remove(h);var p=[];p=p.concat(l.getNodes());var m;d=p.length;for(var f=0;f<d;f++)m=p[f],l.remove(m);l==this.rootGraph&&this.setRootGraph(null);var g=this.graphs.indexOf(l);this.graphs.splice(g,1),l.parent=null}else if(s instanceof n){if(h=s,h==null)throw"Edge is null!";if(!h.isInterGraph)throw"Not an inter-graph edge!";if(!(h.source!=null&&h.target!=null))throw"Source and/or target is null!";if(!(h.source.edges.indexOf(h)!=-1&&h.target.edges.indexOf(h)!=-1))throw"Source and/or target doesn't know this edge!";var g=h.source.edges.indexOf(h);if(h.source.edges.splice(g,1),g=h.target.edges.indexOf(h),h.target.edges.splice(g,1),!(h.source.owner!=null&&h.source.owner.getGraphManager()!=null))throw"Edge owner graph or owner graph manager is null!";if(h.source.owner.getGraphManager().edges.indexOf(h)==-1)throw"Not in owner graph manager's edge list!";var g=h.source.owner.getGraphManager().edges.indexOf(h);h.source.owner.getGraphManager().edges.splice(g,1)}},a.prototype.updateBounds=function(){this.rootGraph.updateBounds(!0)},a.prototype.getGraphs=function(){return this.graphs},a.prototype.getAllNodes=function(){if(this.allNodes==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<u;h++)s=s.concat(l[h].getNodes());this.allNodes=s}return this.allNodes},a.prototype.resetAllNodes=function(){this.allNodes=null},a.prototype.resetAllEdges=function(){this.allEdges=null},a.prototype.resetAllNodesToApplyGravitation=function(){this.allNodesToApplyGravitation=null},a.prototype.getAllEdges=function(){if(this.allEdges==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<l.length;h++)s=s.concat(l[h].getEdges());s=s.concat(this.edges),this.allEdges=s}return this.allEdges},a.prototype.getAllNodesToApplyGravitation=function(){return this.allNodesToApplyGravitation},a.prototype.setAllNodesToApplyGravitation=function(s){if(this.allNodesToApplyGravitation!=null)throw"assert failed";this.allNodesToApplyGravitation=s},a.prototype.getRoot=function(){return this.rootGraph},a.prototype.setRootGraph=function(s){if(s.getGraphManager()!=this)throw"Root not in this graph mgr!";this.rootGraph=s,s.parent==null&&(s.parent=this.layout.newNode("Root node"))},a.prototype.getLayout=function(){return this.layout},a.prototype.isOneAncestorOfOther=function(s,l){if(!(s!=null&&l!=null))throw"assert failed";if(s==l)return!0;var u=s.getOwner(),h;do{if(h=u.getParent(),h==null)break;if(h==l)return!0;if(u=h.getOwner(),u==null)break}while(!0);u=l.getOwner();do{if(h=u.getParent(),h==null)break;if(h==s)return!0;if(u=h.getOwner(),u==null)break}while(!0);return!1},a.prototype.calcLowestCommonAncestors=function(){for(var s,l,u,h,d,f=this.getAllEdges(),p=f.length,m=0;m<p;m++){if(s=f[m],l=s.source,u=s.target,s.lca=null,s.sourceInLca=l,s.targetInLca=u,l==u){s.lca=l.getOwner();continue}for(h=l.getOwner();s.lca==null;){for(s.targetInLca=u,d=u.getOwner();s.lca==null;){if(d==h){s.lca=d;break}if(d==this.rootGraph)break;if(s.lca!=null)throw"assert failed";s.targetInLca=d.getParent(),d=s.targetInLca.getOwner()}if(h==this.rootGraph)break;s.lca==null&&(s.sourceInLca=h.getParent(),h=s.sourceInLca.getOwner())}if(s.lca==null)throw"assert failed"}},a.prototype.calcLowestCommonAncestor=function(s,l){if(s==l)return s.getOwner();var u=s.getOwner();do{if(u==null)break;var h=l.getOwner();do{if(h==null)break;if(h==u)return h;h=h.getParent().getOwner()}while(!0);u=u.getParent().getOwner()}while(!0);return u},a.prototype.calcInclusionTreeDepths=function(s,l){s==null&&l==null&&(s=this.rootGraph,l=1);for(var u,h=s.getNodes(),d=h.length,f=0;f<d;f++)u=h[f],u.inclusionTreeDepth=l,u.child!=null&&this.calcInclusionTreeDepths(u.child,l+1)},a.prototype.includesInvalidEdge=function(){for(var s,l=[],u=this.edges.length,h=0;h<u;h++)s=this.edges[h],this.isOneAncestorOfOther(s.source,s.target)&&l.push(s);for(var h=0;h<l.length;h++)this.remove(l[h]);return!1},e.exports=a}),(function(e,t,r){"use strict";var i=r(12);function n(){}o(n,"IGeometry"),n.calcSeparationAmount=function(a,s,l,u){if(!a.intersects(s))throw"assert failed";var h=new 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m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x<m.bendpoints.length;x++){var b=this.newNode(null);b.setRect(new Point(0,0),new Dimension(1,1)),v.add(b);var T=this.newEdge(null);this.graphManager.add(T,y,b),g.add(b),y=b}var T=this.newEdge(null);return this.graphManager.add(T,y,m.target),this.edgeToDummyNodes.set(m,g),m.isInterGraph()?this.graphManager.remove(m):v.remove(m),g},p.prototype.createBendpointsFromDummyNodes=function(){var m=[];m=m.concat(this.graphManager.getAllEdges()),m=[].concat(i(this.edgeToDummyNodes.keys())).concat(m);for(var g=0;g<m.length;g++){var y=m[g];if(y.bendpoints.length>0){for(var v=this.edgeToDummyNodes.get(y),x=0;x<v.length;x++){var b=v[x],T=new h(b.getCenterX(),b.getCenterY()),w=y.bendpoints.get(x);w.x=T.x,w.y=T.y,b.getOwner().remove(b)}this.graphManager.add(y,y.source,y.target)}}},p.transform=function(m,g,y,v){if(y!=null&&v!=null){var x=g;if(m<=50){var b=g/y;x-=(g-b)/50*(50-m)}else{var T=g*v;x+=(T-g)/50*(m-50)}return x}else{var w,C;return m<=50?(w=9*g/500,C=g/10):(w=9*g/50,C=-8*g),w*m+C}},p.findCenterOfTree=function(m){var g=[];g=g.concat(m);var y=[],v=new Map,x=!1,b=null;(g.length==1||g.length==2)&&(x=!0,b=g[0]);for(var T=0;T<g.length;T++){var w=g[T],C=w.getNeighborsList().size;v.set(w,w.getNeighborsList().size),C==1&&y.push(w)}var k=[];for(k=k.concat(y);!x;){var E=[];E=E.concat(k),k=[];for(var T=0;T<g.length;T++){var w=g[T],A=g.indexOf(w);A>=0&&g.splice(A,1);var N=w.getNeighborsList();N.forEach(function(D){if(y.indexOf(D)<0){var _=v.get(D),R=_-1;R==1&&k.push(D),v.set(D,R)}})}y=y.concat(k),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},e.exports=p}),(function(e,t,r){"use strict";function i(){}o(i,"RandomSeed"),i.seed=1,i.x=0,i.nextDouble=function(){return i.x=Math.sin(i.seed++)*1e4,i.x-Math.floor(i.x)},e.exports=i}),(function(e,t,r){"use strict";var i=r(5);function 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n)h[d]=n[d];h.prototype.initParameters=function(){n.prototype.initParameters.call(this,arguments),this.totalIterations=0,this.notAnimatedIterations=0,this.useFRGridVariant=a.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION,this.grid=[]},h.prototype.calcIdealEdgeLengths=function(){for(var f,p,m,g,y,v,x,b=this.getGraphManager().getAllEdges(),T=0;T<b.length;T++)f=b[T],p=f.idealLength,f.isInterGraph&&(g=f.getSource(),y=f.getTarget(),v=f.getSourceInLca().getEstimatedSize(),x=f.getTargetInLca().getEstimatedSize(),this.useSmartIdealEdgeLengthCalculation&&(f.idealLength+=v+x-2*s.SIMPLE_NODE_SIZE),m=f.getLca().getInclusionTreeDepth(),f.idealLength+=p*a.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR*(g.getInclusionTreeDepth()+y.getInclusionTreeDepth()-2*m))},h.prototype.initSpringEmbedder=function(){var f=this.getAllNodes().length;this.incremental?(f>a.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(f-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(f>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(f-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var f=this.getAllEdges(),p,m=0;m<f.length;m++)p=f[m],this.calcSpringForce(p,p.idealLength)},h.prototype.calcRepulsionForces=function(){var f=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&f&&this.updateGrid(),b=new Set,m=0;m<x.length;m++)y=x[m],this.calculateRepulsionForceOfANode(y,b,f,p),b.add(y);else for(m=0;m<x.length;m++)for(y=x[m],g=m+1;g<x.length;g++)v=x[g],y.getOwner()==v.getOwner()&&this.calcRepulsionForce(y,v)},h.prototype.calcGravitationalForces=function(){for(var f,p=this.getAllNodesToApplyGravitation(),m=0;m<p.length;m++)f=p[m],this.calcGravitationalForce(f)},h.prototype.moveNodes=function(){for(var f=this.getAllNodes(),p,m=0;m<f.length;m++)p=f[m],p.move()},h.prototype.calcSpringForce=function(f,p){var m=f.getSource(),g=f.getTarget(),y,v,x,b;if(this.uniformLeafNodeSizes&&m.getChild()==null&&g.getChild()==null)f.updateLengthSimple();else if(f.updateLength(),f.isOverlapingSourceAndTarget)return;y=f.getLength(),y!=0&&(v=f.edgeElasticity*(y-p),x=v*(f.lengthX/y),b=v*(f.lengthY/y),m.springForceX+=x,m.springForceY+=b,g.springForceX-=x,g.springForceY-=b)},h.prototype.calcRepulsionForce=function(f,p){var m=f.getRect(),g=p.getRect(),y=new Array(2),v=new Array(4),x,b,T,w,C,k,E;if(m.intersects(g)){l.calcSeparationAmount(m,g,y,a.DEFAULT_EDGE_LENGTH/2),k=2*y[0],E=2*y[1];var A=f.noOfChildren*p.noOfChildren/(f.noOfChildren+p.noOfChildren);f.repulsionForceX-=A*k,f.repulsionForceY-=A*E,p.repulsionForceX+=A*k,p.repulsionForceY+=A*E}else this.uniformLeafNodeSizes&&f.getChild()==null&&p.getChild()==null?(x=g.getCenterX()-m.getCenterX(),b=g.getCenterY()-m.getCenterY()):(l.getIntersection(m,g,v),x=v[2]-v[0],b=v[3]-v[1]),Math.abs(x)<a.MIN_REPULSION_DIST&&(x=u.sign(x)*a.MIN_REPULSION_DIST),Math.abs(b)<a.MIN_REPULSION_DIST&&(b=u.sign(b)*a.MIN_REPULSION_DIST),T=x*x+b*b,w=Math.sqrt(T),C=(f.nodeRepulsion/2+p.nodeRepulsion/2)*f.noOfChildren*p.noOfChildren/T,k=C*x/w,E=C*b/w,f.repulsionForceX-=k,f.repulsionForceY-=E,p.repulsionForceX+=k,p.repulsionForceY+=E},h.prototype.calcGravitationalForce=function(f){var p,m,g,y,v,x,b,T;p=f.getOwner(),m=(p.getRight()+p.getLeft())/2,g=(p.getTop()+p.getBottom())/2,y=f.getCenterX()-m,v=f.getCenterY()-g,x=Math.abs(y)+f.getWidth()/2,b=Math.abs(v)+f.getHeight()/2,f.getOwner()==this.graphManager.getRoot()?(T=p.getEstimatedSize()*this.gravityRangeFactor,(x>T||b>T)&&(f.gravitationForceX=-this.gravityConstant*y,f.gravitationForceY=-this.gravityConstant*v)):(T=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>T||b>T)&&(f.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,f.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var f,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),f=this.totalDisplacement<this.totalDisplacementThreshold,this.oldTotalDisplacement=this.totalDisplacement,f||p},h.prototype.animate=function(){this.animationDuringLayout&&!this.isSubLayout&&(this.notAnimatedIterations==this.animationPeriod?(this.update(),this.notAnimatedIterations=0):this.notAnimatedIterations++)},h.prototype.calcNoOfChildrenForAllNodes=function(){for(var f,p=this.graphManager.getAllNodes(),m=0;m<p.length;m++)f=p[m],f.noOfChildren=f.getNoOfChildren()},h.prototype.calcGrid=function(f){var p=0,m=0;p=parseInt(Math.ceil((f.getRight()-f.getLeft())/this.repulsionRange)),m=parseInt(Math.ceil((f.getBottom()-f.getTop())/this.repulsionRange));for(var g=new Array(p),y=0;y<p;y++)g[y]=new Array(m);for(var y=0;y<p;y++)for(var v=0;v<m;v++)g[y][v]=new Array;return g},h.prototype.addNodeToGrid=function(f,p,m){var g=0,y=0,v=0,x=0;g=parseInt(Math.floor((f.getRect().x-p)/this.repulsionRange)),y=parseInt(Math.floor((f.getRect().width+f.getRect().x-p)/this.repulsionRange)),v=parseInt(Math.floor((f.getRect().y-m)/this.repulsionRange)),x=parseInt(Math.floor((f.getRect().height+f.getRect().y-m)/this.repulsionRange));for(var b=g;b<=y;b++)for(var T=v;T<=x;T++)this.grid[b][T].push(f),f.setGridCoordinates(g,y,v,x)},h.prototype.updateGrid=function(){var f,p,m=this.getAllNodes();for(this.grid=this.calcGrid(this.graphManager.getRoot()),f=0;f<m.length;f++)p=m[f],this.addNodeToGrid(p,this.graphManager.getRoot().getLeft(),this.graphManager.getRoot().getTop())},h.prototype.calculateRepulsionForceOfANode=function(f,p,m,g){if(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&m||g){var y=new Set;f.surrounding=new Array;for(var v,x=this.grid,b=f.startX-1;b<f.finishX+2;b++)for(var T=f.startY-1;T<f.finishY+2;T++)if(!(b<0||T<0||b>=x.length||T>=x[0].length)){for(var w=0;w<x[b][T].length;w++)if(v=x[b][T][w],!(f.getOwner()!=v.getOwner()||f==v)&&!p.has(v)&&!y.has(v)){var C=Math.abs(f.getCenterX()-v.getCenterX())-(f.getWidth()/2+v.getWidth()/2),k=Math.abs(f.getCenterY()-v.getCenterY())-(f.getHeight()/2+v.getHeight()/2);C<=this.repulsionRange&&k<=this.repulsionRange&&y.add(v)}}f.surrounding=[].concat(i(y))}for(b=0;b<f.surrounding.length;b++)this.calcRepulsionForce(f,f.surrounding[b])},h.prototype.calcRepulsionRange=function(){return 0},e.exports=h}),(function(e,t,r){"use strict";var i=r(1),n=r(4);function a(l,u,h){i.call(this,l,u,h),this.idealLength=n.DEFAULT_EDGE_LENGTH,this.edgeElasticity=n.DEFAULT_SPRING_STRENGTH}o(a,"FDLayoutEdge"),a.prototype=Object.create(i.prototype);for(var s in i)a[s]=i[s];e.exports=a}),(function(e,t,r){"use strict";var i=r(3),n=r(4);function 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o(l,"Quicksort"),i(l,[{key:"_quicksort",value:o(function(h,d,f){if(d<f){var p=this._partition(h,d,f);this._quicksort(h,d,p),this._quicksort(h,p+1,f)}},"_quicksort")},{key:"_partition",value:o(function(h,d,f){for(var p=this._get(h,d),m=d,g=f;;){for(;this.compareFunction(p,this._get(h,g));)g--;for(;this.compareFunction(this._get(h,m),p);)m++;if(m<g)this._swap(h,m,g),m++,g--;else return g}},"_partition")},{key:"_get",value:o(function(h,d){return h instanceof a?h.get_object_at(d):h[d]},"_get")},{key:"_set",value:o(function(h,d,f){h instanceof a?h.set_object_at(d,f):h[d]=f},"_set")},{key:"_swap",value:o(function(h,d,f){var p=this._get(h,d);this._set(h,d,this._get(h,f)),this._set(h,f,p)},"_swap")},{key:"_defaultCompareFunction",value:o(function(h,d){return d>h},"_defaultCompareFunction")}]),l})();e.exports=s}),(function(e,t,r){"use strict";function i(){}o(i,"SVD"),i.svd=function(n){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=n.length,this.n=n[0].length;var a=Math.min(this.m,this.n);this.s=(function(Et){for(var yt=[];Et-- >0;)yt.push(0);return yt})(Math.min(this.m+1,this.n)),this.U=(function(Et){var yt=o(function oe(ht){if(ht.length==0)return 0;for(var gt=[],et=0;et<ht[0];et++)gt.push(oe(ht.slice(1)));return gt},"allocate");return yt(Et)})([this.m,a]),this.V=(function(Et){var yt=o(function oe(ht){if(ht.length==0)return 0;for(var gt=[],et=0;et<ht[0];et++)gt.push(oe(ht.slice(1)));return gt},"allocate");return yt(Et)})([this.n,this.n]);for(var s=(function(Et){for(var yt=[];Et-- >0;)yt.push(0);return yt})(this.n),l=(function(Et){for(var yt=[];Et-- >0;)yt.push(0);return yt})(this.m),u=!0,h=!0,d=Math.min(this.m-1,this.n),f=Math.max(0,Math.min(this.n-2,this.m)),p=0;p<Math.max(d,f);p++){if(p<d){this.s[p]=0;for(var m=p;m<this.m;m++)this.s[p]=i.hypot(this.s[p],n[m][p]);if(this.s[p]!==0){n[p][p]<0&&(this.s[p]=-this.s[p]);for(var g=p;g<this.m;g++)n[g][p]/=this.s[p];n[p][p]+=1}this.s[p]=-this.s[p]}for(var y=p+1;y<this.n;y++){if((function(Et,yt){return Et&&yt})(p<d,this.s[p]!==0)){for(var v=0,x=p;x<this.m;x++)v+=n[x][p]*n[x][y];v=-v/n[p][p];for(var b=p;b<this.m;b++)n[b][y]+=v*n[b][p]}s[y]=n[p][y]}if((function(Et,yt){return Et&&yt})(u,p<d))for(var T=p;T<this.m;T++)this.U[T][p]=n[T][p];if(p<f){s[p]=0;for(var w=p+1;w<this.n;w++)s[p]=i.hypot(s[p],s[w]);if(s[p]!==0){s[p+1]<0&&(s[p]=-s[p]);for(var C=p+1;C<this.n;C++)s[C]/=s[p];s[p+1]+=1}if(s[p]=-s[p],(function(Et,yt){return Et&&yt})(p+1<this.m,s[p]!==0)){for(var k=p+1;k<this.m;k++)l[k]=0;for(var E=p+1;E<this.n;E++)for(var A=p+1;A<this.m;A++)l[A]+=s[E]*n[A][E];for(var N=p+1;N<this.n;N++)for(var P=-s[N]/s[p+1],I=p+1;I<this.m;I++)n[I][N]+=P*l[I]}if(h)for(var D=p+1;D<this.n;D++)this.V[D][p]=s[D]}}var _=Math.min(this.n,this.m+1);if(d<this.n&&(this.s[d]=n[d][d]),this.m<_&&(this.s[_-1]=0),f+1<_&&(s[f]=n[f][_-1]),s[_-1]=0,u){for(var R=d;R<a;R++){for(var M=0;M<this.m;M++)this.U[M][R]=0;this.U[R][R]=1}for(var L=d-1;L>=0;L--)if(this.s[L]!==0){for(var B=L+1;B<a;B++){for(var O=0,$=L;$<this.m;$++)O+=this.U[$][L]*this.U[$][B];O=-O/this.U[L][L];for(var G=L;G<this.m;G++)this.U[G][B]+=O*this.U[G][L]}for(var F=L;F<this.m;F++)this.U[F][L]=-this.U[F][L];this.U[L][L]=1+this.U[L][L];for(var V=0;V<L-1;V++)this.U[V][L]=0}else{for(var H=0;H<this.m;H++)this.U[H][L]=0;this.U[L][L]=1}}if(h)for(var j=this.n-1;j>=0;j--){if((function(Et,yt){return Et&&yt})(j<f,s[j]!==0))for(var U=j+1;U<a;U++){for(var Q=0,Y=j+1;Y<this.n;Y++)Q+=this.V[Y][j]*this.V[Y][U];Q=-Q/this.V[j+1][j];for(var ae=j+1;ae<this.n;ae++)this.V[ae][U]+=Q*this.V[ae][j]}for(var J=0;J<this.n;J++)this.V[J][j]=0;this.V[j][j]=1}for(var te=_-1,re=0,ee=Math.pow(2,-52),Te=Math.pow(2,-966);_>0;){var ue=void 0,De=void 0;for(ue=_-2;ue>=-1&&ue!==-1;ue--)if(Math.abs(s[ue])<=Te+ee*(Math.abs(this.s[ue])+Math.abs(this.s[ue+1]))){s[ue]=0;break}if(ue===_-2)De=4;else{var Ie=void 0;for(Ie=_-1;Ie>=ue&&Ie!==ue;Ie--){var Ee=(Ie!==_?Math.abs(s[Ie]):0)+(Ie!==ue+1?Math.abs(s[Ie-1]):0);if(Math.abs(this.s[Ie])<=Te+ee*Ee){this.s[Ie]=0;break}}Ie===ue?De=3:Ie===_-1?De=1:(De=2,ue=Ie)}switch(ue++,De){case 1:{var we=s[_-2];s[_-2]=0;for(var Me=_-2;Me>=ue;Me--){var $e=i.hypot(this.s[Me],we),de=this.s[Me]/$e,rt=we/$e;if(this.s[Me]=$e,Me!==ue&&(we=-rt*s[Me-1],s[Me-1]=de*s[Me-1]),h)for(var ke=0;ke<this.n;ke++)$e=de*this.V[ke][Me]+rt*this.V[ke][_-1],this.V[ke][_-1]=-rt*this.V[ke][Me]+de*this.V[ke][_-1],this.V[ke][Me]=$e}}break;case 2:{var Fe=s[ue-1];s[ue-1]=0;for(var He=ue;He<_;He++){var at=i.hypot(this.s[He],Fe),qe=this.s[He]/at,Ue=Fe/at;if(this.s[He]=at,Fe=-Ue*s[He],s[He]=qe*s[He],u)for(var ye=0;ye<this.m;ye++)at=qe*this.U[ye][He]+Ue*this.U[ye][ue-1],this.U[ye][ue-1]=-Ue*this.U[ye][He]+qe*this.U[ye][ue-1],this.U[ye][He]=at}}break;case 3:{var ve=Math.max(Math.max(Math.max(Math.max(Math.abs(this.s[_-1]),Math.abs(this.s[_-2])),Math.abs(s[_-2])),Math.abs(this.s[ue])),Math.abs(s[ue])),ie=this.s[_-1]/ve,fe=this.s[_-2]/ve,W=s[_-2]/ve,ce=this.s[ue]/ve,K=s[ue]/ve,Re=((fe+ie)*(fe-ie)+W*W)/2,xe=ie*W*(ie*W),Oe=0;(function(Et,yt){return Et||yt})(Re!==0,xe!==0)&&(Oe=Math.sqrt(Re*Re+xe),Re<0&&(Oe=-Oe),Oe=xe/(Re+Oe));for(var be=(ce+ie)*(ce-ie)+Oe,Be=ce*K,Ae=ue;Ae<_-1;Ae++){var Ve=i.hypot(be,Be),Pe=be/Ve,Ye=Be/Ve;if(Ae!==ue&&(s[Ae-1]=Ve),be=Pe*this.s[Ae]+Ye*s[Ae],s[Ae]=Pe*s[Ae]-Ye*this.s[Ae],Be=Ye*this.s[Ae+1],this.s[Ae+1]=Pe*this.s[Ae+1],h)for(var le=0;le<this.n;le++)Ve=Pe*this.V[le][Ae]+Ye*this.V[le][Ae+1],this.V[le][Ae+1]=-Ye*this.V[le][Ae]+Pe*this.V[le][Ae+1],this.V[le][Ae]=Ve;if(Ve=i.hypot(be,Be),Pe=be/Ve,Ye=Be/Ve,this.s[Ae]=Ve,be=Pe*s[Ae]+Ye*this.s[Ae+1],this.s[Ae+1]=-Ye*s[Ae]+Pe*this.s[Ae+1],Be=Ye*s[Ae+1],s[Ae+1]=Pe*s[Ae+1],u&&Ae<this.m-1)for(var st=0;st<this.m;st++)Ve=Pe*this.U[st][Ae]+Ye*this.U[st][Ae+1],this.U[st][Ae+1]=-Ye*this.U[st][Ae]+Pe*this.U[st][Ae+1],this.U[st][Ae]=Ve}s[_-2]=be,re=re+1}break;case 4:{if(this.s[ue]<=0&&(this.s[ue]=this.s[ue]<0?-this.s[ue]:0,h))for(var me=0;me<=te;me++)this.V[me][ue]=-this.V[me][ue];for(;ue<te&&!(this.s[ue]>=this.s[ue+1]);){var ot=this.s[ue];if(this.s[ue]=this.s[ue+1],this.s[ue+1]=ot,h&&ue<this.n-1)for(var kt=0;kt<this.n;kt++)ot=this.V[kt][ue+1],this.V[kt][ue+1]=this.V[kt][ue],this.V[kt][ue]=ot;if(u&&ue<this.m-1)for(var Gt=0;Gt<this.m;Gt++)ot=this.U[Gt][ue+1],this.U[Gt][ue+1]=this.U[Gt][ue],this.U[Gt][ue]=ot;ue++}re=0,_--}break}}var Tt={U:this.U,V:this.V,S:this.s};return Tt},i.hypot=function(n,a){var s=void 0;return Math.abs(n)>Math.abs(a)?(s=a/n,s=Math.abs(n)*Math.sqrt(1+s*s)):a!=0?(s=n/a,s=Math.abs(a)*Math.sqrt(1+s*s)):s=0,s},e.exports=i}),(function(e,t,r){"use strict";var i=(function(){function s(l,u){for(var h=0;h<u.length;h++){var d=u[h];d.enumerable=d.enumerable||!1,d.configurable=!0,"value"in d&&(d.writable=!0),Object.defineProperty(l,d.key,d)}}return o(s,"defineProperties"),function(l,u,h){return u&&s(l.prototype,u),h&&s(l,h),l}})();function n(s,l){if(!(s instanceof l))throw new TypeError("Cannot call a class as a function")}o(n,"_classCallCheck");var a=(function(){function s(l,u){var h=arguments.length>2&&arguments[2]!==void 0?arguments[2]:1,d=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,f=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;n(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=d,this.gap_penalty=f,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p<this.iMax;p++){this.grid[p]=new Array(this.jMax);for(var m=0;m<this.jMax;m++)this.grid[p][m]=0}this.tracebackGrid=new Array(this.iMax);for(var g=0;g<this.iMax;g++){this.tracebackGrid[g]=new Array(this.jMax);for(var y=0;y<this.jMax;y++)this.tracebackGrid[g][y]=[null,null,null]}this.alignments=[],this.score=-1,this.computeGrids()}return o(s,"NeedlemanWunsch"),i(s,[{key:"getScore",value:o(function(){return this.score},"getScore")},{key:"getAlignments",value:o(function(){return this.alignments},"getAlignments")},{key:"computeGrids",value:o(function(){for(var u=1;u<this.jMax;u++)this.grid[0][u]=this.grid[0][u-1]+this.gap_penalty,this.tracebackGrid[0][u]=[!1,!1,!0];for(var h=1;h<this.iMax;h++)this.grid[h][0]=this.grid[h-1][0]+this.gap_penalty,this.tracebackGrid[h][0]=[!1,!0,!1];for(var d=1;d<this.iMax;d++)for(var f=1;f<this.jMax;f++){var p=void 0;this.sequence1[d-1]===this.sequence2[f-1]?p=this.grid[d-1][f-1]+this.match_score:p=this.grid[d-1][f-1]+this.mismatch_penalty;var m=this.grid[d-1][f]+this.gap_penalty,g=this.grid[d][f-1]+this.gap_penalty,y=[p,m,g],v=this.arrayAllMaxIndexes(y);this.grid[d][f]=y[v[0]],this.tracebackGrid[d][f]=[v.includes(0),v.includes(1),v.includes(2)]}this.score=this.grid[this.iMax-1][this.jMax-1]},"computeGrids")},{key:"alignmentTraceback",value:o(function(){var u=[];for(u.push({pos:[this.sequence1.length,this.sequence2.length],seq1:"",seq2:""});u[0];){var h=u[0],d=this.tracebackGrid[h.pos[0]][h.pos[1]];d[0]&&u.push({pos:[h.pos[0]-1,h.pos[1]-1],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),d[1]&&u.push({pos:[h.pos[0]-1,h.pos[1]],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:"-"+h.seq2}),d[2]&&u.push({pos:[h.pos[0],h.pos[1]-1],seq1:"-"+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),h.pos[0]===0&&h.pos[1]===0&&this.alignments.push({sequence1:h.seq1,sequence2:h.seq2}),u.shift()}return this.alignments},"alignmentTraceback")},{key:"getAllIndexes",value:o(function(u,h){for(var d=[],f=-1;(f=u.indexOf(h,f+1))!==-1;)d.push(f);return d},"getAllIndexes")},{key:"arrayAllMaxIndexes",value:o(function(u){return this.getAllIndexes(u,Math.max.apply(null,u))},"arrayAllMaxIndexes")}]),s})();e.exports=a}),(function(e,t,r){"use strict";var i=o(function(){},"layoutBase");i.FDLayout=r(18),i.FDLayoutConstants=r(4),i.FDLayoutEdge=r(19),i.FDLayoutNode=r(20),i.DimensionD=r(21),i.HashMap=r(22),i.HashSet=r(23),i.IGeometry=r(8),i.IMath=r(9),i.Integer=r(10),i.Point=r(12),i.PointD=r(5),i.RandomSeed=r(16),i.RectangleD=r(13),i.Transform=r(17),i.UniqueIDGeneretor=r(14),i.Quicksort=r(25),i.LinkedList=r(11),i.LGraphObject=r(2),i.LGraph=r(6),i.LEdge=r(1),i.LGraphManager=r(7),i.LNode=r(3),i.Layout=r(15),i.LayoutConstants=r(0),i.NeedlemanWunsch=r(27),i.Matrix=r(24),i.SVD=r(26),e.exports=i}),(function(e,t,r){"use strict";function i(){this.listeners=[]}o(i,"Emitter");var n=i.prototype;n.addListener=function(a,s){this.listeners.push({event:a,callback:s})},n.removeListener=function(a,s){for(var l=this.listeners.length;l>=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},n.emit=function(a,s){for(var l=0;l<this.listeners.length;l++){var u=this.listeners[l];a===u.event&&u.callback(s)}},e.exports=i})])})});var qq=Js((hC,Wq)=>{"use strict";o((function(t,r){typeof hC=="object"&&typeof Wq=="object"?Wq.exports=r(Vq()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof hC=="object"?hC.coseBase=r(Vq()):t.coseBase=r(t.layoutBase)}),"webpackUniversalModuleDefinition")(hC,function(e){return(()=>{"use strict";var t={45:((a,s,l)=>{var u={};u.layoutBase=l(551),u.CoSEConstants=l(806),u.CoSEEdge=l(767),u.CoSEGraph=l(880),u.CoSEGraphManager=l(578),u.CoSELayout=l(765),u.CoSENode=l(991),u.ConstraintHandler=l(902),a.exports=u}),806:((a,s,l)=>{var u=l(551).FDLayoutConstants;function h(){}o(h,"CoSEConstants");for(var d in u)h[d]=u[d];h.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,h.DEFAULT_RADIAL_SEPARATION=u.DEFAULT_EDGE_LENGTH,h.DEFAULT_COMPONENT_SEPERATION=60,h.TILE=!0,h.TILING_PADDING_VERTICAL=10,h.TILING_PADDING_HORIZONTAL=10,h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0,h.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,h.TREE_REDUCTION_ON_INCREMENTAL=!0,h.PURE_INCREMENTAL=h.DEFAULT_INCREMENTAL,a.exports=h}),767:((a,s,l)=>{var u=l(551).FDLayoutEdge;function h(f,p,m){u.call(this,f,p,m)}o(h,"CoSEEdge"),h.prototype=Object.create(u.prototype);for(var d in u)h[d]=u[d];a.exports=h}),880:((a,s,l)=>{var u=l(551).LGraph;function h(f,p,m){u.call(this,f,p,m)}o(h,"CoSEGraph"),h.prototype=Object.create(u.prototype);for(var d in u)h[d]=u[d];a.exports=h}),578:((a,s,l)=>{var u=l(551).LGraphManager;function h(f){u.call(this,f)}o(h,"CoSEGraphManager"),h.prototype=Object.create(u.prototype);for(var d in u)h[d]=u[d];a.exports=h}),765:((a,s,l)=>{var u=l(551).FDLayout,h=l(578),d=l(880),f=l(991),p=l(767),m=l(806),g=l(902),y=l(551).FDLayoutConstants,v=l(551).LayoutConstants,x=l(551).Point,b=l(551).PointD,T=l(551).DimensionD,w=l(551).Layout,C=l(551).Integer,k=l(551).IGeometry,E=l(551).LGraph,A=l(551).Transform,N=l(551).LinkedList;function P(){u.call(this),this.toBeTiled={},this.constraints={}}o(P,"CoSELayout"),P.prototype=Object.create(u.prototype);for(var I in u)P[I]=u[I];P.prototype.newGraphManager=function(){var D=new h(this);return this.graphManager=D,D},P.prototype.newGraph=function(D){return new d(null,this.graphManager,D)},P.prototype.newNode=function(D){return new f(this.graphManager,D)},P.prototype.newEdge=function(D){return new p(null,null,D)},P.prototype.initParameters=function(){u.prototype.initParameters.call(this,arguments),this.isSubLayout||(m.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=m.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=m.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=y.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=y.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=y.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},P.prototype.initSpringEmbedder=function(){u.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/y.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},P.prototype.layout=function(){var D=v.DEFAULT_CREATE_BENDS_AS_NEEDED;return D&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},P.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(m.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var _=new Set(this.getAllNodes()),R=this.nodesWithGravity.filter(function(B){return _.has(B)});this.graphManager.setAllNodesToApplyGravitation(R)}}else{var D=this.getFlatForest();if(D.length>0)this.positionNodesRadially(D);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var _=new Set(this.getAllNodes()),R=this.nodesWithGravity.filter(function(M){return _.has(M)});this.graphManager.setAllNodesToApplyGravitation(R),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(g.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),m.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},P.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%y.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var D=new Set(this.getAllNodes()),_=this.nodesWithGravity.filter(function(L){return D.has(L)});this.graphManager.setAllNodesToApplyGravitation(_),this.graphManager.updateBounds(),this.updateGrid(),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var R=!this.isTreeGrowing&&!this.isGrowthFinished,M=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(R,M),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},P.prototype.getPositionsData=function(){for(var D=this.graphManager.getAllNodes(),_={},R=0;R<D.length;R++){var M=D[R].rect,L=D[R].id;_[L]={id:L,x:M.getCenterX(),y:M.getCenterY(),w:M.width,h:M.height}}return _},P.prototype.runSpringEmbedder=function(){this.initialAnimationPeriod=25,this.animationPeriod=this.initialAnimationPeriod;var D=!1;if(y.ANIMATE==="during")this.emit("layoutstarted");else{for(;!D;)D=this.tick();this.graphManager.updateBounds()}},P.prototype.moveNodes=function(){for(var D=this.getAllNodes(),_,R=0;R<D.length;R++)_=D[R],_.calculateDisplacement();Object.keys(this.constraints).length>0&&this.updateDisplacements();for(var R=0;R<D.length;R++)_=D[R],_.move()},P.prototype.initConstraintVariables=function(){var D=this;this.idToNodeMap=new Map,this.fixedNodeSet=new Set;for(var _=this.graphManager.getAllNodes(),R=0;R<_.length;R++){var M=_[R];this.idToNodeMap.set(M.id,M)}var L=o(function Y(ae){for(var J=ae.getChild().getNodes(),te,re=0,ee=0;ee<J.length;ee++)te=J[ee],te.getChild()==null?D.fixedNodeSet.has(te.id)&&(re+=100):re+=Y(te);return re},"calculateCompoundWeight");if(this.constraints.fixedNodeConstraint){this.constraints.fixedNodeConstraint.forEach(function(J){D.fixedNodeSet.add(J.nodeId)});for(var _=this.graphManager.getAllNodes(),M,R=0;R<_.length;R++)if(M=_[R],M.getChild()!=null){var B=L(M);B>0&&(M.fixedNodeWeight=B)}}if(this.constraints.relativePlacementConstraint){var O=new Map,$=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(Y){D.fixedNodesOnHorizontal.add(Y),D.fixedNodesOnVertical.add(Y)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var G=this.constraints.alignmentConstraint.vertical,R=0;R<G.length;R++)this.dummyToNodeForVerticalAlignment.set("dummy"+R,[]),G[R].forEach(function(ae){O.set(ae,"dummy"+R),D.dummyToNodeForVerticalAlignment.get("dummy"+R).push(ae),D.fixedNodeSet.has(ae)&&D.fixedNodesOnHorizontal.add("dummy"+R)});if(this.constraints.alignmentConstraint.horizontal)for(var F=this.constraints.alignmentConstraint.horizontal,R=0;R<F.length;R++)this.dummyToNodeForHorizontalAlignment.set("dummy"+R,[]),F[R].forEach(function(ae){$.set(ae,"dummy"+R),D.dummyToNodeForHorizontalAlignment.get("dummy"+R).push(ae),D.fixedNodeSet.has(ae)&&D.fixedNodesOnVertical.add("dummy"+R)})}if(m.RELAX_MOVEMENT_ON_CONSTRAINTS)this.shuffle=function(Y){var ae,J,te;for(te=Y.length-1;te>=2*Y.length/3;te--)ae=Math.floor(Math.random()*(te+1)),J=Y[te],Y[te]=Y[ae],Y[ae]=J;return Y},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(Y){if(Y.left){var ae=O.has(Y.left)?O.get(Y.left):Y.left,J=O.has(Y.right)?O.get(Y.right):Y.right;D.nodesInRelativeHorizontal.includes(ae)||(D.nodesInRelativeHorizontal.push(ae),D.nodeToRelativeConstraintMapHorizontal.set(ae,[]),D.dummyToNodeForVerticalAlignment.has(ae)?D.nodeToTempPositionMapHorizontal.set(ae,D.idToNodeMap.get(D.dummyToNodeForVerticalAlignment.get(ae)[0]).getCenterX()):D.nodeToTempPositionMapHorizontal.set(ae,D.idToNodeMap.get(ae).getCenterX())),D.nodesInRelativeHorizontal.includes(J)||(D.nodesInRelativeHorizontal.push(J),D.nodeToRelativeConstraintMapHorizontal.set(J,[]),D.dummyToNodeForVerticalAlignment.has(J)?D.nodeToTempPositionMapHorizontal.set(J,D.idToNodeMap.get(D.dummyToNodeForVerticalAlignment.get(J)[0]).getCenterX()):D.nodeToTempPositionMapHorizontal.set(J,D.idToNodeMap.get(J).getCenterX())),D.nodeToRelativeConstraintMapHorizontal.get(ae).push({right:J,gap:Y.gap}),D.nodeToRelativeConstraintMapHorizontal.get(J).push({left:ae,gap:Y.gap})}else{var te=$.has(Y.top)?$.get(Y.top):Y.top,re=$.has(Y.bottom)?$.get(Y.bottom):Y.bottom;D.nodesInRelativeVertical.includes(te)||(D.nodesInRelativeVertical.push(te),D.nodeToRelativeConstraintMapVertical.set(te,[]),D.dummyToNodeForHorizontalAlignment.has(te)?D.nodeToTempPositionMapVertical.set(te,D.idToNodeMap.get(D.dummyToNodeForHorizontalAlignment.get(te)[0]).getCenterY()):D.nodeToTempPositionMapVertical.set(te,D.idToNodeMap.get(te).getCenterY())),D.nodesInRelativeVertical.includes(re)||(D.nodesInRelativeVertical.push(re),D.nodeToRelativeConstraintMapVertical.set(re,[]),D.dummyToNodeForHorizontalAlignment.has(re)?D.nodeToTempPositionMapVertical.set(re,D.idToNodeMap.get(D.dummyToNodeForHorizontalAlignment.get(re)[0]).getCenterY()):D.nodeToTempPositionMapVertical.set(re,D.idToNodeMap.get(re).getCenterY())),D.nodeToRelativeConstraintMapVertical.get(te).push({bottom:re,gap:Y.gap}),D.nodeToRelativeConstraintMapVertical.get(re).push({top:te,gap:Y.gap})}});else{var V=new Map,H=new Map;this.constraints.relativePlacementConstraint.forEach(function(Y){if(Y.left){var ae=O.has(Y.left)?O.get(Y.left):Y.left,J=O.has(Y.right)?O.get(Y.right):Y.right;V.has(ae)?V.get(ae).push(J):V.set(ae,[J]),V.has(J)?V.get(J).push(ae):V.set(J,[ae])}else{var te=$.has(Y.top)?$.get(Y.top):Y.top,re=$.has(Y.bottom)?$.get(Y.bottom):Y.bottom;H.has(te)?H.get(te).push(re):H.set(te,[re]),H.has(re)?H.get(re).push(te):H.set(re,[te])}});var j=o(function(ae,J){var te=[],re=[],ee=new N,Te=new Set,ue=0;return ae.forEach(function(De,Ie){if(!Te.has(Ie)){te[ue]=[],re[ue]=!1;var Ee=Ie;for(ee.push(Ee),Te.add(Ee),te[ue].push(Ee);ee.length!=0;){Ee=ee.shift(),J.has(Ee)&&(re[ue]=!0);var we=ae.get(Ee);we.forEach(function(Me){Te.has(Me)||(ee.push(Me),Te.add(Me),te[ue].push(Me))})}ue++}}),{components:te,isFixed:re}},"constructComponents"),U=j(V,D.fixedNodesOnHorizontal);this.componentsOnHorizontal=U.components,this.fixedComponentsOnHorizontal=U.isFixed;var Q=j(H,D.fixedNodesOnVertical);this.componentsOnVertical=Q.components,this.fixedComponentsOnVertical=Q.isFixed}}},P.prototype.updateDisplacements=function(){var D=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(Q){var Y=D.idToNodeMap.get(Q.nodeId);Y.displacementX=0,Y.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var _=this.constraints.alignmentConstraint.vertical,R=0;R<_.length;R++){for(var M=0,L=0;L<_[R].length;L++){if(this.fixedNodeSet.has(_[R][L])){M=0;break}M+=this.idToNodeMap.get(_[R][L]).displacementX}for(var B=M/_[R].length,L=0;L<_[R].length;L++)this.idToNodeMap.get(_[R][L]).displacementX=B}if(this.constraints.alignmentConstraint.horizontal)for(var O=this.constraints.alignmentConstraint.horizontal,R=0;R<O.length;R++){for(var $=0,L=0;L<O[R].length;L++){if(this.fixedNodeSet.has(O[R][L])){$=0;break}$+=this.idToNodeMap.get(O[R][L]).displacementY}for(var G=$/O[R].length,L=0;L<O[R].length;L++)this.idToNodeMap.get(O[R][L]).displacementY=G}}if(this.constraints.relativePlacementConstraint)if(m.RELAX_MOVEMENT_ON_CONSTRAINTS)this.totalIterations%10==0&&(this.shuffle(this.nodesInRelativeHorizontal),this.shuffle(this.nodesInRelativeVertical)),this.nodesInRelativeHorizontal.forEach(function(Q){if(!D.fixedNodesOnHorizontal.has(Q)){var Y=0;D.dummyToNodeForVerticalAlignment.has(Q)?Y=D.idToNodeMap.get(D.dummyToNodeForVerticalAlignment.get(Q)[0]).displacementX:Y=D.idToNodeMap.get(Q).displacementX,D.nodeToRelativeConstraintMapHorizontal.get(Q).forEach(function(ae){if(ae.right){var J=D.nodeToTempPositionMapHorizontal.get(ae.right)-D.nodeToTempPositionMapHorizontal.get(Q)-Y;J<ae.gap&&(Y-=ae.gap-J)}else{var J=D.nodeToTempPositionMapHorizontal.get(Q)-D.nodeToTempPositionMapHorizontal.get(ae.left)+Y;J<ae.gap&&(Y+=ae.gap-J)}}),D.nodeToTempPositionMapHorizontal.set(Q,D.nodeToTempPositionMapHorizontal.get(Q)+Y),D.dummyToNodeForVerticalAlignment.has(Q)?D.dummyToNodeForVerticalAlignment.get(Q).forEach(function(ae){D.idToNodeMap.get(ae).displacementX=Y}):D.idToNodeMap.get(Q).displacementX=Y}}),this.nodesInRelativeVertical.forEach(function(Q){if(!D.fixedNodesOnHorizontal.has(Q)){var Y=0;D.dummyToNodeForHorizontalAlignment.has(Q)?Y=D.idToNodeMap.get(D.dummyToNodeForHorizontalAlignment.get(Q)[0]).displacementY:Y=D.idToNodeMap.get(Q).displacementY,D.nodeToRelativeConstraintMapVertical.get(Q).forEach(function(ae){if(ae.bottom){var J=D.nodeToTempPositionMapVertical.get(ae.bottom)-D.nodeToTempPositionMapVertical.get(Q)-Y;J<ae.gap&&(Y-=ae.gap-J)}else{var J=D.nodeToTempPositionMapVertical.get(Q)-D.nodeToTempPositionMapVertical.get(ae.top)+Y;J<ae.gap&&(Y+=ae.gap-J)}}),D.nodeToTempPositionMapVertical.set(Q,D.nodeToTempPositionMapVertical.get(Q)+Y),D.dummyToNodeForHorizontalAlignment.has(Q)?D.dummyToNodeForHorizontalAlignment.get(Q).forEach(function(ae){D.idToNodeMap.get(ae).displacementY=Y}):D.idToNodeMap.get(Q).displacementY=Y}});else{for(var R=0;R<this.componentsOnHorizontal.length;R++){var F=this.componentsOnHorizontal[R];if(this.fixedComponentsOnHorizontal[R])for(var L=0;L<F.length;L++)this.dummyToNodeForVerticalAlignment.has(F[L])?this.dummyToNodeForVerticalAlignment.get(F[L]).forEach(function(ae){D.idToNodeMap.get(ae).displacementX=0}):this.idToNodeMap.get(F[L]).displacementX=0;else{for(var V=0,H=0,L=0;L<F.length;L++)if(this.dummyToNodeForVerticalAlignment.has(F[L])){var j=this.dummyToNodeForVerticalAlignment.get(F[L]);V+=j.length*this.idToNodeMap.get(j[0]).displacementX,H+=j.length}else V+=this.idToNodeMap.get(F[L]).displacementX,H++;for(var U=V/H,L=0;L<F.length;L++)this.dummyToNodeForVerticalAlignment.has(F[L])?this.dummyToNodeForVerticalAlignment.get(F[L]).forEach(function(ae){D.idToNodeMap.get(ae).displacementX=U}):this.idToNodeMap.get(F[L]).displacementX=U}}for(var R=0;R<this.componentsOnVertical.length;R++){var F=this.componentsOnVertical[R];if(this.fixedComponentsOnVertical[R])for(var L=0;L<F.length;L++)this.dummyToNodeForHorizontalAlignment.has(F[L])?this.dummyToNodeForHorizontalAlignment.get(F[L]).forEach(function(J){D.idToNodeMap.get(J).displacementY=0}):this.idToNodeMap.get(F[L]).displacementY=0;else{for(var V=0,H=0,L=0;L<F.length;L++)if(this.dummyToNodeForHorizontalAlignment.has(F[L])){var j=this.dummyToNodeForHorizontalAlignment.get(F[L]);V+=j.length*this.idToNodeMap.get(j[0]).displacementY,H+=j.length}else V+=this.idToNodeMap.get(F[L]).displacementY,H++;for(var U=V/H,L=0;L<F.length;L++)this.dummyToNodeForHorizontalAlignment.has(F[L])?this.dummyToNodeForHorizontalAlignment.get(F[L]).forEach(function(ee){D.idToNodeMap.get(ee).displacementY=U}):this.idToNodeMap.get(F[L]).displacementY=U}}}},P.prototype.calculateNodesToApplyGravitationTo=function(){var D=[],_,R=this.graphManager.getGraphs(),M=R.length,L;for(L=0;L<M;L++)_=R[L],_.updateConnected(),_.isConnected||(D=D.concat(_.getNodes()));return D},P.prototype.createBendpoints=function(){var D=[];D=D.concat(this.graphManager.getAllEdges());var _=new Set,R;for(R=0;R<D.length;R++){var M=D[R];if(!_.has(M)){var L=M.getSource(),B=M.getTarget();if(L==B)M.getBendpoints().push(new b),M.getBendpoints().push(new b),this.createDummyNodesForBendpoints(M),_.add(M);else{var O=[];if(O=O.concat(L.getEdgeListToNode(B)),O=O.concat(B.getEdgeListToNode(L)),!_.has(O[0])){if(O.length>1){var $;for($=0;$<O.length;$++){var G=O[$];G.getBendpoints().push(new b),this.createDummyNodesForBendpoints(G)}}O.forEach(function(F){_.add(F)})}}}if(_.size==D.length)break}},P.prototype.positionNodesRadially=function(D){for(var _=new x(0,0),R=Math.ceil(Math.sqrt(D.length)),M=0,L=0,B=0,O=new b(0,0),$=0;$<D.length;$++){$%R==0&&(B=0,L=M,$!=0&&(L+=m.DEFAULT_COMPONENT_SEPERATION),M=0);var G=D[$],F=w.findCenterOfTree(G);_.x=B,_.y=L,O=P.radialLayout(G,F,_),O.y>M&&(M=Math.floor(O.y)),B=Math.floor(O.x+m.DEFAULT_COMPONENT_SEPERATION)}this.transform(new b(v.WORLD_CENTER_X-O.x/2,v.WORLD_CENTER_Y-O.y/2))},P.radialLayout=function(D,_,R){var M=Math.max(this.maxDiagonalInTree(D),m.DEFAULT_RADIAL_SEPARATION);P.branchRadialLayout(_,null,0,359,0,M);var L=E.calculateBounds(D),B=new A;B.setDeviceOrgX(L.getMinX()),B.setDeviceOrgY(L.getMinY()),B.setWorldOrgX(R.x),B.setWorldOrgY(R.y);for(var O=0;O<D.length;O++){var $=D[O];$.transform(B)}var G=new b(L.getMaxX(),L.getMaxY());return B.inverseTransformPoint(G)},P.branchRadialLayout=function(D,_,R,M,L,B){var O=(M-R+1)/2;O<0&&(O+=180);var $=(O+R)%360,G=$*k.TWO_PI/360,F=Math.cos(G),V=L*Math.cos(G),H=L*Math.sin(G);D.setCenter(V,H);var j=[];j=j.concat(D.getEdges());var U=j.length;_!=null&&U--;for(var Q=0,Y=j.length,ae,J=D.getEdgesBetween(_);J.length>1;){var te=J[0];J.splice(0,1);var re=j.indexOf(te);re>=0&&j.splice(re,1),Y--,U--}_!=null?ae=(j.indexOf(J[0])+1)%Y:ae=0;for(var ee=Math.abs(M-R)/U,Te=ae;Q!=U;Te=++Te%Y){var ue=j[Te].getOtherEnd(D);if(ue!=_){var De=(R+Q*ee)%360,Ie=(De+ee)%360;P.branchRadialLayout(ue,D,De,Ie,L+B,B),Q++}}},P.maxDiagonalInTree=function(D){for(var _=C.MIN_VALUE,R=0;R<D.length;R++){var M=D[R],L=M.getDiagonal();L>_&&(_=L)}return _},P.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},P.prototype.groupZeroDegreeMembers=function(){var D=this,_={};this.memberGroups={},this.idToDummyNode={};for(var R=[],M=this.graphManager.getAllNodes(),L=0;L<M.length;L++){var B=M[L],O=B.getParent();this.getNodeDegreeWithChildren(B)===0&&(O.id==null||!this.getToBeTiled(O))&&R.push(B)}for(var L=0;L<R.length;L++){var B=R[L],$=B.getParent().id;typeof _[$]>"u"&&(_[$]=[]),_[$]=_[$].concat(B)}Object.keys(_).forEach(function(G){if(_[G].length>1){var F="DummyCompound_"+G;D.memberGroups[F]=_[G];var V=_[G][0].getParent(),H=new f(D.graphManager);H.id=F,H.paddingLeft=V.paddingLeft||0,H.paddingRight=V.paddingRight||0,H.paddingBottom=V.paddingBottom||0,H.paddingTop=V.paddingTop||0,D.idToDummyNode[F]=H;var j=D.getGraphManager().add(D.newGraph(),H),U=V.getChild();U.add(H);for(var Q=0;Q<_[G].length;Q++){var Y=_[G][Q];U.remove(Y),j.add(Y)}}})},P.prototype.clearCompounds=function(){var D={},_={};this.performDFSOnCompounds();for(var R=0;R<this.compoundOrder.length;R++)_[this.compoundOrder[R].id]=this.compoundOrder[R],D[this.compoundOrder[R].id]=[].concat(this.compoundOrder[R].getChild().getNodes()),this.graphManager.remove(this.compoundOrder[R].getChild()),this.compoundOrder[R].child=null;this.graphManager.resetAllNodes(),this.tileCompoundMembers(D,_)},P.prototype.clearZeroDegreeMembers=function(){var D=this,_=this.tiledZeroDegreePack=[];Object.keys(this.memberGroups).forEach(function(R){var M=D.idToDummyNode[R];if(_[R]=D.tileNodes(D.memberGroups[R],M.paddingLeft+M.paddingRight),M.rect.width=_[R].width,M.rect.height=_[R].height,M.setCenter(_[R].centerX,_[R].centerY),M.labelMarginLeft=0,M.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var L=M.rect.width,B=M.rect.height;M.labelWidth&&(M.labelPosHorizontal=="left"?(M.rect.x-=M.labelWidth,M.setWidth(L+M.labelWidth),M.labelMarginLeft=M.labelWidth):M.labelPosHorizontal=="center"&&M.labelWidth>L?(M.rect.x-=(M.labelWidth-L)/2,M.setWidth(M.labelWidth),M.labelMarginLeft=(M.labelWidth-L)/2):M.labelPosHorizontal=="right"&&M.setWidth(L+M.labelWidth)),M.labelHeight&&(M.labelPosVertical=="top"?(M.rect.y-=M.labelHeight,M.setHeight(B+M.labelHeight),M.labelMarginTop=M.labelHeight):M.labelPosVertical=="center"&&M.labelHeight>B?(M.rect.y-=(M.labelHeight-B)/2,M.setHeight(M.labelHeight),M.labelMarginTop=(M.labelHeight-B)/2):M.labelPosVertical=="bottom"&&M.setHeight(B+M.labelHeight))}})},P.prototype.repopulateCompounds=function(){for(var D=this.compoundOrder.length-1;D>=0;D--){var _=this.compoundOrder[D],R=_.id,M=_.paddingLeft,L=_.paddingTop,B=_.labelMarginLeft,O=_.labelMarginTop;this.adjustLocations(this.tiledMemberPack[R],_.rect.x,_.rect.y,M,L,B,O)}},P.prototype.repopulateZeroDegreeMembers=function(){var D=this,_=this.tiledZeroDegreePack;Object.keys(_).forEach(function(R){var M=D.idToDummyNode[R],L=M.paddingLeft,B=M.paddingTop,O=M.labelMarginLeft,$=M.labelMarginTop;D.adjustLocations(_[R],M.rect.x,M.rect.y,L,B,O,$)})},P.prototype.getToBeTiled=function(D){var _=D.id;if(this.toBeTiled[_]!=null)return this.toBeTiled[_];var R=D.getChild();if(R==null)return this.toBeTiled[_]=!1,!1;for(var M=R.getNodes(),L=0;L<M.length;L++){var B=M[L];if(this.getNodeDegree(B)>0)return this.toBeTiled[_]=!1,!1;if(B.getChild()==null){this.toBeTiled[B.id]=!1;continue}if(!this.getToBeTiled(B))return this.toBeTiled[_]=!1,!1}return this.toBeTiled[_]=!0,!0},P.prototype.getNodeDegree=function(D){for(var _=D.id,R=D.getEdges(),M=0,L=0;L<R.length;L++){var B=R[L];B.getSource().id!==B.getTarget().id&&(M=M+1)}return M},P.prototype.getNodeDegreeWithChildren=function(D){var _=this.getNodeDegree(D);if(D.getChild()==null)return _;for(var R=D.getChild().getNodes(),M=0;M<R.length;M++){var L=R[M];_+=this.getNodeDegreeWithChildren(L)}return _},P.prototype.performDFSOnCompounds=function(){this.compoundOrder=[],this.fillCompexOrderByDFS(this.graphManager.getRoot().getNodes())},P.prototype.fillCompexOrderByDFS=function(D){for(var _=0;_<D.length;_++){var R=D[_];R.getChild()!=null&&this.fillCompexOrderByDFS(R.getChild().getNodes()),this.getToBeTiled(R)&&this.compoundOrder.push(R)}},P.prototype.adjustLocations=function(D,_,R,M,L,B,O){_+=M+B,R+=L+O;for(var $=_,G=0;G<D.rows.length;G++){var F=D.rows[G];_=$;for(var V=0,H=0;H<F.length;H++){var j=F[H];j.rect.x=_,j.rect.y=R,_+=j.rect.width+D.horizontalPadding,j.rect.height>V&&(V=j.rect.height)}R+=V+D.verticalPadding}},P.prototype.tileCompoundMembers=function(D,_){var R=this;this.tiledMemberPack=[],Object.keys(D).forEach(function(M){var L=_[M];if(R.tiledMemberPack[M]=R.tileNodes(D[M],L.paddingLeft+L.paddingRight),L.rect.width=R.tiledMemberPack[M].width,L.rect.height=R.tiledMemberPack[M].height,L.setCenter(R.tiledMemberPack[M].centerX,R.tiledMemberPack[M].centerY),L.labelMarginLeft=0,L.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var B=L.rect.width,O=L.rect.height;L.labelWidth&&(L.labelPosHorizontal=="left"?(L.rect.x-=L.labelWidth,L.setWidth(B+L.labelWidth),L.labelMarginLeft=L.labelWidth):L.labelPosHorizontal=="center"&&L.labelWidth>B?(L.rect.x-=(L.labelWidth-B)/2,L.setWidth(L.labelWidth),L.labelMarginLeft=(L.labelWidth-B)/2):L.labelPosHorizontal=="right"&&L.setWidth(B+L.labelWidth)),L.labelHeight&&(L.labelPosVertical=="top"?(L.rect.y-=L.labelHeight,L.setHeight(O+L.labelHeight),L.labelMarginTop=L.labelHeight):L.labelPosVertical=="center"&&L.labelHeight>O?(L.rect.y-=(L.labelHeight-O)/2,L.setHeight(L.labelHeight),L.labelMarginTop=(L.labelHeight-O)/2):L.labelPosVertical=="bottom"&&L.setHeight(O+L.labelHeight))}})},P.prototype.tileNodes=function(D,_){var R=this.tileNodesByFavoringDim(D,_,!0),M=this.tileNodesByFavoringDim(D,_,!1),L=this.getOrgRatio(R),B=this.getOrgRatio(M),O;return B<L?O=M:O=R,O},P.prototype.getOrgRatio=function(D){var _=D.width,R=D.height,M=_/R;return M<1&&(M=1/M),M},P.prototype.calcIdealRowWidth=function(D,_){var R=m.TILING_PADDING_VERTICAL,M=m.TILING_PADDING_HORIZONTAL,L=D.length,B=0,O=0,$=0;D.forEach(function(Q){B+=Q.getWidth(),O+=Q.getHeight(),Q.getWidth()>$&&($=Q.getWidth())});var G=B/L,F=O/L,V=Math.pow(R-M,2)+4*(G+M)*(F+R)*L,H=(M-R+Math.sqrt(V))/(2*(G+M)),j;_?(j=Math.ceil(H),j==H&&j++):j=Math.floor(H);var U=j*(G+M)-M;return $>U&&(U=$),U+=M*2,U},P.prototype.tileNodesByFavoringDim=function(D,_,R){var M=m.TILING_PADDING_VERTICAL,L=m.TILING_PADDING_HORIZONTAL,B=m.TILING_COMPARE_BY,O={rows:[],rowWidth:[],rowHeight:[],width:0,height:_,verticalPadding:M,horizontalPadding:L,centerX:0,centerY:0};B&&(O.idealRowWidth=this.calcIdealRowWidth(D,R));var $=o(function(Y){return Y.rect.width*Y.rect.height},"getNodeArea"),G=o(function(Y,ae){return $(ae)-$(Y)},"areaCompareFcn");D.sort(function(Q,Y){var ae=G;return O.idealRowWidth?(ae=B,ae(Q.id,Y.id)):ae(Q,Y)});for(var F=0,V=0,H=0;H<D.length;H++){var j=D[H];F+=j.getCenterX(),V+=j.getCenterY()}O.centerX=F/D.length,O.centerY=V/D.length;for(var H=0;H<D.length;H++){var j=D[H];if(O.rows.length==0)this.insertNodeToRow(O,j,0,_);else if(this.canAddHorizontal(O,j.rect.width,j.rect.height)){var U=O.rows.length-1;O.idealRowWidth||(U=this.getShortestRowIndex(O)),this.insertNodeToRow(O,j,U,_)}else this.insertNodeToRow(O,j,O.rows.length,_);this.shiftToLastRow(O)}return O},P.prototype.insertNodeToRow=function(D,_,R,M){var L=M;if(R==D.rows.length){var B=[];D.rows.push(B),D.rowWidth.push(L),D.rowHeight.push(0)}var O=D.rowWidth[R]+_.rect.width;D.rows[R].length>0&&(O+=D.horizontalPadding),D.rowWidth[R]=O,D.width<O&&(D.width=O);var $=_.rect.height;R>0&&($+=D.verticalPadding);var G=0;$>D.rowHeight[R]&&(G=D.rowHeight[R],D.rowHeight[R]=$,G=D.rowHeight[R]-G),D.height+=G,D.rows[R].push(_)},P.prototype.getShortestRowIndex=function(D){for(var _=-1,R=Number.MAX_VALUE,M=0;M<D.rows.length;M++)D.rowWidth[M]<R&&(_=M,R=D.rowWidth[M]);return _},P.prototype.getLongestRowIndex=function(D){for(var _=-1,R=Number.MIN_VALUE,M=0;M<D.rows.length;M++)D.rowWidth[M]>R&&(_=M,R=D.rowWidth[M]);return _},P.prototype.canAddHorizontal=function(D,_,R){if(D.idealRowWidth){var M=D.rows.length-1,L=D.rowWidth[M];return L+_+D.horizontalPadding<=D.idealRowWidth}var B=this.getShortestRowIndex(D);if(B<0)return!0;var O=D.rowWidth[B];if(O+D.horizontalPadding+_<=D.width)return!0;var $=0;D.rowHeight[B]<R&&B>0&&($=R+D.verticalPadding-D.rowHeight[B]);var G;D.width-O>=_+D.horizontalPadding?G=(D.height+$)/(O+_+D.horizontalPadding):G=(D.height+$)/D.width,$=R+D.verticalPadding;var F;return D.width<_?F=(D.height+$)/_:F=(D.height+$)/D.width,F<1&&(F=1/F),G<1&&(G=1/G),G<F},P.prototype.shiftToLastRow=function(D){var _=this.getLongestRowIndex(D),R=D.rowWidth.length-1,M=D.rows[_],L=M[M.length-1],B=L.width+D.horizontalPadding;if(D.width-D.rowWidth[R]>B&&_!=R){M.splice(-1,1),D.rows[R].push(L),D.rowWidth[_]=D.rowWidth[_]-B,D.rowWidth[R]=D.rowWidth[R]+B,D.width=D.rowWidth[instance.getLongestRowIndex(D)];for(var O=Number.MIN_VALUE,$=0;$<M.length;$++)M[$].height>O&&(O=M[$].height);_>0&&(O+=D.verticalPadding);var G=D.rowHeight[_]+D.rowHeight[R];D.rowHeight[_]=O,D.rowHeight[R]<L.height+D.verticalPadding&&(D.rowHeight[R]=L.height+D.verticalPadding);var F=D.rowHeight[_]+D.rowHeight[R];D.height+=F-G,this.shiftToLastRow(D)}},P.prototype.tilingPreLayout=function(){m.TILE&&(this.groupZeroDegreeMembers(),this.clearCompounds(),this.clearZeroDegreeMembers())},P.prototype.tilingPostLayout=function(){m.TILE&&(this.repopulateZeroDegreeMembers(),this.repopulateCompounds())},P.prototype.reduceTrees=function(){for(var D=[],_=!0,R;_;){var M=this.graphManager.getAllNodes(),L=[];_=!1;for(var B=0;B<M.length;B++)if(R=M[B],R.getEdges().length==1&&!R.getEdges()[0].isInterGraph&&R.getChild()==null){if(m.PURE_INCREMENTAL){var O=R.getEdges()[0].getOtherEnd(R),$=new T(R.getCenterX()-O.getCenterX(),R.getCenterY()-O.getCenterY());L.push([R,R.getEdges()[0],R.getOwner(),$])}else L.push([R,R.getEdges()[0],R.getOwner()]);_=!0}if(_==!0){for(var G=[],F=0;F<L.length;F++)L[F][0].getEdges().length==1&&(G.push(L[F]),L[F][0].getOwner().remove(L[F][0]));D.push(G),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()}}this.prunedNodesAll=D},P.prototype.growTree=function(D){for(var _=D.length,R=D[_-1],M,L=0;L<R.length;L++)M=R[L],this.findPlaceforPrunedNode(M),M[2].add(M[0]),M[2].add(M[1],M[1].source,M[1].target);D.splice(D.length-1,1),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()},P.prototype.findPlaceforPrunedNode=function(D){var _,R,M=D[0];if(M==D[1].source?R=D[1].target:R=D[1].source,m.PURE_INCREMENTAL)M.setCenter(R.getCenterX()+D[3].getWidth(),R.getCenterY()+D[3].getHeight());else{var L=R.startX,B=R.finishX,O=R.startY,$=R.finishY,G=0,F=0,V=0,H=0,j=[G,V,F,H];if(O>0)for(var U=L;U<=B;U++)j[0]+=this.grid[U][O-1].length+this.grid[U][O].length-1;if(B<this.grid.length-1)for(var U=O;U<=$;U++)j[1]+=this.grid[B+1][U].length+this.grid[B][U].length-1;if($<this.grid[0].length-1)for(var U=L;U<=B;U++)j[2]+=this.grid[U][$+1].length+this.grid[U][$].length-1;if(L>0)for(var U=O;U<=$;U++)j[3]+=this.grid[L-1][U].length+this.grid[L][U].length-1;for(var Q=C.MAX_VALUE,Y,ae,J=0;J<j.length;J++)j[J]<Q?(Q=j[J],Y=1,ae=J):j[J]==Q&&Y++;if(Y==3&&Q==0)j[0]==0&&j[1]==0&&j[2]==0?_=1:j[0]==0&&j[1]==0&&j[3]==0?_=0:j[0]==0&&j[2]==0&&j[3]==0?_=3:j[1]==0&&j[2]==0&&j[3]==0&&(_=2);else if(Y==2&&Q==0){var te=Math.floor(Math.random()*2);j[0]==0&&j[1]==0?te==0?_=0:_=1:j[0]==0&&j[2]==0?te==0?_=0:_=2:j[0]==0&&j[3]==0?te==0?_=0:_=3:j[1]==0&&j[2]==0?te==0?_=1:_=2:j[1]==0&&j[3]==0?te==0?_=1:_=3:te==0?_=2:_=3}else if(Y==4&&Q==0){var te=Math.floor(Math.random()*4);_=te}else _=ae;_==0?M.setCenter(R.getCenterX(),R.getCenterY()-R.getHeight()/2-y.DEFAULT_EDGE_LENGTH-M.getHeight()/2):_==1?M.setCenter(R.getCenterX()+R.getWidth()/2+y.DEFAULT_EDGE_LENGTH+M.getWidth()/2,R.getCenterY()):_==2?M.setCenter(R.getCenterX(),R.getCenterY()+R.getHeight()/2+y.DEFAULT_EDGE_LENGTH+M.getHeight()/2):M.setCenter(R.getCenterX()-R.getWidth()/2-y.DEFAULT_EDGE_LENGTH-M.getWidth()/2,R.getCenterY())}},a.exports=P}),991:((a,s,l)=>{var u=l(551).FDLayoutNode,h=l(551).IMath;function d(p,m,g,y){u.call(this,p,m,g,y)}o(d,"CoSENode"),d.prototype=Object.create(u.prototype);for(var f in u)d[f]=u[f];d.prototype.calculateDisplacement=function(){var p=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementX=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementX)),Math.abs(this.displacementY)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementY=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},d.prototype.propogateDisplacementToChildren=function(p,m){for(var g=this.getChild().getNodes(),y,v=0;v<g.length;v++)y=g[v],y.getChild()==null?(y.displacementX+=p,y.displacementY+=m):y.propogateDisplacementToChildren(p,m)},d.prototype.move=function(){var p=this.graphManager.getLayout();(this.child==null||this.child.getNodes().length==0)&&(this.moveBy(this.displacementX,this.displacementY),p.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY)),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},d.prototype.setPred1=function(p){this.pred1=p},d.prototype.getPred1=function(){return pred1},d.prototype.getPred2=function(){return pred2},d.prototype.setNext=function(p){this.next=p},d.prototype.getNext=function(){return next},d.prototype.setProcessed=function(p){this.processed=p},d.prototype.isProcessed=function(){return processed},a.exports=d}),902:((a,s,l)=>{function u(g){if(Array.isArray(g)){for(var y=0,v=Array(g.length);y<g.length;y++)v[y]=g[y];return v}else return Array.from(g)}o(u,"_toConsumableArray");var h=l(806),d=l(551).LinkedList,f=l(551).Matrix,p=l(551).SVD;function m(){}o(m,"ConstraintHandler"),m.handleConstraints=function(g){var y={};y.fixedNodeConstraint=g.constraints.fixedNodeConstraint,y.alignmentConstraint=g.constraints.alignmentConstraint,y.relativePlacementConstraint=g.constraints.relativePlacementConstraint;for(var v=new Map,x=new Map,b=[],T=[],w=g.getAllNodes(),C=0,k=0;k<w.length;k++){var E=w[k];E.getChild()==null&&(x.set(E.id,C++),b.push(E.getCenterX()),T.push(E.getCenterY()),v.set(E.id,E))}y.relativePlacementConstraint&&y.relativePlacementConstraint.forEach(function(ve){!ve.gap&&ve.gap!=0&&(ve.left?ve.gap=h.DEFAULT_EDGE_LENGTH+v.get(ve.left).getWidth()/2+v.get(ve.right).getWidth()/2:ve.gap=h.DEFAULT_EDGE_LENGTH+v.get(ve.top).getHeight()/2+v.get(ve.bottom).getHeight()/2)});var A=o(function(ie,fe){return{x:ie.x-fe.x,y:ie.y-fe.y}},"calculatePositionDiff"),N=o(function(ie){var fe=0,W=0;return ie.forEach(function(ce){fe+=b[x.get(ce)],W+=T[x.get(ce)]}),{x:fe/ie.size,y:W/ie.size}},"calculateAvgPosition"),P=o(function(ie,fe,W,ce,K){function Re(Ye,le){var st=new Set(Ye),me=!0,ot=!1,kt=void 0;try{for(var Gt=le[Symbol.iterator](),Tt;!(me=(Tt=Gt.next()).done);me=!0){var Et=Tt.value;st.add(Et)}}catch(yt){ot=!0,kt=yt}finally{try{!me&&Gt.return&&Gt.return()}finally{if(ot)throw kt}}return st}o(Re,"setUnion");var xe=new Map;ie.forEach(function(Ye,le){xe.set(le,0)}),ie.forEach(function(Ye,le){Ye.forEach(function(st){xe.set(st.id,xe.get(st.id)+1)})});var Oe=new Map,be=new Map,Be=new d;xe.forEach(function(Ye,le){Ye==0?(Be.push(le),W||(fe=="horizontal"?Oe.set(le,x.has(le)?b[x.get(le)]:ce.get(le)):Oe.set(le,x.has(le)?T[x.get(le)]:ce.get(le)))):Oe.set(le,Number.NEGATIVE_INFINITY),W&&be.set(le,new Set([le]))}),W&&K.forEach(function(Ye){var le=[];if(Ye.forEach(function(ot){W.has(ot)&&le.push(ot)}),le.length>0){var st=0;le.forEach(function(ot){fe=="horizontal"?(Oe.set(ot,x.has(ot)?b[x.get(ot)]:ce.get(ot)),st+=Oe.get(ot)):(Oe.set(ot,x.has(ot)?T[x.get(ot)]:ce.get(ot)),st+=Oe.get(ot))}),st=st/le.length,Ye.forEach(function(ot){W.has(ot)||Oe.set(ot,st)})}else{var me=0;Ye.forEach(function(ot){fe=="horizontal"?me+=x.has(ot)?b[x.get(ot)]:ce.get(ot):me+=x.has(ot)?T[x.get(ot)]:ce.get(ot)}),me=me/Ye.length,Ye.forEach(function(ot){Oe.set(ot,me)})}});for(var Ae=o(function(){var le=Be.shift(),st=ie.get(le);st.forEach(function(me){if(Oe.get(me.id)<Oe.get(le)+me.gap)if(W&&W.has(me.id)){var ot=void 0;if(fe=="horizontal"?ot=x.has(me.id)?b[x.get(me.id)]:ce.get(me.id):ot=x.has(me.id)?T[x.get(me.id)]:ce.get(me.id),Oe.set(me.id,ot),ot<Oe.get(le)+me.gap){var kt=Oe.get(le)+me.gap-ot;be.get(le).forEach(function(Gt){Oe.set(Gt,Oe.get(Gt)-kt)})}}else Oe.set(me.id,Oe.get(le)+me.gap);xe.set(me.id,xe.get(me.id)-1),xe.get(me.id)==0&&Be.push(me.id),W&&be.set(me.id,Re(be.get(le),be.get(me.id)))})},"_loop");Be.length!=0;)Ae();if(W){var Ve=new Set;ie.forEach(function(Ye,le){Ye.length==0&&Ve.add(le)});var Pe=[];be.forEach(function(Ye,le){if(Ve.has(le)){var st=!1,me=!0,ot=!1,kt=void 0;try{for(var Gt=Ye[Symbol.iterator](),Tt;!(me=(Tt=Gt.next()).done);me=!0){var Et=Tt.value;W.has(Et)&&(st=!0)}}catch(ht){ot=!0,kt=ht}finally{try{!me&&Gt.return&&Gt.return()}finally{if(ot)throw kt}}if(!st){var yt=!1,oe=void 0;Pe.forEach(function(ht,gt){ht.has([].concat(u(Ye))[0])&&(yt=!0,oe=gt)}),yt?Ye.forEach(function(ht){Pe[oe].add(ht)}):Pe.push(new Set(Ye))}}}),Pe.forEach(function(Ye,le){var st=Number.POSITIVE_INFINITY,me=Number.POSITIVE_INFINITY,ot=Number.NEGATIVE_INFINITY,kt=Number.NEGATIVE_INFINITY,Gt=!0,Tt=!1,Et=void 0;try{for(var yt=Ye[Symbol.iterator](),oe;!(Gt=(oe=yt.next()).done);Gt=!0){var ht=oe.value,gt=void 0;fe=="horizontal"?gt=x.has(ht)?b[x.get(ht)]:ce.get(ht):gt=x.has(ht)?T[x.get(ht)]:ce.get(ht);var et=Oe.get(ht);gt<st&&(st=gt),gt>ot&&(ot=gt),et<me&&(me=et),et>kt&&(kt=et)}}catch(lr){Tt=!0,Et=lr}finally{try{!Gt&&yt.return&&yt.return()}finally{if(Tt)throw Et}}var pe=(st+ot)/2-(me+kt)/2,Qe=!0,nt=!1,dt=void 0;try{for(var Ft=Ye[Symbol.iterator](),Rt;!(Qe=(Rt=Ft.next()).done);Qe=!0){var $t=Rt.value;Oe.set($t,Oe.get($t)+pe)}}catch(lr){nt=!0,dt=lr}finally{try{!Qe&&Ft.return&&Ft.return()}finally{if(nt)throw dt}}})}return Oe},"findAppropriatePositionForRelativePlacement"),I=o(function(ie){var fe=0,W=0,ce=0,K=0;if(ie.forEach(function(be){be.left?b[x.get(be.left)]-b[x.get(be.right)]>=0?fe++:W++:T[x.get(be.top)]-T[x.get(be.bottom)]>=0?ce++:K++}),fe>W&&ce>K)for(var Re=0;Re<x.size;Re++)b[Re]=-1*b[Re],T[Re]=-1*T[Re];else if(fe>W)for(var xe=0;xe<x.size;xe++)b[xe]=-1*b[xe];else if(ce>K)for(var Oe=0;Oe<x.size;Oe++)T[Oe]=-1*T[Oe]},"applyReflectionForRelativePlacement"),D=o(function(ie){var fe=[],W=new 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Map,V=[];if(y.fixedNodeConstraint&&y.fixedNodeConstraint.forEach(function(ve){$.add(ve.nodeId)}),y.relativePlacementConstraint&&(y.relativePlacementConstraint.forEach(function(ve){ve.left?(G.has(ve.left)?G.get(ve.left).push({id:ve.right,gap:ve.gap,direction:"horizontal"}):G.set(ve.left,[{id:ve.right,gap:ve.gap,direction:"horizontal"}]),G.has(ve.right)||G.set(ve.right,[])):(G.has(ve.top)?G.get(ve.top).push({id:ve.bottom,gap:ve.gap,direction:"vertical"}):G.set(ve.top,[{id:ve.bottom,gap:ve.gap,direction:"vertical"}]),G.has(ve.bottom)||G.set(ve.bottom,[]))}),F=_(G),V=D(F)),h.TRANSFORM_ON_CONSTRAINT_HANDLING){if(y.fixedNodeConstraint&&y.fixedNodeConstraint.length>1)y.fixedNodeConstraint.forEach(function(ve,ie){M[ie]=[ve.position.x,ve.position.y],L[ie]=[b[x.get(ve.nodeId)],T[x.get(ve.nodeId)]]}),B=!0;else if(y.alignmentConstraint)(function(){var ve=0;if(y.alignmentConstraint.vertical){for(var ie=y.alignmentConstraint.vertical,fe=o(function(Oe){var be=new 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0),C.alignmentConstraint.horizontal&&(!Array.isArray(C.alignmentConstraint.horizontal)||C.alignmentConstraint.horizontal.length==0)&&(C.alignmentConstraint.horizontal=void 0)),C.relativePlacementConstraint&&(!Array.isArray(C.relativePlacementConstraint)||C.relativePlacementConstraint.length==0)&&(C.relativePlacementConstraint=void 0);var R=C.fixedNodeConstraint||C.alignmentConstraint||C.relativePlacementConstraint;R&&(C.tile=!1,C.packComponents=!1);var M=void 0,L=!1;if(k.layoutUtilities&&C.packComponents&&(M=k.layoutUtilities("get"),M||(M=k.layoutUtilities()),L=!0),E.nodes().length>0)if(L){var $=f.getTopMostNodes(C.eles.nodes());if(D=f.connectComponents(k,C.eles,$),D.forEach(function(Ee){var we=Ee.boundingBox();_.push({x:we.x1+we.w/2,y:we.y1+we.h/2})}),C.randomize&&D.forEach(function(Ee){C.eles=Ee,A.push(m(C))}),C.quality=="default"||C.quality=="proof"){var G=k.collection();if(C.tile){var F=new Map,V=[],H=[],j=0,U={nodeIndexes:F,xCoords:V,yCoords:H},Q=[];if(D.forEach(function(Ee,we){Ee.edges().length==0&&(Ee.nodes().forEach(function(Me,$e){G.merge(Ee.nodes()[$e]),Me.isParent()||(U.nodeIndexes.set(Ee.nodes()[$e].id(),j++),U.xCoords.push(Ee.nodes()[0].position().x),U.yCoords.push(Ee.nodes()[0].position().y))}),Q.push(we))}),G.length>1){var Y=G.boundingBox();_.push({x:Y.x1+Y.w/2,y:Y.y1+Y.h/2}),D.push(G),A.push(U);for(var ae=Q.length-1;ae>=0;ae--)D.splice(Q[ae],1),A.splice(Q[ae],1),_.splice(Q[ae],1)}}D.forEach(function(Ee,we){C.eles=Ee,I.push(y(C,A[we])),f.relocateComponent(_[we],I[we],C)})}else D.forEach(function(Ee,we){f.relocateComponent(_[we],A[we],C)});var J=new Set;if(D.length>1){var te=[],re=E.filter(function(Ee){return Ee.css("display")=="none"});D.forEach(function(Ee,we){var Me=void 0;if(C.quality=="draft"&&(Me=A[we].nodeIndexes),Ee.nodes().not(re).length>0){var $e={};$e.edges=[],$e.nodes=[];var de=void 0;Ee.nodes().not(re).forEach(function(rt){if(C.quality=="draft")if(!rt.isParent())de=Me.get(rt.id()),$e.nodes.push({x:A[we].xCoords[de]-rt.boundingbox().w/2,y:A[we].yCoords[de]-rt.boundingbox().h/2,width:rt.boundingbox().w,height:rt.boundingbox().h});else{var ke=f.calcBoundingBox(rt,A[we].xCoords,A[we].yCoords,Me);$e.nodes.push({x:ke.topLeftX,y:ke.topLeftY,width:ke.width,height:ke.height})}else I[we][rt.id()]&&$e.nodes.push({x:I[we][rt.id()].getLeft(),y:I[we][rt.id()].getTop(),width:I[we][rt.id()].getWidth(),height:I[we][rt.id()].getHeight()})}),Ee.edges().forEach(function(rt){var ke=rt.source(),Fe=rt.target();if(ke.css("display")!="none"&&Fe.css("display")!="none")if(C.quality=="draft"){var He=Me.get(ke.id()),at=Me.get(Fe.id()),qe=[],Ue=[];if(ke.isParent()){var ye=f.calcBoundingBox(ke,A[we].xCoords,A[we].yCoords,Me);qe.push(ye.topLeftX+ye.width/2),qe.push(ye.topLeftY+ye.height/2)}else qe.push(A[we].xCoords[He]),qe.push(A[we].yCoords[He]);if(Fe.isParent()){var ve=f.calcBoundingBox(Fe,A[we].xCoords,A[we].yCoords,Me);Ue.push(ve.topLeftX+ve.width/2),Ue.push(ve.topLeftY+ve.height/2)}else Ue.push(A[we].xCoords[at]),Ue.push(A[we].yCoords[at]);$e.edges.push({startX:qe[0],startY:qe[1],endX:Ue[0],endY:Ue[1]})}else I[we][ke.id()]&&I[we][Fe.id()]&&$e.edges.push({startX:I[we][ke.id()].getCenterX(),startY:I[we][ke.id()].getCenterY(),endX:I[we][Fe.id()].getCenterX(),endY:I[we][Fe.id()].getCenterY()})}),$e.nodes.length>0&&(te.push($e),J.add(we))}});var ee=M.packComponents(te,C.randomize).shifts;if(C.quality=="draft")A.forEach(function(Ee,we){var Me=Ee.xCoords.map(function(de){return de+ee[we].dx}),$e=Ee.yCoords.map(function(de){return de+ee[we].dy});Ee.xCoords=Me,Ee.yCoords=$e});else{var Te=0;J.forEach(function(Ee){Object.keys(I[Ee]).forEach(function(we){var Me=I[Ee][we];Me.setCenter(Me.getCenterX()+ee[Te].dx,Me.getCenterY()+ee[Te].dy)}),Te++})}}}else{var B=C.eles.boundingBox();if(_.push({x:B.x1+B.w/2,y:B.y1+B.h/2}),C.randomize){var O=m(C);A.push(O)}C.quality=="default"||C.quality=="proof"?(I.push(y(C,A[0])),f.relocateComponent(_[0],I[0],C)):f.relocateComponent(_[0],A[0],C)}var ue=o(function(we,Me){if(C.quality=="default"||C.quality=="proof"){typeof we=="number"&&(we=Me);var $e=void 0,de=void 0,rt=we.data("id");return I.forEach(function(Fe){rt in Fe&&($e={x:Fe[rt].getRect().getCenterX(),y:Fe[rt].getRect().getCenterY()},de=Fe[rt])}),C.nodeDimensionsIncludeLabels&&(de.labelWidth&&(de.labelPosHorizontal=="left"?$e.x+=de.labelWidth/2:de.labelPosHorizontal=="right"&&($e.x-=de.labelWidth/2)),de.labelHeight&&(de.labelPosVertical=="top"?$e.y+=de.labelHeight/2:de.labelPosVertical=="bottom"&&($e.y-=de.labelHeight/2))),$e==null&&($e={x:we.position("x"),y:we.position("y")}),{x:$e.x,y:$e.y}}else{var ke=void 0;return A.forEach(function(Fe){var He=Fe.nodeIndexes.get(we.id());He!=null&&(ke={x:Fe.xCoords[He],y:Fe.yCoords[He]})}),ke==null&&(ke={x:we.position("x"),y:we.position("y")}),{x:ke.x,y:ke.y}}},"getPositions");if(C.quality=="default"||C.quality=="proof"||C.randomize){var De=f.calcParentsWithoutChildren(k,E),Ie=E.filter(function(Ee){return Ee.css("display")=="none"});C.eles=E.not(Ie),E.nodes().not(":parent").not(Ie).layoutPositions(w,C,ue),De.length>0&&De.forEach(function(Ee){Ee.position(ue(Ee))})}else console.log("If randomize option is set to false, then quality option must be 'default' or 'proof'.")},"run")}]),b})();a.exports=x}),657:((a,s,l)=>{var u=l(548),h=l(140).layoutBase.Matrix,d=l(140).layoutBase.SVD,f=o(function(m){var g=m.cy,y=m.eles,v=y.nodes(),x=y.nodes(":parent"),b=new Map,T=new Map,w=new Map,C=[],k=[],E=[],A=[],N=[],P=[],I=[],D=[],_=void 0,R=void 0,M=1e8,L=1e-9,B=m.piTol,O=m.samplingType,$=m.nodeSeparation,G=void 0,F=o(function(){for(var fe=0,W=0,ce=!1;W<G;){fe=Math.floor(Math.random()*R),ce=!1;for(var 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st=0;st<R;st++)xe[st]=K[st];k=h.multCons(Re,Math.sqrt(Math.abs(fe))),E=h.multCons(xe,Math.sqrt(Math.abs(W)))},"powerIteration");u.connectComponents(g,y,u.getTopMostNodes(v),b),x.forEach(function(ie){u.connectComponents(g,y,u.getTopMostNodes(ie.descendants().intersection(y)),b)});for(var Q=0,Y=0;Y<v.length;Y++)v[Y].isParent()||T.set(v[Y].id(),Q++);var ae=!0,J=!1,te=void 0;try{for(var re=b.keys()[Symbol.iterator](),ee;!(ae=(ee=re.next()).done);ae=!0){var Te=ee.value;T.set(Te,Q++)}}catch(ie){J=!0,te=ie}finally{try{!ae&&re.return&&re.return()}finally{if(J)throw te}}for(var ue=0;ue<T.size;ue++)C[ue]=[];x.forEach(function(ie){for(var fe=ie.children().intersection(y);fe.nodes(":childless").length==0;)fe=fe.nodes()[0].children().intersection(y);var W=0,ce=fe.nodes(":childless")[0].connectedEdges().length;fe.nodes(":childless").forEach(function(K,Re){K.connectedEdges().length<ce&&(ce=K.connectedEdges().length,W=Re)}),w.set(ie.id(),fe.nodes(":childless")[W].id())}),v.forEach(function(ie){var fe=void 0;ie.isParent()?fe=T.get(w.get(ie.id())):fe=T.get(ie.id()),ie.neighborhood().nodes().forEach(function(W){y.intersection(ie.edgesWith(W)).length>0&&(W.isParent()?C[fe].push(w.get(W.id())):C[fe].push(W.id()))})});var De=o(function(fe){var W=T.get(fe),ce=void 0;b.get(fe).forEach(function(K){g.getElementById(K).isParent()?ce=w.get(K):ce=K,C[W].push(ce),C[T.get(ce)].push(fe)})},"_loop"),Ie=!0,Ee=!1,we=void 0;try{for(var Me=b.keys()[Symbol.iterator](),$e;!(Ie=($e=Me.next()).done);Ie=!0){var de=$e.value;De(de)}}catch(ie){Ee=!0,we=ie}finally{try{!Ie&&Me.return&&Me.return()}finally{if(Ee)throw we}}R=T.size;var rt=void 0;if(R>2){G=R<m.sampleSize?R:m.sampleSize;for(var ke=0;ke<R;ke++)P[ke]=[];for(var Fe=0;Fe<G;Fe++)D[Fe]=[];return m.quality=="draft"||m.step=="all"?(H(O),j(),U(),rt={nodeIndexes:T,xCoords:k,yCoords:E}):(T.forEach(function(ie,fe){k.push(g.getElementById(fe).position("x")),E.push(g.getElementById(fe).position("y"))}),rt={nodeIndexes:T,xCoords:k,yCoords:E}),rt}else{var He=T.keys(),at=g.getElementById(He.next().value),qe=at.position(),Ue=at.outerWidth();if(k.push(qe.x),E.push(qe.y),R==2){var ye=g.getElementById(He.next().value),ve=ye.outerWidth();k.push(qe.x+Ue/2+ve/2+m.idealEdgeLength),E.push(qe.y)}return rt={nodeIndexes:T,xCoords:k,yCoords:E},rt}},"spectralLayout");a.exports={spectralLayout:f}}),579:((a,s,l)=>{var u=l(212),h=o(function(f){f&&f("layout","fcose",u)},"register");typeof cytoscape<"u"&&h(cytoscape),a.exports=h}),140:(a=>{a.exports=e})},r={};function i(a){var s=r[a];if(s!==void 0)return s.exports;var l=r[a]={exports:{}};return t[a](l,l.exports,i),l.exports}o(i,"__webpack_require__");var n=i(579);return n})()})});var Oy,wm,Hq=z(()=>{"use strict";uc();Oy=o(e=>`<g><rect width="80" height="80" style="fill: #087ebf; stroke-width: 0px;"/>${e}</g>`,"wrapIcon"),wm={prefix:"mermaid-architecture",height:80,width:80,icons:{database:{body:Oy('<path id="b" data-name="4" d="m20,57.86c0,3.94,8.95,7.14,20,7.14s20-3.2,20-7.14" style="fill: none; stroke: 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+.ishikawa .ishikawa-spine,
+.ishikawa .ishikawa-branch,
+.ishikawa .ishikawa-sub-branch {
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+  stroke-width: 2;
+  fill: none;
+}
+
+.ishikawa .ishikawa-sub-branch {
+  stroke-width: 1;
+}
+
+.ishikawa .ishikawa-arrow {
+  fill: ${e.lineColor};
+}
+
+.ishikawa .ishikawa-head {
+  fill: ${e.mainBkg};
+  stroke: ${e.lineColor};
+  stroke-width: 2;
+}
+
+.ishikawa .ishikawa-label-box {
+  fill: ${e.mainBkg};
+  stroke: ${e.lineColor};
+  stroke-width: 2;
+}
+
+.ishikawa text {
+  font-family: ${e.fontFamily};
+  font-size: ${e.fontSize};
+  fill: ${e.textColor};
+}
+
+.ishikawa .ishikawa-head-label {
+  font-weight: 600;
+  text-anchor: middle;
+  dominant-baseline: middle;
+  font-size: 14px;
+}
+
+.ishikawa .ishikawa-label {
+  text-anchor: end;
+}
+
+.ishikawa .ishikawa-label.cause {
+  text-anchor: middle;
+  dominant-baseline: middle;
+}
+
+.ishikawa .ishikawa-label.align {
+  text-anchor: end;
+  dominant-baseline: middle;
+}
+
+.ishikawa .ishikawa-label.up {
+  dominant-baseline: baseline;
+}
+
+.ishikawa .ishikawa-label.down {
+  dominant-baseline: hanging;
+}
+`,"getStyles"),uLe=XEt});var dLe={};xr(dLe,{diagram:()=>KEt});var KEt,fLe=z(()=>{"use strict";JRe();eLe();cLe();hLe();KEt={parser:QRe,get db(){return new __},renderer:lLe,styles:uLe}});var rU,gLe,yLe=z(()=>{"use strict";rU=(function(){var e=o(function(b,T,w,C){for(w=w||{},C=b.length;C--;w[b[C]]=T);return 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C=new Error(T);throw C.hash=w,C}},"parseError"),parse:o(function(T){var w=this,C=[0],k=[],E=[null],A=[],N=this.table,P="",I=0,D=0,_=0,R=2,M=1,L=A.slice.call(arguments,1),B=Object.create(this.lexer),O={yy:{}};for(var $ in this.yy)Object.prototype.hasOwnProperty.call(this.yy,$)&&(O.yy[$]=this.yy[$]);B.setInput(T,O.yy),O.yy.lexer=B,O.yy.parser=this,typeof B.yylloc>"u"&&(B.yylloc={});var G=B.yylloc;A.push(G);var F=B.options&&B.options.ranges;typeof O.yy.parseError=="function"?this.parseError=O.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function V(Ie){C.length=C.length-2*Ie,E.length=E.length-Ie,A.length=A.length-Ie}o(V,"popStack");function H(){var Ie;return Ie=k.pop()||B.lex()||M,typeof Ie!="number"&&(Ie instanceof Array&&(k=Ie,Ie=k.pop()),Ie=w.symbols_[Ie]||Ie),Ie}o(H,"lex");for(var j,U,Q,Y,ae,J,te={},re,ee,Te,ue;;){if(Q=C[C.length-1],this.defaultActions[Q]?Y=this.defaultActions[Q]:((j===null||typeof j>"u")&&(j=H()),Y=N[Q]&&N[Q][j]),typeof 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+`+B.showPosition()+`
+Expecting `+ue.join(", ")+", got '"+(this.terminals_[j]||j)+"'":De="Parse error on line "+(I+1)+": Unexpected "+(j==M?"end of input":"'"+(this.terminals_[j]||j)+"'"),this.parseError(De,{text:B.match,token:this.terminals_[j]||j,line:B.yylineno,loc:G,expected:ue})}if(Y[0]instanceof Array&&Y.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Q+", token: "+j);switch(Y[0]){case 1:C.push(j),E.push(B.yytext),A.push(B.yylloc),C.push(Y[1]),j=null,U?(j=U,U=null):(D=B.yyleng,P=B.yytext,I=B.yylineno,G=B.yylloc,_>0&&_--);break;case 2:if(ee=this.productions_[Y[1]][1],te.$=E[E.length-ee],te._$={first_line:A[A.length-(ee||1)].first_line,last_line:A[A.length-1].last_line,first_column:A[A.length-(ee||1)].first_column,last_column:A[A.length-1].last_column},F&&(te._$.range=[A[A.length-(ee||1)].range[0],A[A.length-1].range[1]]),J=this.performAction.apply(te,[P,D,I,O.yy,Y[1],E,A].concat(L)),typeof J<"u")return J;ee&&(C=C.slice(0,-1*ee*2),E=E.slice(0,-1*ee),A=A.slice(0,-1*ee)),C.push(this.productions_[Y[1]][0]),E.push(te.$),A.push(te._$),Te=N[C[C.length-2]][C[C.length-1]],C.push(Te);break;case 3:return!0}}return!0},"parse")},v=(function(){var b={EOF:1,parseError:o(function(w,C){if(this.yy.parser)this.yy.parser.parseError(w,C);else throw new Error(w)},"parseError"),setInput:o(function(T,w){return this.yy=w||this.yy||{},this._input=T,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var T=this._input[0];this.yytext+=T,this.yyleng++,this.offset++,this.match+=T,this.matched+=T;var w=T.match(/(?:\r\n?|\n).*/g);return w?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),T},"input"),unput:o(function(T){var w=T.length,C=T.split(/(?:\r\n?|\n)/g);this._input=T+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-w),this.offset-=w;var k=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),C.length-1&&(this.yylineno-=C.length-1);var E=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:C?(C.length===k.length?this.yylloc.first_column:0)+k[k.length-C.length].length-C[0].length:this.yylloc.first_column-w},this.options.ranges&&(this.yylloc.range=[E[0],E[0]+this.yyleng-w]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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+    font-size: 32px;
+    fill: ${e.vennTitleTextColor};
+    font-family: ${e.fontFamily};
+  }
+
+  .venn-circle text {
+    font-size: 48px;
+    font-family: ${e.fontFamily};
+  }
+
+  .venn-intersection text {
+    font-size: 48px;
+    fill: ${e.vennSetTextColor};
+    font-family: ${e.fontFamily};
+  }
+
+  .venn-text-node {
+    font-family: ${e.fontFamily};
+    color: ${e.vennSetTextColor};
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V=O.append("g").attr("class","wardley-annotation");V.append("circle").attr("cx",F.x).attr("cy",F.y).attr("r",10).attr("fill","white").attr("stroke",a.axisColor).attr("stroke-width",1.5),V.append("text").attr("x",F.x).attr("y",F.y).attr("text-anchor","middle").attr("dominant-baseline","central").attr("font-size",10).attr("fill",a.axisTextColor).attr("font-weight","bold").text($.number)})}),u.annotationsBox){let $=x(u.annotationsBox.x),G=b(u.annotationsBox.y),F=10,V=16,H=11,j=O.append("g").attr("class","wardley-annotations-box"),U=[...u.annotations].filter(Y=>Y.text).sort((Y,ae)=>Y.number-ae.number),Q=[];if(U.forEach((Y,ae)=>{let J=j.append("text").attr("x",$+F).attr("y",G+F+(ae+1)*V).attr("font-size",H).attr("fill",a.axisTextColor).attr("text-anchor","start").attr("dominant-baseline","middle").text(`${Y.number}. ${Y.text}`);Q.push(J)}),Q.length>0){let Y=0,ae=0;Q.forEach(De=>{let Ie=De.node(),Ee=Ie.getComputedTextLength();Y=Math.max(Y,Ee);let 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+        M ${G} ${F-H/2}
+        L ${G+V-j} ${F-H/2}
+        L ${G+V-j} ${F-H/2-8}
+        L ${G+V} ${F}
+        L ${G+V-j} ${F+H/2+8}
+        L ${G+V-j} ${F+H/2}
+        L ${G} ${F+H/2}
+        Z
+      `;O.append("path").attr("d",U).attr("fill","white").attr("stroke",a.componentStroke).attr("stroke-width",1),O.append("text").attr("x",G+V/2).attr("y",F+H/2+15).attr("text-anchor","middle").attr("font-size",10).attr("fill",a.axisTextColor).attr("font-weight","bold").text($.name)})}if(u.deaccelerators.length>0){let O=m.append("g").attr("class","wardley-deaccelerators");u.deaccelerators.forEach($=>{let G=x($.x),F=b($.y),V=60,H=30,j=20,U=`
+        M ${G+V} ${F-H/2}
+        L ${G+j} ${F-H/2}
+        L ${G+j} ${F-H/2-8}
+        L ${G} ${F}
+        L ${G+j} ${F+H/2+8}
+        L ${G+j} ${F+H/2}
+        L ${G+V} ${F+H/2}
+        Z
+      `;O.append("path").attr("d",U).attr("fill","white").attr("stroke",a.componentStroke).attr("stroke-width",1),O.append("text").attr("x",G+V/2).attr("y",F+H/2+15).attr("text-anchor","middle").attr("font-size",10).attr("fill",a.axisTextColor).attr("font-weight","bold").text($.name)})}},"draw"),cMe={draw:gAt}});var hMe,dMe=z(()=>{"use strict";Xt();$f();br();hMe=o(({wardley:e}={})=>{let t=Ao(),r=Nt(),i=Gr(t,r.themeVariables),n=Gr(i.wardley,e);return`
+  .wardley-background {
+    fill: ${n.backgroundColor};
+  }
+  .wardley-axes line, .wardley-axes path {
+    stroke: ${n.axisColor};
+  }
+  .wardley-axis-label {
+    fill: ${n.axisTextColor};
+  }
+  .wardley-stage-label {
+    fill: ${n.axisTextColor};
+  }
+  .wardley-grid line {
+    stroke: ${n.gridColor};
+  }
+  .wardley-node circle {
+    fill: ${n.componentFill};
+    stroke: ${n.componentStroke};
+  }
+  .wardley-node-label {
+    fill: ${n.componentLabelColor};
+  }
+  .wardley-link {
+    stroke: ${n.linkStroke};
+  }
+  .wardley-link--dashed {
+    stroke-dasharray: 4 4;
+  }
+  .wardley-link-label {
+    fill: ${n.axisTextColor};
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+  .wardley-trend line {
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+/*! Bundled license information:
+
+lodash-es/lodash.js:
+  (**
+   * @license
+   * Lodash (Custom Build) <https://lodash.com/>
+   * Build: `lodash modularize exports="es" -o ./`
+   * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
+   * Released under MIT license <https://lodash.com/license>
+   * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
+   * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+   *)
+*/
+/*! Check if previously processed */
+/*!
+ * Wait for document loaded before starting the execution
+ */
+/*! Bundled license information:
+
+dompurify/dist/purify.es.mjs:
+  (*! @license DOMPurify 3.4.0 | (c) Cure53 and other contributors | Released under the Apache license 2.0 and Mozilla Public License 2.0 | github.com/cure53/DOMPurify/blob/3.4.0/LICENSE *)
+
+js-yaml/dist/js-yaml.mjs:
+  (*! js-yaml 4.1.1 https://github.com/nodeca/js-yaml @license MIT *)
+
+lodash-es/lodash.js:
+  (**
+   * @license
+   * Lodash (Custom Build) <https://lodash.com/>
+   * Build: `lodash modularize exports="es" --repo lodash/lodash#4.18.1 -o ./`
+   * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
+   * Released under MIT license <https://lodash.com/license>
+   * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
+   * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+   *)
+
+cytoscape/dist/cytoscape.esm.mjs:
+  (*!
+  Embeddable Minimum Strictly-Compliant Promises/A+ 1.1.1 Thenable
+  Copyright (c) 2013-2014 Ralf S. Engelschall (http://engelschall.com)
+  Licensed under The MIT License (http://opensource.org/licenses/MIT)
+  *)
+  (*!
+  Event object based on jQuery events, MIT license
+  
+  https://jquery.org/license/
+  https://tldrlegal.com/license/mit-license
+  https://github.com/jquery/jquery/blob/master/src/event.js
+  *)
+  (*! Bezier curve function generator. Copyright Gaetan Renaudeau. MIT License: http://en.wikipedia.org/wiki/MIT_License *)
+  (*! Runge-Kutta spring physics function generator. Adapted from Framer.js, copyright Koen Bok. MIT License: http://en.wikipedia.org/wiki/MIT_License *)
+*/
+globalThis["mermaid"] = globalThis.__esbuild_esm_mermaid_nm["mermaid"].default;
+
+</script>
+<script>
+
+/**
+ * Copyright (c) 2016-2024, The Cytoscape Consortium.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy of
+ * this software and associated documentation files (the “Software”), to deal in
+ * the Software without restriction, including without limitation the rights to
+ * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+ * of the Software, and to permit persons to whom the Software is furnished to do
+ * so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
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f.zoomingEnabled=n.zoomingEnabled,f.userZoomingEnabled=n.userZoomingEnabled,f.zoom=n.zoom,f.minZoom=n.minZoom,f.maxZoom=n.maxZoom,f.panningEnabled=n.panningEnabled,f.userPanningEnabled=n.userPanningEnabled,f.pan=qe(n.pan),f.boxSelectionEnabled=n.boxSelectionEnabled,f.renderer=qe(g.renderer),f.hideEdgesOnViewport=g.hideEdgesOnViewport,f.textureOnViewport=g.textureOnViewport,f.wheelSensitivity=g.wheelSensitivity,f.motionBlur=g.motionBlur,f.multiClickDebounceTime=g.multiClickDebounceTime,f}}),Rs.$id=Rs.getElementById,[rs,fs,ys,ms,bs,xs,Es,ks,Is,As,Ls].forEach((function(e){L(Rs,e)}));var Vs={fit:!0,directed:!1,padding:30,circle:!1,grid:!1,spacingFactor:1.75,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,roots:void 0,depthSort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:function(e,t){return!0},ready:void 0,stop:void 0,transform:function(e,t){return t}},Fs={maximal:!1,acyclic:!1},js=function(e){return 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t=js(e),r=t.depth,i=t.index,a=b[r].length,o=Math.max(u.w/((n.grid?Z:a)+1),I),s=Math.max(u.h/(b.length+1),I),l=Math.min(u.w/2/b.length,u.h/2/b.length);if(l=Math.max(l,I),n.circle){var c=l*r+l-(b.length>0&&b[0].length<=3?l/2:0),d=2*Math.PI/b[r].length*i;return 0===r&&1===b[0].length&&(c=1),{x:G+c*Math.cos(d),y:U+c*Math.sin(d)}}return{x:G+(i+1-(a+1)/2)*o,y:(r+1)*s}})),this};var Xs={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,radius:void 0,startAngle:1.5*Math.PI,sweep:void 0,clockwise:!0,sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:function(e,t){return!0},ready:void 0,stop:void 0,transform:function(e,t){return t}};function Ws(e){this.options=L({},Xs,e)}Ws.prototype.run=function(){var e=this.options,t=e,n=e.cy,r=t.eles,i=void 0!==t.counterclockwise?!t.counterclockwise:t.clockwise,a=r.nodes().not(":parent");t.sort&&(a=a.sort(t.sort));for(var o,s=_t(t.boundingBox?t.boundingBox:{x1:0,y1:0,w:n.width(),h:n.height()}),l=s.x1+s.w/2,u=s.y1+s.h/2,c=(void 0===t.sweep?2*Math.PI-2*Math.PI/a.length:t.sweep)/Math.max(1,a.length-1),d=0,h=0;h<a.length;h++){var p=a[h].layoutDimensions(t),f=p.w,g=p.h;d=Math.max(d,f,g)}if(o=x(t.radius)?t.radius:a.length<=1?0:Math.min(s.h,s.w)/2-d,a.length>1&&t.avoidOverlap){d*=1.75;var v=Math.cos(c)-Math.cos(0),y=Math.sin(c)-Math.sin(0),m=Math.sqrt(d*d/(v*v+y*y));o=Math.max(m,o)}return r.nodes().layoutPositions(this,t,(function(e,n){var r=t.startAngle+n*c*(i?1:-1),a=o*Math.cos(r),s=o*Math.sin(r);return{x:l+a,y:u+s}})),this};var Hs,Ks={fit:!0,padding:30,startAngle:1.5*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:function(e){return e.degree()},levelWidth:function(e){return e.maxDegree()/4},animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:function(e,t){return!0},ready:void 0,stop:void 0,transform:function(e,t){return t}};function Gs(e){this.options=L({},Ks,e)}Gs.prototype.run=function(){for(var e=this.options,t=e,n=void 0!==t.counterclockwise?!t.counterclockwise:t.clockwise,r=e.cy,i=t.eles,a=i.nodes().not(":parent"),o=_t(t.boundingBox?t.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),s=o.x1+o.w/2,l=o.y1+o.h/2,u=[],c=0,d=0;d<a.length;d++){var h,p=a[d];h=t.concentric(p),u.push({value:h,node:p}),p._private.scratch.concentric=h}a.updateStyle();for(var f=0;f<a.length;f++){var g=a[f].layoutDimensions(t);c=Math.max(c,g.w,g.h)}u.sort((function(e,t){return t.value-e.value}));for(var v=t.levelWidth(a),y=[[]],m=y[0],b=0;b<u.length;b++){var x=u[b];if(m.length>0)Math.abs(m[0].value-x.value)>=v&&(m=[],y.push(m));m.push(x)}var w=c+t.minNodeSpacing;if(!t.avoidOverlap){var E=y.length>0&&y[0].length>1,k=(Math.min(o.w,o.h)/2-w)/(y.length+E?1:0);w=Math.min(w,k)}for(var C=0,S=0;S<y.length;S++){var P=y[S],D=void 0===t.sweep?2*Math.PI-2*Math.PI/P.length:t.sweep,T=P.dTheta=D/Math.max(1,P.length-1);if(P.length>1&&t.avoidOverlap){var _=Math.cos(T)-Math.cos(0),M=Math.sin(T)-Math.sin(0),B=Math.sqrt(w*w/(_*_+M*M));C=Math.max(B,C)}P.r=C,C+=w}if(t.equidistant){for(var N=0,z=0,I=0;I<y.length;I++){var A=y[I].r-z;N=Math.max(N,A)}z=0;for(var L=0;L<y.length;L++){var O=y[L];0===L&&(z=O.r),O.r=z,z+=N}}for(var R={},V=0;V<y.length;V++)for(var F=y[V],j=F.dTheta,q=F.r,Y=0;Y<F.length;Y++){var X=F[Y],W=t.startAngle+(n?1:-1)*j*Y,H={x:s+q*Math.cos(W),y:l+q*Math.sin(W)};R[X.node.id()]=H}return i.nodes().layoutPositions(this,t,(function(e){var t=e.id();return R[t]})),this};var Us={ready:function(){},stop:function(){},animate:!0,animationEasing:void 0,animationDuration:void 0,animateFilter:function(e,t){return!0},animationThreshold:250,refresh:20,fit:!0,padding:30,boundingBox:void 0,nodeDimensionsIncludeLabels:!1,randomize:!1,componentSpacing:40,nodeRepulsion:function(e){return 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i=we(),a=function(){nl(r,t,e),!0===e.fit&&t.fit(e.padding)},o=function(t){return!(n.stopped||t>=e.numIter)&&(rl(r,e),r.temperature=r.temperature*e.coolingFactor,!(r.temperature<e.minTemp))},s=function(){if(!0===e.animate||!1===e.animate)a(),n.one("layoutstop",e.stop),n.emit({type:"layoutstop",layout:n});else{var t=e.eles.nodes(),i=tl(r,e,t);t.layoutPositions(n,e,i)}},l=0,u=!0;if(!0===e.animate){!function t(){for(var n=0;u&&n<e.refresh;)u=o(l),l++,n++;u?(we()-i>=e.animationThreshold&&a(),xe(t)):(gl(r,e),s())}()}else{for(;u;)u=o(l),l++;gl(r,e),s()}return this},Zs.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this},Zs.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};var $s=function(e,t,n){for(var r=n.eles.edges(),i=n.eles.nodes(),a=_t(n.boundingBox?n.boundingBox:{x1:0,y1:0,w:e.width(),h:e.height()}),o={isCompound:e.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:i.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:r.size(),temperature:n.initialTemp,clientWidth:a.w,clientHeight:a.h,boundingBox:a},s=n.eles.components(),l={},u=0;u<s.length;u++)for(var c=s[u],d=0;d<c.length;d++){l[c[d].id()]=u}for(u=0;u<o.nodeSize;u++){var h=(m=i[u]).layoutDimensions(n);(I={}).isLocked=m.locked(),I.id=m.data("id"),I.parentId=m.data("parent"),I.cmptId=l[m.id()],I.children=[],I.positionX=m.position("x"),I.positionY=m.position("y"),I.offsetX=0,I.offsetY=0,I.height=h.w,I.width=h.h,I.maxX=I.positionX+I.width/2,I.minX=I.positionX-I.width/2,I.maxY=I.positionY+I.height/2,I.minY=I.positionY-I.height/2,I.padLeft=parseFloat(m.style("padding")),I.padRight=parseFloat(m.style("padding")),I.padTop=parseFloat(m.style("padding")),I.padBottom=parseFloat(m.style("padding")),I.nodeRepulsion=y(n.nodeRepulsion)?n.nodeRepulsion(m):n.nodeRepulsion,o.layoutNodes.push(I),o.idToIndex[I.id]=u}var p=[],f=0,g=-1,v=[];for(u=0;u<o.nodeSize;u++){var m,b=(m=o.layoutNodes[u]).parentId;null!=b?o.layoutNodes[o.idToIndex[b]].children.push(m.id):(p[++g]=m.id,v.push(m.id))}for(o.graphSet.push(v);f<=g;){var x=p[f++],w=o.idToIndex[x],E=o.layoutNodes[w].children;if(E.length>0){o.graphSet.push(E);for(u=0;u<E.length;u++)p[++g]=E[u]}}for(u=0;u<o.graphSet.length;u++){var k=o.graphSet[u];for(d=0;d<k.length;d++){var C=o.idToIndex[k[d]];o.indexToGraph[C]=u}}for(u=0;u<o.edgeSize;u++){var S=r[u],P={};P.id=S.data("id"),P.sourceId=S.data("source"),P.targetId=S.data("target");var D=y(n.idealEdgeLength)?n.idealEdgeLength(S):n.idealEdgeLength,T=y(n.edgeElasticity)?n.edgeElasticity(S):n.edgeElasticity,_=o.idToIndex[P.sourceId],M=o.idToIndex[P.targetId];if(o.indexToGraph[_]!=o.indexToGraph[M]){for(var B=Qs(P.sourceId,P.targetId,o),N=o.graphSet[B],z=0,I=o.layoutNodes[_];-1===N.indexOf(I.id);)I=o.layoutNodes[o.idToIndex[I.parentId]],z++;for(I=o.layoutNodes[M];-1===N.indexOf(I.id);)I=o.layoutNodes[o.idToIndex[I.parentId]],z++;D*=z*n.nestingFactor}P.idealLength=D,P.elasticity=T,o.layoutEdges.push(P)}return o},Qs=function(e,t,n){var r=Js(e,t,0,n);return 2>r.count?0:r.graph},Js=function e(t,n,r,i){var a=i.graphSet[r];if(-1<a.indexOf(t)&&-1<a.indexOf(n))return{count:2,graph:r};for(var o=0,s=0;s<a.length;s++){var l=a[s],u=i.idToIndex[l],c=i.layoutNodes[u].children;if(0!==c.length){var d=e(t,n,i.indexToGraph[i.idToIndex[c[0]]],i);if(0!==d.count){if(1!==d.count)return d;if(2===++o)break}}}return{count:o,graph:r}},el=function(e,t){for(var n=e.clientWidth,r=e.clientHeight,i=0;i<e.nodeSize;i++){var a=e.layoutNodes[i];0!==a.children.length||a.isLocked||(a.positionX=Math.random()*n,a.positionY=Math.random()*r)}},tl=function(e,t,n){var r=e.boundingBox,i={x1:1/0,x2:-1/0,y1:1/0,y2:-1/0};return t.boundingBox&&(n.forEach((function(t){var n=e.layoutNodes[e.idToIndex[t.data("id")]];i.x1=Math.min(i.x1,n.positionX),i.x2=Math.max(i.x2,n.positionX),i.y1=Math.min(i.y1,n.positionY),i.y2=Math.max(i.y2,n.positionY)})),i.w=i.x2-i.x1,i.h=i.y2-i.y1),function(n,a){var o=e.layoutNodes[e.idToIndex[n.data("id")]];if(t.boundingBox){var s=(o.positionX-i.x1)/i.w,l=(o.positionY-i.y1)/i.h;return{x:r.x1+s*r.w,y:r.y1+l*r.h}}return{x:o.positionX,y:o.positionY}}},nl=function(e,t,n){var r=n.layout,i=n.eles.nodes(),a=tl(e,n,i);i.positions(a),!0!==e.ready&&(e.ready=!0,r.one("layoutready",n.ready),r.emit({type:"layoutready",layout:this}))},rl=function(e,t,n){il(e,t),ul(e),cl(e,t),dl(e),hl(e)},il=function(e,t){for(var n=0;n<e.graphSet.length;n++)for(var r=e.graphSet[n],i=r.length,a=0;a<i;a++)for(var o=e.layoutNodes[e.idToIndex[r[a]]],s=a+1;s<i;s++){var l=e.layoutNodes[e.idToIndex[r[s]]];ol(o,l,e,t)}},al=function(e){return-e+2*e*Math.random()},ol=function(e,t,n,r){if(e.cmptId===t.cmptId||n.isCompound){var i=t.positionX-e.positionX,a=t.positionY-e.positionY;0===i&&0===a&&(i=al(1),a=al(1));var o=sl(e,t,i,a);if(o>0)var s=(u=r.nodeOverlap*o)*i/(g=Math.sqrt(i*i+a*a)),l=u*a/g;else{var u,c=ll(e,i,a),d=ll(t,-1*i,-1*a),h=d.x-c.x,p=d.y-c.y,f=h*h+p*p,g=Math.sqrt(f);s=(u=(e.nodeRepulsion+t.nodeRepulsion)/f)*h/g,l=u*p/g}e.isLocked||(e.offsetX-=s,e.offsetY-=l),t.isLocked||(t.offsetX+=s,t.offsetY+=l)}},sl=function(e,t,n,r){if(n>0)var i=e.maxX-t.minX;else i=t.maxX-e.minX;if(r>0)var a=e.maxY-t.minY;else a=t.maxY-e.minY;return i>=0&&a>=0?Math.sqrt(i*i+a*a):0},ll=function(e,t,n){var r=e.positionX,i=e.positionY,a=e.height||1,o=e.width||1,s=n/t,l=a/o,u={};return 0===t&&0<n||0===t&&0>n?(u.x=r,u.y=i+a/2,u):0<t&&-1*l<=s&&s<=l?(u.x=r+o/2,u.y=i+o*n/2/t,u):0>t&&-1*l<=s&&s<=l?(u.x=r-o/2,u.y=i-o*n/2/t,u):0<n&&(s<=-1*l||s>=l)?(u.x=r+a*t/2/n,u.y=i+a/2,u):0>n&&(s<=-1*l||s>=l)?(u.x=r-a*t/2/n,u.y=i-a/2,u):u},ul=function(e,t){for(var n=0;n<e.edgeSize;n++){var r=e.layoutEdges[n],i=e.idToIndex[r.sourceId],a=e.layoutNodes[i],o=e.idToIndex[r.targetId],s=e.layoutNodes[o],l=s.positionX-a.positionX,u=s.positionY-a.positionY;if(0!==l||0!==u){var c=ll(a,l,u),d=ll(s,-1*l,-1*u),h=d.x-c.x,p=d.y-c.y,f=Math.sqrt(h*h+p*p),g=Math.pow(r.idealLength-f,2)/r.elasticity;if(0!==f)var v=g*h/f,y=g*p/f;else v=0,y=0;a.isLocked||(a.offsetX+=v,a.offsetY+=y),s.isLocked||(s.offsetX-=v,s.offsetY-=y)}}},cl=function(e,t){if(0!==t.gravity)for(var n=0;n<e.graphSet.length;n++){var r=e.graphSet[n],i=r.length;if(0===n)var a=e.clientHeight/2,o=e.clientWidth/2;else{var s=e.layoutNodes[e.idToIndex[r[0]]],l=e.layoutNodes[e.idToIndex[s.parentId]];a=l.positionX,o=l.positionY}for(var u=0;u<i;u++){var c=e.layoutNodes[e.idToIndex[r[u]]];if(!c.isLocked){var d=a-c.positionX,h=o-c.positionY,p=Math.sqrt(d*d+h*h);if(p>1){var f=t.gravity*d/p,g=t.gravity*h/p;c.offsetX+=f,c.offsetY+=g}}}}},dl=function(e,t){var n=[],r=0,i=-1;for(n.push.apply(n,e.graphSet[0]),i+=e.graphSet[0].length;r<=i;){var a=n[r++],o=e.idToIndex[a],s=e.layoutNodes[o],l=s.children;if(0<l.length&&!s.isLocked){for(var u=s.offsetX,c=s.offsetY,d=0;d<l.length;d++){var 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s}},Ml={calculateArrowAngles:function(e){var t,n,r,i,a,o,s=e._private.rscratch,l="haystack"===s.edgeType,u="bezier"===s.edgeType,c="multibezier"===s.edgeType,d="segments"===s.edgeType,h="compound"===s.edgeType,p="self"===s.edgeType;if(l?(r=s.haystackPts[0],i=s.haystackPts[1],a=s.haystackPts[2],o=s.haystackPts[3]):(r=s.arrowStartX,i=s.arrowStartY,a=s.arrowEndX,o=s.arrowEndY),g=s.midX,v=s.midY,d)t=r-s.segpts[0],n=i-s.segpts[1];else if(c||h||p||u){var f=s.allpts;t=r-Pt(f[0],f[2],f[4],.1),n=i-Pt(f[1],f[3],f[5],.1)}else t=r-g,n=i-v;s.srcArrowAngle=xt(t,n);var g=s.midX,v=s.midY;if(l&&(g=(r+a)/2,v=(i+o)/2),t=a-r,n=o-i,d)if((f=s.allpts).length/2%2==0){var y=(S=f.length/2)-2;t=f[S]-f[y],n=f[S+1]-f[y+1]}else if(s.isRound)t=s.midVector[1],n=-s.midVector[0];else{y=(S=f.length/2-1)-2;t=f[S]-f[y],n=f[S+1]-f[y+1]}else if(c||h||p){var m,b,x,w,f=s.allpts;if(s.ctrlpts.length/2%2==0){var E=(k=(C=f.length/2-1)+2)+2;m=Pt(f[C],f[k],f[E],0),b=Pt(f[C+1],f[k+1],f[E+1],0),x=Pt(f[C],f[k],f[E],1e-4),w=Pt(f[C+1],f[k+1],f[E+1],1e-4)}else{var k,C;E=(k=f.length/2-1)+2;m=Pt(f[C=k-2],f[k],f[E],.4999),b=Pt(f[C+1],f[k+1],f[E+1],.4999),x=Pt(f[C],f[k],f[E],.5),w=Pt(f[C+1],f[k+1],f[E+1],.5)}t=x-m,n=w-b}if(s.midtgtArrowAngle=xt(t,n),s.midDispX=t,s.midDispY=n,t*=-1,n*=-1,d)if((f=s.allpts).length/2%2==0);else if(!s.isRound){var S,P=(S=f.length/2-1)+2;t=-(f[P]-f[S]),n=-(f[P+1]-f[S+1])}if(s.midsrcArrowAngle=xt(t,n),d)t=a-s.segpts[s.segpts.length-2],n=o-s.segpts[s.segpts.length-1];else if(c||h||p||u){var D=(f=s.allpts).length;t=a-Pt(f[D-6],f[D-4],f[D-2],.9),n=o-Pt(f[D-5],f[D-3],f[D-1],.9)}else t=a-g,n=o-v;s.tgtArrowAngle=xt(t,n)}};Ml.getArrowWidth=Ml.getArrowHeight=function(e,t){var n=this.arrowWidthCache=this.arrowWidthCache||{},r=n[e+", "+t];return r||(r=Math.max(Math.pow(13.37*e,.9),29)*t,n[e+", "+t]=r,r)};var Bl,Nl,zl,Il,Al,Ll,Ol,Rl,Vl,Fl,jl,ql,Yl,Xl,Wl,Hl,Kl,Gl={},Ul={},Zl=function(e,t,n){n.x=t.x-e.x,n.y=t.y-e.y,n.len=Math.sqrt(n.x*n.x+n.y*n.y),n.nx=n.x/n.len,n.ny=n.y/n.len,n.ang=Math.atan2(n.ny,n.nx)},$l=function(e,t,n,r,i){var a,o;if(e!==Kl?Zl(t,e,Gl):((o=Gl).x=-1*(a=Ul).x,o.y=-1*a.y,o.nx=-1*a.nx,o.ny=-1*a.ny,o.ang=a.ang>0?-(Math.PI-a.ang):Math.PI+a.ang),Zl(t,n,Ul),zl=Gl.nx*Ul.ny-Gl.ny*Ul.nx,Il=Gl.nx*Ul.nx-Gl.ny*-Ul.ny,Ol=Math.asin(Math.max(-1,Math.min(1,zl))),Math.abs(Ol)<1e-6)return Bl=t.x,Nl=t.y,void(Vl=jl=0);Al=1,Ll=!1,Il<0?Ol<0?Ol=Math.PI+Ol:(Ol=Math.PI-Ol,Al=-1,Ll=!0):Ol>0&&(Al=-1,Ll=!0),jl=void 0!==t.radius?t.radius:r,Rl=Ol/2,ql=Math.min(Gl.len/2,Ul.len/2),i?(Fl=Math.abs(Math.cos(Rl)*jl/Math.sin(Rl)))>ql?(Fl=ql,Vl=Math.abs(Fl*Math.sin(Rl)/Math.cos(Rl))):Vl=jl:(Fl=Math.min(ql,jl),Vl=Math.abs(Fl*Math.sin(Rl)/Math.cos(Rl))),Wl=t.x+Ul.nx*Fl,Hl=t.y+Ul.ny*Fl,Bl=Wl-Ul.ny*Vl*Al,Nl=Hl+Ul.nx*Vl*Al,Yl=t.x+Gl.nx*Fl,Xl=t.y+Gl.ny*Fl,Kl=t};function Ql(e,t){0===t.radius?e.lineTo(t.cx,t.cy):e.arc(t.cx,t.cy,t.radius,t.startAngle,t.endAngle,t.counterClockwise)}function Jl(e,t,n,r){var i=!(arguments.length>4&&void 0!==arguments[4])||arguments[4];return 0===r||0===t.radius?{cx:t.x,cy:t.y,radius:0,startX:t.x,startY:t.y,stopX:t.x,stopY:t.y,startAngle:void 0,endAngle:void 0,counterClockwise:void 0}:($l(e,t,n,r,i),{cx:Bl,cy:Nl,radius:Vl,startX:Yl,startY:Xl,stopX:Wl,stopY:Hl,startAngle:Gl.ang+Math.PI/2*Al,endAngle:Ul.ang-Math.PI/2*Al,counterClockwise:Ll})}var eu={};function tu(e){var t=[];if(null!=e){for(var n=0;n<e.length;n+=2){var r=e[n],i=e[n+1];t.push({x:r,y:i})}return t}}eu.findMidptPtsEtc=function(e,t){var n,r=t.posPts,i=t.intersectionPts,o=t.vectorNormInverse,s=e.pstyle("source-endpoint"),l=e.pstyle("target-endpoint"),u=null!=s.units&&null!=l.units;switch(e.pstyle("edge-distances").value){case"node-position":n=r;break;case"intersection":n=i;break;case"endpoints":if(u){var c=a(this.manualEndptToPx(e.source()[0],s),2),d=c[0],h=c[1],p=a(this.manualEndptToPx(e.target()[0],l),2),f=p[0],g=p[1],v={x1:d,y1:h,x2:f,y2:g};o=function(e,t,n,r){var i=r-t,a=n-e,o=Math.sqrt(a*a+i*i);return{x:-i/o,y:a/o}}(d,h,f,g),n=v}else je("Edge ".concat(e.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint.  Falling back on edge-distances:intersection (default).")),n=i}return{midptPts:n,vectorNormInverse:o}},eu.findHaystackPoints=function(e){for(var t=0;t<e.length;t++){var n=e[t],r=n._private,i=r.rscratch;if(!i.haystack){var a=2*Math.random()*Math.PI;i.source={x:Math.cos(a),y:Math.sin(a)},a=2*Math.random()*Math.PI,i.target={x:Math.cos(a),y:Math.sin(a)}}var o=r.source,s=r.target,l=o.position(),u=s.position(),c=o.width(),d=s.width(),h=o.height(),p=s.height(),f=n.pstyle("haystack-radius").value/2;i.haystackPts=i.allpts=[i.source.x*c*f+l.x,i.source.y*h*f+l.y,i.target.x*d*f+u.x,i.target.y*p*f+u.y],i.midX=(i.allpts[0]+i.allpts[2])/2,i.midY=(i.allpts[1]+i.allpts[3])/2,i.edgeType="haystack",i.haystack=!0,this.storeEdgeProjections(n),this.calculateArrowAngles(n),this.recalculateEdgeLabelProjections(n),this.calculateLabelAngles(n)}},eu.findSegmentsPoints=function(e,t){var n=e._private.rscratch,r=e.pstyle("segment-weights"),i=e.pstyle("segment-distances"),a=e.pstyle("segment-radii"),o=e.pstyle("radius-type"),s=Math.min(r.pfValue.length,i.pfValue.length),l=a.pfValue[a.pfValue.length-1],u=o.pfValue[o.pfValue.length-1];n.edgeType="segments",n.segpts=[],n.radii=[],n.isArcRadius=[];for(var c=0;c<s;c++){var d=r.pfValue[c],h=i.pfValue[c],p=1-d,f=d,g=this.findMidptPtsEtc(e,t),v=g.midptPts,y=g.vectorNormInverse,m={x:v.x1*p+v.x2*f,y:v.y1*p+v.y2*f};n.segpts.push(m.x+y.x*h,m.y+y.y*h),n.radii.push(void 0!==a.pfValue[c]?a.pfValue[c]:l),n.isArcRadius.push("arc-radius"===(void 0!==o.pfValue[c]?o.pfValue[c]:u))}},eu.findLoopPoints=function(e,t,n,r){var i=e._private.rscratch,a=t.dirCounts,o=t.srcPos,s=e.pstyle("control-point-distances"),l=s?s.pfValue[0]:void 0,u=e.pstyle("loop-direction").pfValue,c=e.pstyle("loop-sweep").pfValue,d=e.pstyle("control-point-step-size").pfValue;i.edgeType="self";var h=n,p=d;r&&(h=0,p=l);var f=u-Math.PI/2,g=f-c/2,v=f+c/2,y=String(u+"_"+c);h=void 0===a[y]?a[y]=0:++a[y],i.ctrlpts=[o.x+1.4*Math.cos(g)*p*(h/3+1),o.y+1.4*Math.sin(g)*p*(h/3+1),o.x+1.4*Math.cos(v)*p*(h/3+1),o.y+1.4*Math.sin(v)*p*(h/3+1)]},eu.findCompoundLoopPoints=function(e,t,n,r){var i=e._private.rscratch;i.edgeType="compound";var a=t.srcPos,o=t.tgtPos,s=t.srcW,l=t.srcH,u=t.tgtW,c=t.tgtH,d=e.pstyle("control-point-step-size").pfValue,h=e.pstyle("control-point-distances"),p=h?h.pfValue[0]:void 0,f=n,g=d;r&&(f=0,g=p);var v={x:a.x-s/2,y:a.y-l/2},y={x:o.x-u/2,y:o.y-c/2},m={x:Math.min(v.x,y.x),y:Math.min(v.y,y.y)},b=Math.max(.5,Math.log(.01*s)),x=Math.max(.5,Math.log(.01*u));i.ctrlpts=[m.x,m.y-(1+Math.pow(50,1.12)/100)*g*(f/3+1)*b,m.x-(1+Math.pow(50,1.12)/100)*g*(f/3+1)*x,m.y]},eu.findStraightEdgePoints=function(e){e._private.rscratch.edgeType="straight"},eu.findBezierPoints=function(e,t,n,r,i){var a=e._private.rscratch,o=e.pstyle("control-point-step-size").pfValue,s=e.pstyle("control-point-distances"),l=e.pstyle("control-point-weights"),u=s&&l?Math.min(s.value.length,l.value.length):1,c=s?s.pfValue[0]:void 0,d=l.value[0],h=r;a.edgeType=h?"multibezier":"bezier",a.ctrlpts=[];for(var p=0;p<u;p++){var f=(.5-t.eles.length/2+n)*o*(i?-1:1),g=void 0,v=Et(f);h&&(c=s?s.pfValue[p]:o,d=l.value[p]);var y=void 0!==(g=r?c:void 0!==c?v*c:void 0)?g:f,m=1-d,b=d,x=this.findMidptPtsEtc(e,t),w=x.midptPts,E=x.vectorNormInverse,k={x:w.x1*m+w.x2*b,y:w.y1*m+w.y2*b};a.ctrlpts.push(k.x+E.x*y,k.y+E.y*y)}},eu.findTaxiPoints=function(e,t){var n=e._private.rscratch;n.edgeType="segments";var r=t.posPts,i=t.srcW,a=t.srcH,o=t.tgtW,s=t.tgtH,l="node-position"!==e.pstyle("edge-distances").value,u=e.pstyle("taxi-direction").value,c=u,d=e.pstyle("taxi-turn"),h="%"===d.units,p=d.pfValue,f=p<0,g=e.pstyle("taxi-turn-min-distance").pfValue,v=l?(i+o)/2:0,y=l?(a+s)/2:0,m=r.x2-r.x1,b=r.y2-r.y1,x=function(e,t){return e>0?Math.max(e-t,0):Math.min(e+t,0)},w=x(m,v),E=x(b,y),k=!1;"auto"===c?u=Math.abs(w)>Math.abs(E)?"horizontal":"vertical":"upward"===c||"downward"===c?(u="vertical",k=!0):"leftward"!==c&&"rightward"!==c||(u="horizontal",k=!0);var C,S="vertical"===u,P=S?E:w,D=S?b:m,T=Et(D),_=!1;(k&&(h||f)||!("downward"===c&&D<0||"upward"===c&&D>0||"leftward"===c&&D>0||"rightward"===c&&D<0)||(P=(T*=-1)*Math.abs(P),_=!0),h)?C=(p<0?1+p:p)*P:C=(p<0?P:0)+p*T;var M=function(e){return Math.abs(e)<g||Math.abs(e)>=Math.abs(P)},B=M(C),N=M(Math.abs(P)-Math.abs(C));if((B||N)&&!_)if(S){var z=Math.abs(D)<=a/2,I=Math.abs(m)<=o/2;if(z){var A=(r.x1+r.x2)/2,L=r.y1,O=r.y2;n.segpts=[A,L,A,O]}else if(I){var R=(r.y1+r.y2)/2,V=r.x1,F=r.x2;n.segpts=[V,R,F,R]}else n.segpts=[r.x1,r.y2]}else{var j=Math.abs(D)<=i/2,q=Math.abs(b)<=s/2;if(j){var Y=(r.y1+r.y2)/2,X=r.x1,W=r.x2;n.segpts=[X,Y,W,Y]}else if(q){var H=(r.x1+r.x2)/2,K=r.y1,G=r.y2;n.segpts=[H,K,H,G]}else n.segpts=[r.x2,r.y1]}else if(S){var U=r.y1+C+(l?a/2*T:0),Z=r.x1,$=r.x2;n.segpts=[Z,U,$,U]}else{var Q=r.x1+C+(l?i/2*T:0),J=r.y1,ee=r.y2;n.segpts=[Q,J,Q,ee]}if(n.isRound){var te=e.pstyle("taxi-radius").value,ne="arc-radius"===e.pstyle("radius-type").value[0];n.radii=new Array(n.segpts.length/2).fill(te),n.isArcRadius=new Array(n.segpts.length/2).fill(ne)}},eu.tryToCorrectInvalidPoints=function(e,t){var n=e._private.rscratch;if("bezier"===n.edgeType){var r=t.srcPos,i=t.tgtPos,a=t.srcW,o=t.srcH,s=t.tgtW,l=t.tgtH,u=t.srcShape,c=t.tgtShape,d=t.srcCornerRadius,h=t.tgtCornerRadius,p=t.srcRs,f=t.tgtRs,g=!x(n.startX)||!x(n.startY),v=!x(n.arrowStartX)||!x(n.arrowStartY),y=!x(n.endX)||!x(n.endY),m=!x(n.arrowEndX)||!x(n.arrowEndY),b=3*(this.getArrowWidth(e.pstyle("width").pfValue,e.pstyle("arrow-scale").value)*this.arrowShapeWidth),w=kt({x:n.ctrlpts[0],y:n.ctrlpts[1]},{x:n.startX,y:n.startY}),E=w<b,k=kt({x:n.ctrlpts[0],y:n.ctrlpts[1]},{x:n.endX,y:n.endY}),C=k<b,S=!1;if(g||v||E){S=!0;var P={x:n.ctrlpts[0]-r.x,y:n.ctrlpts[1]-r.y},D=Math.sqrt(P.x*P.x+P.y*P.y),T={x:P.x/D,y:P.y/D},_=Math.max(a,o),M={x:n.ctrlpts[0]+2*T.x*_,y:n.ctrlpts[1]+2*T.y*_},B=u.intersectLine(r.x,r.y,a,o,M.x,M.y,0,d,p);E?(n.ctrlpts[0]=n.ctrlpts[0]+T.x*(b-w),n.ctrlpts[1]=n.ctrlpts[1]+T.y*(b-w)):(n.ctrlpts[0]=B[0]+T.x*b,n.ctrlpts[1]=B[1]+T.y*b)}if(y||m||C){S=!0;var N={x:n.ctrlpts[0]-i.x,y:n.ctrlpts[1]-i.y},z=Math.sqrt(N.x*N.x+N.y*N.y),I={x:N.x/z,y:N.y/z},A=Math.max(a,o),L={x:n.ctrlpts[0]+2*I.x*A,y:n.ctrlpts[1]+2*I.y*A},O=c.intersectLine(i.x,i.y,s,l,L.x,L.y,0,h,f);C?(n.ctrlpts[0]=n.ctrlpts[0]+I.x*(b-k),n.ctrlpts[1]=n.ctrlpts[1]+I.y*(b-k)):(n.ctrlpts[0]=O[0]+I.x*b,n.ctrlpts[1]=O[1]+I.y*b)}S&&this.findEndpoints(e)}},eu.storeAllpts=function(e){var t=e._private.rscratch;if("multibezier"===t.edgeType||"bezier"===t.edgeType||"self"===t.edgeType||"compound"===t.edgeType){t.allpts=[],t.allpts.push(t.startX,t.startY);for(var n=0;n+1<t.ctrlpts.length;n+=2)t.allpts.push(t.ctrlpts[n],t.ctrlpts[n+1]),n+3<t.ctrlpts.length&&t.allpts.push((t.ctrlpts[n]+t.ctrlpts[n+2])/2,(t.ctrlpts[n+1]+t.ctrlpts[n+3])/2);var r;t.allpts.push(t.endX,t.endY),t.ctrlpts.length/2%2==0?(r=t.allpts.length/2-1,t.midX=t.allpts[r],t.midY=t.allpts[r+1]):(r=t.allpts.length/2-3,.5,t.midX=Pt(t.allpts[r],t.allpts[r+2],t.allpts[r+4],.5),t.midY=Pt(t.allpts[r+1],t.allpts[r+3],t.allpts[r+5],.5))}else if("straight"===t.edgeType)t.allpts=[t.startX,t.startY,t.endX,t.endY],t.midX=(t.startX+t.endX+t.arrowStartX+t.arrowEndX)/4,t.midY=(t.startY+t.endY+t.arrowStartY+t.arrowEndY)/4;else if("segments"===t.edgeType){if(t.allpts=[],t.allpts.push(t.startX,t.startY),t.allpts.push.apply(t.allpts,t.segpts),t.allpts.push(t.endX,t.endY),t.isRound){t.roundCorners=[];for(var i=2;i+3<t.allpts.length;i+=2){var a=t.radii[i/2-1],o=t.isArcRadius[i/2-1];t.roundCorners.push(Jl({x:t.allpts[i-2],y:t.allpts[i-1]},{x:t.allpts[i],y:t.allpts[i+1],radius:a},{x:t.allpts[i+2],y:t.allpts[i+3]},a,o))}}if(t.segpts.length%4==0){var s=t.segpts.length/2,l=s-2;t.midX=(t.segpts[l]+t.segpts[s])/2,t.midY=(t.segpts[l+1]+t.segpts[s+1])/2}else{var u=t.segpts.length/2-1;if(t.isRound){var c={x:t.segpts[u],y:t.segpts[u+1]},d=t.roundCorners[u/2],h=[c.x-d.cx,c.y-d.cy],p=d.radius/Math.sqrt(Math.pow(h[0],2)+Math.pow(h[1],2));h=h.map((function(e){return e*p})),t.midX=d.cx+h[0],t.midY=d.cy+h[1],t.midVector=h}else t.midX=t.segpts[u],t.midY=t.segpts[u+1]}}},eu.checkForInvalidEdgeWarning=function(e){var t=e[0]._private.rscratch;t.nodesOverlap||x(t.startX)&&x(t.startY)&&x(t.endX)&&x(t.endY)?t.loggedErr=!1:t.loggedErr||(t.loggedErr=!0,je("Edge `"+e.id()+"` has invalid endpoints and so it is impossible to draw.  Adjust your edge style (e.g. control points) accordingly or use an alternative edge type.  This is expected behaviour when the source node and the target node overlap."))},eu.findEdgeControlPoints=function(e){var t=this;if(e&&0!==e.length){for(var n=this,r=n.cy.hasCompoundNodes(),i={map:new $e,get:function(e){var t=this.map.get(e[0]);return null!=t?t.get(e[1]):null},set:function(e,t){var n=this.map.get(e[0]);null==n&&(n=new $e,this.map.set(e[0],n)),n.set(e[1],t)}},a=[],o=[],s=0;s<e.length;s++){var l=e[s],u=l._private,c=l.pstyle("curve-style").value;if(!l.removed()&&l.takesUpSpace())if("haystack"!==c){var d="unbundled-bezier"===c||c.endsWith("segments")||"straight"===c||"straight-triangle"===c||c.endsWith("taxi"),h="unbundled-bezier"===c||"bezier"===c,p=u.source,f=u.target,g=[p.poolIndex(),f.poolIndex()].sort(),v=i.get(g);null==v&&(v={eles:[]},i.set(g,v),a.push(g)),v.eles.push(l),d&&(v.hasUnbundled=!0),h&&(v.hasBezier=!0)}else o.push(l)}for(var y=function(e){var o=a[e],s=i.get(o),l=void 0;if(!s.hasUnbundled){var u=s.eles[0].parallelEdges().filter((function(e){return e.isBundledBezier()}));Ge(s.eles),u.forEach((function(e){return s.eles.push(e)})),s.eles.sort((function(e,t){return e.poolIndex()-t.poolIndex()}))}var c=s.eles[0],d=c.source(),h=c.target();if(d.poolIndex()>h.poolIndex()){var p=d;d=h,h=p}var f=s.srcPos=d.position(),g=s.tgtPos=h.position(),v=s.srcW=d.outerWidth(),y=s.srcH=d.outerHeight(),m=s.tgtW=h.outerWidth(),b=s.tgtH=h.outerHeight(),w=s.srcShape=n.nodeShapes[t.getNodeShape(d)],E=s.tgtShape=n.nodeShapes[t.getNodeShape(h)],k=s.srcCornerRadius="auto"===d.pstyle("corner-radius").value?"auto":d.pstyle("corner-radius").pfValue,C=s.tgtCornerRadius="auto"===h.pstyle("corner-radius").value?"auto":h.pstyle("corner-radius").pfValue,S=s.tgtRs=h._private.rscratch,P=s.srcRs=d._private.rscratch;s.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var D=0;D<s.eles.length;D++){var T=s.eles[D],_=T[0]._private.rscratch,M=T.pstyle("curve-style").value,B="unbundled-bezier"===M||M.endsWith("segments")||M.endsWith("taxi"),N=!d.same(T.source());if(!s.calculatedIntersection&&d!==h&&(s.hasBezier||s.hasUnbundled)){s.calculatedIntersection=!0;var 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g=a.collection(e.getAllInBox(s[0],s[1],s[2],s[3]));e.redrawHint("select",!0),g.length>0&&e.redrawHint("eles",!0),a.emit({type:"boxend",originalEvent:t,position:{x:o[0],y:o[1]}});var v=function(e){return e.selectable()&&!e.selected()};"additive"===a.selectionType()||h||a.$(n).unmerge(g).unselect(),g.emit("box").stdFilter(v).select().emit("boxselect"),e.redraw()}if(e.hoverData.dragging&&(e.hoverData.dragging=!1,e.redrawHint("select",!0),e.redrawHint("eles",!0),e.redraw()),!s[4]){e.redrawHint("drag",!0),e.redrawHint("eles",!0);var y=c&&c.grabbed();d(u),y&&(c.emit("freeon"),u.emit("free"),e.dragData.didDrag&&(c.emit("dragfreeon"),u.emit("dragfree")))}}s[4]=0,e.hoverData.down=null,e.hoverData.cxtStarted=!1,e.hoverData.draggingEles=!1,e.hoverData.selecting=!1,e.hoverData.isOverThresholdDrag=!1,e.dragData.didDrag=!1,e.hoverData.dragged=!1,e.hoverData.dragDelta=[],e.hoverData.mdownPos=null,e.hoverData.mdownGPos=null,e.hoverData.which=null}}),!1);var k,C,S,P,D,T,_,M,B,N,z,I,A,L=function(t){if(!e.scrollingPage){var n=e.cy,r=n.zoom(),i=n.pan(),a=e.projectIntoViewport(t.clientX,t.clientY),o=[a[0]*r+i.x,a[1]*r+i.y];if(e.hoverData.draggingEles||e.hoverData.dragging||e.hoverData.cxtStarted||0!==e.selection[4])t.preventDefault();else if(n.panningEnabled()&&n.userPanningEnabled()&&n.zoomingEnabled()&&n.userZoomingEnabled()){var s;t.preventDefault(),e.data.wheelZooming=!0,clearTimeout(e.data.wheelTimeout),e.data.wheelTimeout=setTimeout((function(){e.data.wheelZooming=!1,e.redrawHint("eles",!0),e.redraw()}),150),s=null!=t.deltaY?t.deltaY/-250:null!=t.wheelDeltaY?t.wheelDeltaY/1e3:t.wheelDelta/1e3,s*=e.wheelSensitivity,1===t.deltaMode&&(s*=33);var l=n.zoom()*Math.pow(10,s);"gesturechange"===t.type&&(l=e.gestureStartZoom*t.scale),n.zoom({level:l,renderedPosition:{x:o[0],y:o[1]}}),n.emit("gesturechange"===t.type?"pinchzoom":"scrollzoom")}}};e.registerBinding(e.container,"wheel",L,!0),e.registerBinding(t,"scroll",(function(t){e.scrollingPage=!0,clearTimeout(e.scrollingPageTimeout),e.scrollingPageTimeout=setTimeout((function(){e.scrollingPage=!1}),250)}),!0),e.registerBinding(e.container,"gesturestart",(function(t){e.gestureStartZoom=e.cy.zoom(),e.hasTouchStarted||t.preventDefault()}),!0),e.registerBinding(e.container,"gesturechange",(function(t){e.hasTouchStarted||L(t)}),!0),e.registerBinding(e.container,"mouseout",(function(t){var n=e.projectIntoViewport(t.clientX,t.clientY);e.cy.emit({originalEvent:t,type:"mouseout",position:{x:n[0],y:n[1]}})}),!1),e.registerBinding(e.container,"mouseover",(function(t){var n=e.projectIntoViewport(t.clientX,t.clientY);e.cy.emit({originalEvent:t,type:"mouseover",position:{x:n[0],y:n[1]}})}),!1);var O,R,V,F,j,q,Y,X=function(e,t,n,r){return Math.sqrt((n-e)*(n-e)+(r-t)*(r-t))},W=function(e,t,n,r){return(n-e)*(n-e)+(r-t)*(r-t)};if(e.registerBinding(e.container,"touchstart",O=function(t){if(e.hasTouchStarted=!0,E(t)){p(),e.touchData.capture=!0,e.data.bgActivePosistion=void 0;var n=e.cy,i=e.touchData.now,a=e.touchData.earlier;if(t.touches[0]){var o=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);i[0]=o[0],i[1]=o[1]}if(t.touches[1]){o=e.projectIntoViewport(t.touches[1].clientX,t.touches[1].clientY);i[2]=o[0],i[3]=o[1]}if(t.touches[2]){o=e.projectIntoViewport(t.touches[2].clientX,t.touches[2].clientY);i[4]=o[0],i[5]=o[1]}if(t.touches[1]){e.touchData.singleTouchMoved=!0,d(e.dragData.touchDragEles);var l=e.findContainerClientCoords();B=l[0],N=l[1],z=l[2],I=l[3],k=t.touches[0].clientX-B,C=t.touches[0].clientY-N,S=t.touches[1].clientX-B,P=t.touches[1].clientY-N,A=0<=k&&k<=z&&0<=S&&S<=z&&0<=C&&C<=I&&0<=P&&P<=I;var h=n.pan(),f=n.zoom();D=X(k,C,S,P),T=W(k,C,S,P),M=[((_=[(k+S)/2,(C+P)/2])[0]-h.x)/f,(_[1]-h.y)/f];if(T<4e4&&!t.touches[2]){var g=e.findNearestElement(i[0],i[1],!0,!0),v=e.findNearestElement(i[2],i[3],!0,!0);return g&&g.isNode()?(g.activate().emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start=g):v&&v.isNode()?(v.activate().emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start=v):n.emit({originalEvent:t,type:"cxttapstart",position:{x:i[0],y:i[1]}}),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!0,e.touchData.cxtDragged=!1,e.data.bgActivePosistion=void 0,void e.redraw()}}if(t.touches[2])n.boxSelectionEnabled()&&t.preventDefault();else if(t.touches[1]);else if(t.touches[0]){var y=e.findNearestElements(i[0],i[1],!0,!0),m=y[0];if(null!=m&&(m.activate(),e.touchData.start=m,e.touchData.starts=y,e.nodeIsGrabbable(m))){var b=e.dragData.touchDragEles=n.collection(),x=null;e.redrawHint("eles",!0),e.redrawHint("drag",!0),m.selected()?(x=n.$((function(t){return t.selected()&&e.nodeIsGrabbable(t)})),u(x,{addToList:b})):c(m,{addToList:b}),s(m);var w=function(e){return{originalEvent:t,type:e,position:{x:i[0],y:i[1]}}};m.emit(w("grabon")),x?x.forEach((function(e){e.emit(w("grab"))})):m.emit(w("grab"))}r(m,["touchstart","tapstart","vmousedown"],t,{x:i[0],y:i[1]}),null==m&&(e.data.bgActivePosistion={x:o[0],y:o[1]},e.redrawHint("select",!0),e.redraw()),e.touchData.singleTouchMoved=!1,e.touchData.singleTouchStartTime=+new Date,clearTimeout(e.touchData.tapholdTimeout),e.touchData.tapholdTimeout=setTimeout((function(){!1!==e.touchData.singleTouchMoved||e.pinching||e.touchData.selecting||r(e.touchData.start,["taphold"],t,{x:i[0],y:i[1]})}),e.tapholdDuration)}if(t.touches.length>=1){for(var L=e.touchData.startPosition=[null,null,null,null,null,null],O=0;O<i.length;O++)L[O]=a[O]=i[O];var R=t.touches[0];e.touchData.startGPosition=[R.clientX,R.clientY]}}},!1),e.registerBinding(t,"touchmove",R=function(t){var n=e.touchData.capture;if(n||E(t)){var i=e.selection,o=e.cy,s=e.touchData.now,l=e.touchData.earlier,c=o.zoom();if(t.touches[0]){var h=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);s[0]=h[0],s[1]=h[1]}if(t.touches[1]){h=e.projectIntoViewport(t.touches[1].clientX,t.touches[1].clientY);s[2]=h[0],s[3]=h[1]}if(t.touches[2]){h=e.projectIntoViewport(t.touches[2].clientX,t.touches[2].clientY);s[4]=h[0],s[5]=h[1]}var p,f=e.touchData.startGPosition;if(n&&t.touches[0]&&f){for(var g=[],v=0;v<s.length;v++)g[v]=s[v]-l[v];var y=t.touches[0].clientX-f[0],m=y*y,b=t.touches[0].clientY-f[1];p=m+b*b>=e.touchTapThreshold2}if(n&&e.touchData.cxt){t.preventDefault();var w=t.touches[0].clientX-B,_=t.touches[0].clientY-N,z=t.touches[1].clientX-B,I=t.touches[1].clientY-N,L=W(w,_,z,I);if(L/T>=2.25||L>=22500){e.touchData.cxt=!1,e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var O={originalEvent:t,type:"cxttapend",position:{x:s[0],y:s[1]}};e.touchData.start?(e.touchData.start.unactivate().emit(O),e.touchData.start=null):o.emit(O)}}if(n&&e.touchData.cxt){O={originalEvent:t,type:"cxtdrag",position:{x:s[0],y:s[1]}};e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.touchData.start?e.touchData.start.emit(O):o.emit(O),e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxtDragged=!0;var R=e.findNearestElement(s[0],s[1],!0,!0);e.touchData.cxtOver&&R===e.touchData.cxtOver||(e.touchData.cxtOver&&e.touchData.cxtOver.emit({originalEvent:t,type:"cxtdragout",position:{x:s[0],y:s[1]}}),e.touchData.cxtOver=R,R&&R.emit({originalEvent:t,type:"cxtdragover",position:{x:s[0],y:s[1]}}))}else if(n&&t.touches[2]&&o.boxSelectionEnabled())t.preventDefault(),e.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,e.touchData.selecting||o.emit({originalEvent:t,type:"boxstart",position:{x:s[0],y:s[1]}}),e.touchData.selecting=!0,e.touchData.didSelect=!0,i[4]=1,i&&0!==i.length&&void 0!==i[0]?(i[2]=(s[0]+s[2]+s[4])/3,i[3]=(s[1]+s[3]+s[5])/3):(i[0]=(s[0]+s[2]+s[4])/3,i[1]=(s[1]+s[3]+s[5])/3,i[2]=(s[0]+s[2]+s[4])/3+1,i[3]=(s[1]+s[3]+s[5])/3+1),e.redrawHint("select",!0),e.redraw();else if(n&&t.touches[1]&&!e.touchData.didSelect&&o.zoomingEnabled()&&o.panningEnabled()&&o.userZoomingEnabled()&&o.userPanningEnabled()){if(t.preventDefault(),e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),ee=e.dragData.touchDragEles){e.redrawHint("drag",!0);for(var V=0;V<ee.length;V++){var F=ee[V]._private;F.grabbed=!1,F.rscratch.inDragLayer=!1}}var j=e.touchData.start,q=(w=t.touches[0].clientX-B,_=t.touches[0].clientY-N,z=t.touches[1].clientX-B,I=t.touches[1].clientY-N,X(w,_,z,I)),Y=q/D;if(A){var H=(w-k+(z-S))/2,K=(_-C+(I-P))/2,G=o.zoom(),U=G*Y,Z=o.pan(),$=M[0]*G+Z.x,Q=M[1]*G+Z.y,J={x:-U/G*($-Z.x-H)+$,y:-U/G*(Q-Z.y-K)+Q};if(j&&j.active()){var ee=e.dragData.touchDragEles;d(ee),e.redrawHint("drag",!0),e.redrawHint("eles",!0),j.unactivate().emit("freeon"),ee.emit("free"),e.dragData.didDrag&&(j.emit("dragfreeon"),ee.emit("dragfree"))}o.viewport({zoom:U,pan:J,cancelOnFailedZoom:!0}),o.emit("pinchzoom"),D=q,k=w,C=_,S=z,P=I,e.pinching=!0}if(t.touches[0]){h=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);s[0]=h[0],s[1]=h[1]}if(t.touches[1]){h=e.projectIntoViewport(t.touches[1].clientX,t.touches[1].clientY);s[2]=h[0],s[3]=h[1]}if(t.touches[2]){h=e.projectIntoViewport(t.touches[2].clientX,t.touches[2].clientY);s[4]=h[0],s[5]=h[1]}}else if(t.touches[0]&&!e.touchData.didSelect){var te=e.touchData.start,ne=e.touchData.last;if(e.hoverData.draggingEles||e.swipePanning||(R=e.findNearestElement(s[0],s[1],!0,!0)),n&&null!=te&&t.preventDefault(),n&&null!=te&&e.nodeIsDraggable(te))if(p){ee=e.dragData.touchDragEles;var re=!e.dragData.didDrag;re&&u(ee,{inDragLayer:!0}),e.dragData.didDrag=!0;var ie={x:0,y:0};if(x(g[0])&&x(g[1]))if(ie.x+=g[0],ie.y+=g[1],re)e.redrawHint("eles",!0),(ae=e.touchData.dragDelta)&&x(ae[0])&&x(ae[1])&&(ie.x+=ae[0],ie.y+=ae[1]);e.hoverData.draggingEles=!0,ee.silentShift(ie).emit("position drag"),e.redrawHint("drag",!0),e.touchData.startPosition[0]==l[0]&&e.touchData.startPosition[1]==l[1]&&e.redrawHint("eles",!0),e.redraw()}else{var ae;0===(ae=e.touchData.dragDelta=e.touchData.dragDelta||[]).length?(ae.push(g[0]),ae.push(g[1])):(ae[0]+=g[0],ae[1]+=g[1])}if(r(te||R,["touchmove","tapdrag","vmousemove"],t,{x:s[0],y:s[1]}),te&&te.grabbed()||R==ne||(ne&&ne.emit({originalEvent:t,type:"tapdragout",position:{x:s[0],y:s[1]}}),R&&R.emit({originalEvent:t,type:"tapdragover",position:{x:s[0],y:s[1]}})),e.touchData.last=R,n)for(V=0;V<s.length;V++)s[V]&&e.touchData.startPosition[V]&&p&&(e.touchData.singleTouchMoved=!0);if(n&&(null==te||te.pannable())&&o.panningEnabled()&&o.userPanningEnabled()){a(te,e.touchData.starts)&&(t.preventDefault(),e.data.bgActivePosistion||(e.data.bgActivePosistion=bt(e.touchData.startPosition)),e.swipePanning?(o.panBy({x:g[0]*c,y:g[1]*c}),o.emit("dragpan")):p&&(e.swipePanning=!0,o.panBy({x:y*c,y:b*c}),o.emit("dragpan"),te&&(te.unactivate(),e.redrawHint("select",!0),e.touchData.start=null)));h=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);s[0]=h[0],s[1]=h[1]}}for(v=0;v<s.length;v++)l[v]=s[v];n&&t.touches.length>0&&!e.hoverData.draggingEles&&!e.swipePanning&&null!=e.data.bgActivePosistion&&(e.data.bgActivePosistion=void 0,e.redrawHint("select",!0),e.redraw())}},!1),e.registerBinding(t,"touchcancel",V=function(t){var n=e.touchData.start;e.touchData.capture=!1,n&&n.unactivate()}),e.registerBinding(t,"touchend",F=function(t){var i=e.touchData.start;if(e.touchData.capture){0===t.touches.length&&(e.touchData.capture=!1),t.preventDefault();var a=e.selection;e.swipePanning=!1,e.hoverData.draggingEles=!1;var o,s=e.cy,l=s.zoom(),u=e.touchData.now,c=e.touchData.earlier;if(t.touches[0]){var h=e.projectIntoViewport(t.touches[0].clientX,t.touches[0].clientY);u[0]=h[0],u[1]=h[1]}if(t.touches[1]){h=e.projectIntoViewport(t.touches[1].clientX,t.touches[1].clientY);u[2]=h[0],u[3]=h[1]}if(t.touches[2]){h=e.projectIntoViewport(t.touches[2].clientX,t.touches[2].clientY);u[4]=h[0],u[5]=h[1]}if(i&&i.unactivate(),e.touchData.cxt){if(o={originalEvent:t,type:"cxttapend",position:{x:u[0],y:u[1]}},i?i.emit(o):s.emit(o),!e.touchData.cxtDragged){var p={originalEvent:t,type:"cxttap",position:{x:u[0],y:u[1]}};i?i.emit(p):s.emit(p)}return e.touchData.start&&(e.touchData.start._private.grabbed=!1),e.touchData.cxt=!1,e.touchData.start=null,void e.redraw()}if(!t.touches[2]&&s.boxSelectionEnabled()&&e.touchData.selecting){e.touchData.selecting=!1;var f=s.collection(e.getAllInBox(a[0],a[1],a[2],a[3]));a[0]=void 0,a[1]=void 0,a[2]=void 0,a[3]=void 0,a[4]=0,e.redrawHint("select",!0),s.emit({type:"boxend",originalEvent:t,position:{x:u[0],y:u[1]}});f.emit("box").stdFilter((function(e){return e.selectable()&&!e.selected()})).select().emit("boxselect"),f.nonempty()&&e.redrawHint("eles",!0),e.redraw()}if(null!=i&&i.unactivate(),t.touches[2])e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);else if(t.touches[1]);else if(t.touches[0]);else if(!t.touches[0]){e.data.bgActivePosistion=void 0,e.redrawHint("select",!0);var g=e.dragData.touchDragEles;if(null!=i){var v=i._private.grabbed;d(g),e.redrawHint("drag",!0),e.redrawHint("eles",!0),v&&(i.emit("freeon"),g.emit("free"),e.dragData.didDrag&&(i.emit("dragfreeon"),g.emit("dragfree"))),r(i,["touchend","tapend","vmouseup","tapdragout"],t,{x:u[0],y:u[1]}),i.unactivate(),e.touchData.start=null}else{var y=e.findNearestElement(u[0],u[1],!0,!0);r(y,["touchend","tapend","vmouseup","tapdragout"],t,{x:u[0],y:u[1]})}var m=e.touchData.startPosition[0]-u[0],b=m*m,x=e.touchData.startPosition[1]-u[1],w=(b+x*x)*l*l;e.touchData.singleTouchMoved||(i||s.$(":selected").unselect(["tapunselect"]),r(i,["tap","vclick"],t,{x:u[0],y:u[1]}),j=!1,t.timeStamp-Y<=s.multiClickDebounceTime()?(q&&clearTimeout(q),j=!0,Y=null,r(i,["dbltap","vdblclick"],t,{x:u[0],y:u[1]})):(q=setTimeout((function(){j||r(i,["onetap","voneclick"],t,{x:u[0],y:u[1]})}),s.multiClickDebounceTime()),Y=t.timeStamp)),null!=i&&!e.dragData.didDrag&&i._private.selectable&&w<e.touchTapThreshold2&&!e.pinching&&("single"===s.selectionType()?(s.$(n).unmerge(i).unselect(["tapunselect"]),i.select(["tapselect"])):i.selected()?i.unselect(["tapunselect"]):i.select(["tapselect"]),e.redrawHint("eles",!0)),e.touchData.singleTouchMoved=!0}for(var E=0;E<u.length;E++)c[E]=u[E];e.dragData.didDrag=!1,0===t.touches.length&&(e.touchData.dragDelta=[],e.touchData.startPosition=[null,null,null,null,null,null],e.touchData.startGPosition=null,e.touchData.didSelect=!1),t.touches.length<2&&(1===t.touches.length&&(e.touchData.startGPosition=[t.touches[0].clientX,t.touches[0].clientY]),e.pinching=!1,e.redrawHint("eles",!0),e.redraw())}},!1),"undefined"==typeof TouchEvent){var H=[],K=function(e){return{clientX:e.clientX,clientY:e.clientY,force:1,identifier:e.pointerId,pageX:e.pageX,pageY:e.pageY,radiusX:e.width/2,radiusY:e.height/2,screenX:e.screenX,screenY:e.screenY,target:e.target}},G=function(e){H.push(function(e){return{event:e,touch:K(e)}}(e))},U=function(e){for(var t=0;t<H.length;t++){if(H[t].event.pointerId===e.pointerId)return void H.splice(t,1)}},Z=function(e){e.touches=H.map((function(e){return e.touch}))},$=function(e){return"mouse"===e.pointerType||4===e.pointerType};e.registerBinding(e.container,"pointerdown",(function(e){$(e)||(e.preventDefault(),G(e),Z(e),O(e))})),e.registerBinding(e.container,"pointerup",(function(e){$(e)||(U(e),Z(e),F(e))})),e.registerBinding(e.container,"pointercancel",(function(e){$(e)||(U(e),Z(e),V())})),e.registerBinding(e.container,"pointermove",(function(e){$(e)||(e.preventDefault(),function(e){var t=H.filter((function(t){return t.event.pointerId===e.pointerId}))[0];t.event=e,t.touch=K(e)}(e),Z(e),R(e))}))}};var gu={generatePolygon:function(e,t){return this.nodeShapes[e]={renderer:this,name:e,points:t,draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl("polygon",e,t,n,r,i,this.points)},intersectLine:function(e,t,n,r,i,a,o,s){return $t(i,a,this.points,e,t,n/2,r/2,o)},checkPoint:function(e,t,n,r,i,a,o,s){return Xt(e,t,this.points,a,o,r,i,[0,-1],n)}}}};gu.generateEllipse=function(){return this.nodeShapes.ellipse={renderer:this,name:"ellipse",draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i)},intersectLine:function(e,t,n,r,i,a,o,s){return function(e,t,n,r,i,a){var o=n-e,s=r-t;o/=i,s/=a;var l=Math.sqrt(o*o+s*s),u=l-1;if(u<0)return[];var c=u/l;return[(n-e)*c+e,(r-t)*c+t]}(i,a,e,t,n/2+o,r/2+o)},checkPoint:function(e,t,n,r,i,a,o,s){return Kt(e,t,r,i,a,o,n)}}},gu.generateRoundPolygon=function(e,t){return this.nodeShapes[e]={renderer:this,name:e,points:t,getOrCreateCorners:function(e,n,r,i,a,o,s){if(void 0!==o[s]&&o[s+"-cx"]===e&&o[s+"-cy"]===n)return o[s];o[s]=new Array(t.length/2),o[s+"-cx"]=e,o[s+"-cy"]=n;var l=r/2,u=i/2;a="auto"===a?rn(r,i):a;for(var c=new Array(t.length/2),d=0;d<t.length/2;d++)c[d]={x:e+l*t[2*d],y:n+u*t[2*d+1]};var h,p,f,g,v=c.length;for(p=c[v-1],h=0;h<v;h++)f=c[h%v],g=c[(h+1)%v],o[s][h]=Jl(p,f,g,a),p=f,f=g;return o[s]},draw:function(e,t,n,r,i,a,o){this.renderer.nodeShapeImpl("round-polygon",e,t,n,r,i,this.points,this.getOrCreateCorners(t,n,r,i,a,o,"drawCorners"))},intersectLine:function(e,t,n,r,i,a,o,s,l){return function(e,t,n,r,i,a,o,s,l){var u,c=[],d=new Array(2*n.length);l.forEach((function(n,a){0===a?(d[d.length-2]=n.startX,d[d.length-1]=n.startY):(d[4*a-2]=n.startX,d[4*a-1]=n.startY),d[4*a]=n.stopX,d[4*a+1]=n.stopY,0!==(u=Gt(e,t,r,i,n.cx,n.cy,n.radius)).length&&c.push(u[0],u[1])}));for(var h=0;h<d.length/4;h++)0!==(u=Zt(e,t,r,i,d[4*h],d[4*h+1],d[4*h+2],d[4*h+3],!1)).length&&c.push(u[0],u[1]);if(c.length>2){for(var p=[c[0],c[1]],f=Math.pow(p[0]-e,2)+Math.pow(p[1]-t,2),g=1;g<c.length/2;g++){var v=Math.pow(c[2*g]-e,2)+Math.pow(c[2*g+1]-t,2);v<=f&&(p[0]=c[2*g],p[1]=c[2*g+1],f=v)}return p}return c}(i,a,this.points,e,t,0,0,0,this.getOrCreateCorners(e,t,n,r,s,l,"corners"))},checkPoint:function(e,t,n,r,i,a,o,s,l){return function(e,t,n,r,i,a,o,s){for(var l=new Array(2*n.length),u=0;u<s.length;u++){var c=s[u];if(l[4*u+0]=c.startX,l[4*u+1]=c.startY,l[4*u+2]=c.stopX,l[4*u+3]=c.stopY,Math.pow(c.cx-e,2)+Math.pow(c.cy-t,2)<=Math.pow(c.radius,2))return!0}return Yt(e,t,l)}(e,t,this.points,0,0,0,0,this.getOrCreateCorners(a,o,r,i,s,l,"corners"))}}},gu.generateRoundRectangle=function(){return this.nodeShapes["round-rectangle"]=this.nodeShapes.roundrectangle={renderer:this,name:"round-rectangle",points:Jt(4,0),draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i,this.points,a)},intersectLine:function(e,t,n,r,i,a,o,s){return Rt(i,a,e,t,n,r,o,s)},checkPoint:function(e,t,n,r,i,a,o,s){var l=r/2,u=i/2;s="auto"===s?nn(r,i):s;var c=2*(s=Math.min(l,u,s));return!!Xt(e,t,this.points,a,o,r,i-c,[0,-1],n)||(!!Xt(e,t,this.points,a,o,r-c,i,[0,-1],n)||(!!Kt(e,t,c,c,a-l+s,o-u+s,n)||(!!Kt(e,t,c,c,a+l-s,o-u+s,n)||(!!Kt(e,t,c,c,a+l-s,o+u-s,n)||!!Kt(e,t,c,c,a-l+s,o+u-s,n)))))}}},gu.generateCutRectangle=function(){return this.nodeShapes["cut-rectangle"]=this.nodeShapes.cutrectangle={renderer:this,name:"cut-rectangle",cornerLength:8,points:Jt(4,0),draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i,null,a)},generateCutTrianglePts:function(e,t,n,r,i){var a="auto"===i?this.cornerLength:i,o=t/2,s=e/2,l=n-s,u=n+s,c=r-o,d=r+o;return{topLeft:[l,c+a,l+a,c,l+a,c+a],topRight:[u-a,c,u,c+a,u-a,c+a],bottomRight:[u,d-a,u-a,d,u-a,d-a],bottomLeft:[l+a,d,l,d-a,l+a,d-a]}},intersectLine:function(e,t,n,r,i,a,o,s){var l=this.generateCutTrianglePts(n+2*o,r+2*o,e,t,s),u=[].concat.apply([],[l.topLeft.splice(0,4),l.topRight.splice(0,4),l.bottomRight.splice(0,4),l.bottomLeft.splice(0,4)]);return $t(i,a,u,e,t)},checkPoint:function(e,t,n,r,i,a,o,s){var l="auto"===s?this.cornerLength:s;if(Xt(e,t,this.points,a,o,r,i-2*l,[0,-1],n))return!0;if(Xt(e,t,this.points,a,o,r-2*l,i,[0,-1],n))return!0;var u=this.generateCutTrianglePts(r,i,a,o);return Yt(e,t,u.topLeft)||Yt(e,t,u.topRight)||Yt(e,t,u.bottomRight)||Yt(e,t,u.bottomLeft)}}},gu.generateBarrel=function(){return this.nodeShapes.barrel={renderer:this,name:"barrel",points:Jt(4,0),draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i)},intersectLine:function(e,t,n,r,i,a,o,s){var l=this.generateBarrelBezierPts(n+2*o,r+2*o,e,t),u=function(e){var t=Dt({x:e[0],y:e[1]},{x:e[2],y:e[3]},{x:e[4],y:e[5]},.15),n=Dt({x:e[0],y:e[1]},{x:e[2],y:e[3]},{x:e[4],y:e[5]},.5),r=Dt({x:e[0],y:e[1]},{x:e[2],y:e[3]},{x:e[4],y:e[5]},.85);return[e[0],e[1],t.x,t.y,n.x,n.y,r.x,r.y,e[4],e[5]]},c=[].concat(u(l.topLeft),u(l.topRight),u(l.bottomRight),u(l.bottomLeft));return $t(i,a,c,e,t)},generateBarrelBezierPts:function(e,t,n,r){var i=t/2,a=e/2,o=n-a,s=n+a,l=r-i,u=r+i,c=an(e,t),d=c.heightOffset,h=c.widthOffset,p=c.ctrlPtOffsetPct*e,f={topLeft:[o,l+d,o+p,l,o+h,l],topRight:[s-h,l,s-p,l,s,l+d],bottomRight:[s,u-d,s-p,u,s-h,u],bottomLeft:[o+h,u,o+p,u,o,u-d]};return f.topLeft.isTop=!0,f.topRight.isTop=!0,f.bottomLeft.isBottom=!0,f.bottomRight.isBottom=!0,f},checkPoint:function(e,t,n,r,i,a,o,s){var l=an(r,i),u=l.heightOffset,c=l.widthOffset;if(Xt(e,t,this.points,a,o,r,i-2*u,[0,-1],n))return!0;if(Xt(e,t,this.points,a,o,r-2*c,i,[0,-1],n))return!0;for(var d=this.generateBarrelBezierPts(r,i,a,o),h=function(e,t,n){var r,i,a=n[4],o=n[2],s=n[0],l=n[5],u=n[1],c=Math.min(a,s),d=Math.max(a,s),h=Math.min(l,u),p=Math.max(l,u);if(c<=e&&e<=d&&h<=t&&t<=p){var f=[(r=a)-2*(i=o)+s,2*(i-r),r],g=function(e,t,n,r){var i=t*t-4*e*(n-=r);if(i<0)return[];var a=Math.sqrt(i),o=2*e;return[(-t+a)/o,(-t-a)/o]}(f[0],f[1],f[2],e).filter((function(e){return 0<=e&&e<=1}));if(g.length>0)return g[0]}return null},p=Object.keys(d),f=0;f<p.length;f++){var g=d[p[f]],v=h(e,t,g);if(null!=v){var y=g[5],m=g[3],b=g[1],x=Pt(y,m,b,v);if(g.isTop&&x<=t)return!0;if(g.isBottom&&t<=x)return!0}}return!1}}},gu.generateBottomRoundrectangle=function(){return this.nodeShapes["bottom-round-rectangle"]=this.nodeShapes.bottomroundrectangle={renderer:this,name:"bottom-round-rectangle",points:Jt(4,0),draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i,this.points,a)},intersectLine:function(e,t,n,r,i,a,o,s){var l=t-(r/2+o),u=Zt(i,a,e,t,e-(n/2+o),l,e+(n/2+o),l,!1);return u.length>0?u:Rt(i,a,e,t,n,r,o,s)},checkPoint:function(e,t,n,r,i,a,o,s){var l=2*(s="auto"===s?nn(r,i):s);if(Xt(e,t,this.points,a,o,r,i-l,[0,-1],n))return!0;if(Xt(e,t,this.points,a,o,r-l,i,[0,-1],n))return!0;var u=r/2+2*n,c=i/2+2*n;return!!Yt(e,t,[a-u,o-c,a-u,o,a+u,o,a+u,o-c])||(!!Kt(e,t,l,l,a+r/2-s,o+i/2-s,n)||!!Kt(e,t,l,l,a-r/2+s,o+i/2-s,n))}}},gu.registerNodeShapes=function(){var e=this.nodeShapes={},t=this;this.generateEllipse(),this.generatePolygon("triangle",Jt(3,0)),this.generateRoundPolygon("round-triangle",Jt(3,0)),this.generatePolygon("rectangle",Jt(4,0)),e.square=e.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();var n=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",n),this.generateRoundPolygon("round-diamond",n),this.generatePolygon("pentagon",Jt(5,0)),this.generateRoundPolygon("round-pentagon",Jt(5,0)),this.generatePolygon("hexagon",Jt(6,0)),this.generateRoundPolygon("round-hexagon",Jt(6,0)),this.generatePolygon("heptagon",Jt(7,0)),this.generateRoundPolygon("round-heptagon",Jt(7,0)),this.generatePolygon("octagon",Jt(8,0)),this.generateRoundPolygon("round-octagon",Jt(8,0));var r=new Array(20),i=tn(5,0),a=tn(5,Math.PI/5),o=.5*(3-Math.sqrt(5));o*=1.57;for(var s=0;s<a.length/2;s++)a[2*s]*=o,a[2*s+1]*=o;for(s=0;s<5;s++)r[4*s]=i[2*s],r[4*s+1]=i[2*s+1],r[4*s+2]=a[2*s],r[4*s+3]=a[2*s+1];r=en(r),this.generatePolygon("star",r),this.generatePolygon("vee",[-1,-1,0,-.333,1,-1,0,1]),this.generatePolygon("rhomboid",[-1,-1,.333,-1,1,1,-.333,1]),this.generatePolygon("right-rhomboid",[-.333,-1,1,-1,.333,1,-1,1]),this.nodeShapes.concavehexagon=this.generatePolygon("concave-hexagon",[-1,-.95,-.75,0,-1,.95,1,.95,.75,0,1,-.95]);var l=[-1,-1,.25,-1,1,0,.25,1,-1,1];this.generatePolygon("tag",l),this.generateRoundPolygon("round-tag",l),e.makePolygon=function(e){var n,r="polygon-"+e.join("$");return(n=this[r])?n:t.generatePolygon(r,e)}};var vu={timeToRender:function(){return this.redrawTotalTime/this.redrawCount},redraw:function(e){e=e||We();var t=this;void 0===t.averageRedrawTime&&(t.averageRedrawTime=0),void 0===t.lastRedrawTime&&(t.lastRedrawTime=0),void 0===t.lastDrawTime&&(t.lastDrawTime=0),t.requestedFrame=!0,t.renderOptions=e},beforeRender:function(e,t){if(!this.destroyed){null==t&&Ve("Priority is not optional for beforeRender");var n=this.beforeRenderCallbacks;n.push({fn:e,priority:t}),n.sort((function(e,t){return t.priority-e.priority}))}}},yu=function(e,t,n){for(var r=e.beforeRenderCallbacks,i=0;i<r.length;i++)r[i].fn(t,n)};vu.startRenderLoop=function(){var e=this,t=e.cy;if(!e.renderLoopStarted){e.renderLoopStarted=!0;xe((function n(r){if(!e.destroyed){if(t.batching());else if(e.requestedFrame&&!e.skipFrame){yu(e,!0,r);var i=we();e.render(e.renderOptions);var a=e.lastDrawTime=we();void 0===e.averageRedrawTime&&(e.averageRedrawTime=a-i),void 0===e.redrawCount&&(e.redrawCount=0),e.redrawCount++,void 0===e.redrawTotalTime&&(e.redrawTotalTime=0);var o=a-i;e.redrawTotalTime+=o,e.lastRedrawTime=o,e.averageRedrawTime=e.averageRedrawTime/2+o/2,e.requestedFrame=!1}else yu(e,!1,r);e.skipFrame=!1,xe(n)}}))}};var mu=function(e){this.init(e)},bu=mu.prototype;bu.clientFunctions=["redrawHint","render","renderTo","matchCanvasSize","nodeShapeImpl","arrowShapeImpl"],bu.init=function(e){var t=this;t.options=e,t.cy=e.cy;var n=t.container=e.cy.container(),r=t.cy.window();if(r){var i=r.document,a=i.head,o="__________cytoscape_container",s=null!=i.getElementById("__________cytoscape_stylesheet");if(n.className.indexOf(o)<0&&(n.className=(n.className||"")+" "+o),!s){var l=i.createElement("style");l.id="__________cytoscape_stylesheet",l.textContent="."+o+" { position: relative; }",a.insertBefore(l,a.children[0])}"static"===r.getComputedStyle(n).getPropertyValue("position")&&je("A Cytoscape container has style position:static and so can not use UI extensions properly")}t.selection=[void 0,void 0,void 0,void 0,0],t.bezierProjPcts=[.05,.225,.4,.5,.6,.775,.95],t.hoverData={down:null,last:null,downTime:null,triggerMode:null,dragging:!1,initialPan:[null,null],capture:!1},t.dragData={possibleDragElements:[]},t.touchData={start:null,capture:!1,startPosition:[null,null,null,null,null,null],singleTouchStartTime:null,singleTouchMoved:!0,now:[null,null,null,null,null,null],earlier:[null,null,null,null,null,null]},t.redraws=0,t.showFps=e.showFps,t.debug=e.debug,t.hideEdgesOnViewport=e.hideEdgesOnViewport,t.textureOnViewport=e.textureOnViewport,t.wheelSensitivity=e.wheelSensitivity,t.motionBlurEnabled=e.motionBlur,t.forcedPixelRatio=x(e.pixelRatio)?e.pixelRatio:null,t.motionBlur=e.motionBlur,t.motionBlurOpacity=e.motionBlurOpacity,t.motionBlurTransparency=1-t.motionBlurOpacity,t.motionBlurPxRatio=1,t.mbPxRBlurry=1,t.minMbLowQualFrames=4,t.fullQualityMb=!1,t.clearedForMotionBlur=[],t.desktopTapThreshold=e.desktopTapThreshold,t.desktopTapThreshold2=e.desktopTapThreshold*e.desktopTapThreshold,t.touchTapThreshold=e.touchTapThreshold,t.touchTapThreshold2=e.touchTapThreshold*e.touchTapThreshold,t.tapholdDuration=500,t.bindings=[],t.beforeRenderCallbacks=[],t.beforeRenderPriorities={animations:400,eleCalcs:300,eleTxrDeq:200,lyrTxrDeq:150,lyrTxrSkip:100},t.registerNodeShapes(),t.registerArrowShapes(),t.registerCalculationListeners()},bu.notify=function(e,t){var n=this,r=n.cy;this.destroyed||("init"!==e?"destroy"!==e?(("add"===e||"remove"===e||"move"===e&&r.hasCompoundNodes()||"load"===e||"zorder"===e||"mount"===e)&&n.invalidateCachedZSortedEles(),"viewport"===e&&n.redrawHint("select",!0),"load"!==e&&"resize"!==e&&"mount"!==e||(n.invalidateContainerClientCoordsCache(),n.matchCanvasSize(n.container)),n.redrawHint("eles",!0),n.redrawHint("drag",!0),this.startRenderLoop(),this.redraw()):n.destroy():n.load())},bu.destroy=function(){var e=this;e.destroyed=!0,e.cy.stopAnimationLoop();for(var t=0;t<e.bindings.length;t++){var n=e.bindings[t],r=n.target;(r.off||r.removeEventListener).apply(r,n.args)}if(e.bindings=[],e.beforeRenderCallbacks=[],e.onUpdateEleCalcsFns=[],e.removeObserver&&e.removeObserver.disconnect(),e.styleObserver&&e.styleObserver.disconnect(),e.resizeObserver&&e.resizeObserver.disconnect(),e.labelCalcDiv)try{document.body.removeChild(e.labelCalcDiv)}catch(e){}},bu.isHeadless=function(){return!1},[Tl,hu,pu,fu,gu,vu].forEach((function(e){L(bu,e)}));var xu=function(e){return function(){var t=this,n=this.renderer;if(!t.dequeueingSetup){t.dequeueingSetup=!0;var r=ve((function(){n.redrawHint("eles",!0),n.redrawHint("drag",!0),n.redraw()}),e.deqRedrawThreshold),i=e.priority||Re;n.beforeRender((function(i,a){var o=we(),s=n.averageRedrawTime,l=n.lastRedrawTime,u=[],c=n.cy.extent(),d=n.getPixelRatio();for(i||n.flushRenderedStyleQueue();;){var h=we(),p=h-o,f=h-a;if(l<1e3/60){var g=1e3/60-(i?s:0);if(f>=e.deqFastCost*g)break}else if(i){if(p>=e.deqCost*l||p>=e.deqAvgCost*s)break}else if(f>=e.deqNoDrawCost*(1e3/60))break;var v=e.deq(t,d,c);if(!(v.length>0))break;for(var y=0;y<v.length;y++)u.push(v[y])}u.length>0&&(e.onDeqd(t,u),!i&&e.shouldRedraw(t,u,d,c)&&r())}),i(t))}}},wu=function(){function e(n){var r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Le;t(this,e),this.idsByKey=new $e,this.keyForId=new $e,this.cachesByLvl=new $e,this.lvls=[],this.getKey=n,this.doesEleInvalidateKey=r}return r(e,[{key:"getIdsFor",value:function(e){null==e&&Ve("Can not get id list for null key");var t=this.idsByKey,n=this.idsByKey.get(e);return n||(n=new Je,t.set(e,n)),n}},{key:"addIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).add(t)}},{key:"deleteIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).delete(t)}},{key:"getNumberOfIdsForKey",value:function(e){return null==e?0:this.getIdsFor(e).size}},{key:"updateKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t),r=this.getKey(e);this.deleteIdForKey(n,t),this.addIdForKey(r,t),this.keyForId.set(t,r)}},{key:"deleteKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteIdForKey(n,t),this.keyForId.delete(t)}},{key:"keyHasChangedFor",value:function(e){var t=e.id();return this.keyForId.get(t)!==this.getKey(e)}},{key:"isInvalid",value:function(e){return this.keyHasChangedFor(e)||this.doesEleInvalidateKey(e)}},{key:"getCachesAt",value:function(e){var t=this.cachesByLvl,n=this.lvls,r=t.get(e);return r||(r=new $e,t.set(e,r),n.push(e)),r}},{key:"getCache",value:function(e,t){return this.getCachesAt(t).get(e)}},{key:"get",value:function(e,t){var n=this.getKey(e),r=this.getCache(n,t);return null!=r&&this.updateKeyMappingFor(e),r}},{key:"getForCachedKey",value:function(e,t){var n=this.keyForId.get(e.id());return this.getCache(n,t)}},{key:"hasCache",value:function(e,t){return this.getCachesAt(t).has(e)}},{key:"has",value:function(e,t){var n=this.getKey(e);return this.hasCache(n,t)}},{key:"setCache",value:function(e,t,n){n.key=e,this.getCachesAt(t).set(e,n)}},{key:"set",value:function(e,t,n){var r=this.getKey(e);this.setCache(r,t,n),this.updateKeyMappingFor(e)}},{key:"deleteCache",value:function(e,t){this.getCachesAt(t).delete(e)}},{key:"delete",value:function(e,t){var n=this.getKey(e);this.deleteCache(n,t)}},{key:"invalidateKey",value:function(e){var t=this;this.lvls.forEach((function(n){return t.deleteCache(e,n)}))}},{key:"invalidate",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteKeyMappingFor(e);var r=this.doesEleInvalidateKey(e);return r&&this.invalidateKey(n),r||0===this.getNumberOfIdsForKey(n)}}]),e}(),Eu={dequeue:"dequeue",downscale:"downscale",highQuality:"highQuality"},ku=He({getKey:null,doesEleInvalidateKey:Le,drawElement:null,getBoundingBox:null,getRotationPoint:null,getRotationOffset:null,isVisible:Ae,allowEdgeTxrCaching:!0,allowParentTxrCaching:!0}),Cu=function(e,t){this.renderer=e,this.onDequeues=[];var n=ku(t);L(this,n),this.lookup=new wu(n.getKey,n.doesEleInvalidateKey),this.setupDequeueing()},Su=Cu.prototype;Su.reasons=Eu,Su.getTextureQueue=function(e){return this.eleImgCaches=this.eleImgCaches||{},this.eleImgCaches[e]=this.eleImgCaches[e]||[]},Su.getRetiredTextureQueue=function(e){var t=this.eleImgCaches.retired=this.eleImgCaches.retired||{};return t[e]=t[e]||[]},Su.getElementQueue=function(){return this.eleCacheQueue=this.eleCacheQueue||new rt((function(e,t){return t.reqs-e.reqs}))},Su.getElementKeyToQueue=function(){return 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o=t.pop(),s=o.key,l=o.eles[0],u=i.hasCache(l,o.level);if(n[s]=null,!u){r.push(o);var c=this.getBoundingBox(l);this.getElement(l,c,e,o.level,Eu.dequeue)}}return r},Su.removeFromQueue=function(e){var t=this.getElementQueue(),n=this.getElementKeyToQueue(),r=this.getKey(e),i=n[r];null!=i&&(1===i.eles.length?(i.reqs=Ie,t.updateItem(i),t.pop(),n[r]=null):i.eles.unmerge(e))},Su.onDequeue=function(e){this.onDequeues.push(e)},Su.offDequeue=function(e){Ke(this.onDequeues,e)},Su.setupDequeueing=xu({deqRedrawThreshold:100,deqCost:.15,deqAvgCost:.1,deqNoDrawCost:.9,deqFastCost:.9,deq:function(e,t,n){return e.dequeue(t,n)},onDeqd:function(e,t){for(var n=0;n<e.onDequeues.length;n++){(0,e.onDequeues[n])(t)}},shouldRedraw:function(e,t,n,r){for(var i=0;i<t.length;i++)for(var a=t[i].eles,o=0;o<a.length;o++){var s=a[o].boundingBox();if(At(s,r))return!0}return!1},priority:function(e){return e.renderer.beforeRenderPriorities.eleTxrDeq}});var Pu=function(e){var t=this,n=t.renderer=e,r=n.cy;t.layersByLevel={},t.firstGet=!0,t.lastInvalidationTime=we()-500,t.skipping=!1,t.eleTxrDeqs=r.collection(),t.scheduleElementRefinement=ve((function(){t.refineElementTextures(t.eleTxrDeqs),t.eleTxrDeqs.unmerge(t.eleTxrDeqs)}),50),n.beforeRender((function(e,n){n-t.lastInvalidationTime<=250?t.skipping=!0:t.skipping=!1}),n.beforeRenderPriorities.lyrTxrSkip);t.layersQueue=new rt((function(e,t){return t.reqs-e.reqs})),t.setupDequeueing()},Du=Pu.prototype,Tu=0,_u=Math.pow(2,53)-1;Du.makeLayer=function(e,t){var n=Math.pow(2,t),r=Math.ceil(e.w*n),i=Math.ceil(e.h*n),a=this.renderer.makeOffscreenCanvas(r,i),o={id:Tu=++Tu%_u,bb:e,level:t,width:r,height:i,canvas:a,context:a.getContext("2d"),eles:[],elesQueue:[],reqs:0},s=o.context,l=-o.bb.x1,u=-o.bb.y1;return s.scale(n,n),s.translate(l,u),o},Du.getLayers=function(e,t,n){var r=this,i=r.renderer.cy.zoom(),a=r.firstGet;if(r.firstGet=!1,null==n)if((n=Math.ceil(wt(i*t)))<-4)n=-4;else if(i>=3.99||n>2)return null;r.validateLayersElesOrdering(n,e);var o,s,l=r.layersByLevel,u=Math.pow(2,n),c=l[n]=l[n]||[];if(r.levelIsComplete(n,e))return c;!function(){var t=function(t){if(r.validateLayersElesOrdering(t,e),r.levelIsComplete(t,e))return s=l[t],!0},i=function(e){if(!s)for(var r=n+e;-4<=r&&r<=2&&!t(r);r+=e);};i(1),i(-1);for(var a=c.length-1;a>=0;a--){var o=c[a];o.invalid&&Ke(c,o)}}();var d=function(t){var i=(t=t||{}).after;if(function(){if(!o){o=_t();for(var t=0;t<e.length;t++)Mt(o,e[t].boundingBox())}}(),o.w*u*(o.h*u)>16e6)return null;var a=r.makeLayer(o,n);if(null!=i){var s=c.indexOf(i)+1;c.splice(s,0,a)}else(void 0===t.insert||t.insert)&&c.unshift(a);return a};if(r.skipping&&!a)return null;for(var h=null,p=e.length/1,f=!a,g=0;g<e.length;g++){var v=e[g],y=v._private.rscratch,m=y.imgLayerCaches=y.imgLayerCaches||{},b=m[n];if(b)h=b;else{if((!h||h.eles.length>=p||!Ot(h.bb,v.boundingBox()))&&!(h=d({insert:!0,after:h})))return 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i=n?null:e[r],a=n?e[r]:e[r].ele,o=a._private.rscratch,s=o.imgLayerCaches=o.imgLayerCaches||{},l=-4;l<=2;l++){var u=s[l];u&&(i&&this.getEleLevelForLayerLevel(u.level)!==i.level||t(u,a,i))}},Du.haveLayers=function(){for(var e=!1,t=-4;t<=2;t++){var n=this.layersByLevel[t];if(n&&n.length>0){e=!0;break}}return e},Du.invalidateElements=function(e){var t=this;0!==e.length&&(t.lastInvalidationTime=we(),0!==e.length&&t.haveLayers()&&t.updateElementsInLayers(e,(function(e,n,r){t.invalidateLayer(e)})))},Du.invalidateLayer=function(e){if(this.lastInvalidationTime=we(),!e.invalid){var t=e.level,n=e.eles,r=this.layersByLevel[t];Ke(r,e),e.elesQueue=[],e.invalid=!0,e.replacement&&(e.replacement.invalid=!0);for(var i=0;i<n.length;i++){var a=n[i]._private.rscratch.imgLayerCaches;a&&(a[t]=null)}}},Du.refineElementTextures=function(e){var t=this;t.updateElementsInLayers(e,(function(e,n,r){var i=e.replacement;if(i||((i=e.replacement=t.makeLayer(e.bb,e.level)).replaces=e,i.eles=e.eles),!i.reqs)for(var 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+
+</script>
+
+<script>
+
+/* cute-dbt report interaction engine v1 (cute-dbt#178)
+   ----------------------------------------------------------------------------
+   First-party, NOT vendored: this file lives at templates/interaction.js
+   (beside the template it drives), embedded at compile time via
+   asset_embed::INTERACTION_JS (include_str!) and interpolated inline by the
+   askama renderer — never a runtime asset, never an external script tag
+   (the zero-egress contract). Its integrity gates are the banner-pin +
+   end-of-file-sentinel tests in src/adapters/asset_embed.rs (a truncated
+   copy fails CI). */
+// cute-dbt report.html — interactive layer.
+//
+// DOM + class + data-* attributes are the askama template contract.
+// The Mermaid `<g id>` selector is runtime-constructed against the
+// `{svgId}-flowchart-` prefix Mermaid 11.14+ stamps each node group
+// with; never anchor on the bare `^flowchart-` form (it matches
+// nothing on 11.14+ and produces a silent click-binding failure).
+(function () {
+  "use strict";
+
+  // Edge / edge-type colors keyed by the snake_case wire form serialized
+  // by the renderer. Okabe-Ito colorblind-safe palette. UNION variants
+  // both render dashed (row concatenation, not row matching) but split
+  // colors so the legend distinguishes them: orange for `union_all`
+  // (duplicates preserved) and blue for `union_distinct` (deduplicated).
+  // `right` reuses the same blue family — fine because joins render
+  // solid, UNION renders dashed; the dash pattern is the visual cue.
+  // cute-dbt#178 — FIXED categorical hue mapping (Okabe-Ito). The dark-bg
+  // variant preserves hue IDENTITY (blue stays blue) but lifts lightness so
+  // the page-drawn edges + the near-black `from` anchor stay legible on dark
+  // themes. Node FILLS stay light in every theme (light fill + dark label
+  // reads high-contrast on any bg), so only the edges/anchor flip.
+  // One key per line — the edge-vocab-completeness CI gate and the BDD
+  // legend steps line-anchor their greps on `<wire_key>:`.
+  var JOIN_COLORS_LIGHT = {
+    from:           "#1c1c1f",
+    inner:          "#009E73",
+    left:           "#0072B2",
+    right:          "#56B4E9",
+    cross:          "#CC79A7",
+    full:           "#982c61",
+    union_all:      "#E69F00",
+    union_distinct: "#0072B2"
+  };
+  var JOIN_COLORS_DARK = {
+    from:           "#cbd0da",
+    inner:          "#34d399",
+    left:           "#5aa6f0",
+    right:          "#7cc4f2",
+    cross:          "#e08fc0",
+    full:           "#e472a3",
+    union_all:      "#f0b020",
+    union_distinct: "#5aa6f0"
+  };
+  function dagIsDark() { return document.documentElement.classList.contains("dark"); }
+  function dagEdges() { return dagIsDark() ? JOIN_COLORS_DARK : JOIN_COLORS_LIGHT; }
+
+  var ROLE_LABEL = {
+    import:    "Import CTE",
+    transform: "Transform CTE",
+    final:     "Final select"
+  };
+
+  // cute-dbt#180 — role fills/strokes/shapes for the Cytoscape engine,
+  // pinned 1:1 to the Mermaid classDef literals in buildMermaidSource
+  // (role_import / role_transform / role_final). Node fills stay LIGHT in
+  // every theme (light fill + dark label reads high-contrast on any bg) —
+  // only the edges/anchor carry the dark-bg variant, exactly like Mermaid.
+  // The final stroke is #b07400 (design pass-2, cute-dbt#198): the pass-1
+  // #E69F00 failed AA against the light #fdf2dc fill; #b07400 is the same
+  // amber family at AA depth. The legend's two-layer swatch (report.css)
+  // mirrors it via color-mix on --dag-final-stroke.
+  var ROLE_FILLS = {
+    import:    { fill: "#e8f1f8", stroke: "#0072B2" },
+    transform: { fill: "#f4f4f5", stroke: "#8a8a90" },
+    final:     { fill: "#fdf2dc", stroke: "#b07400" }
+  };
+
+  // cute-dbt#180 — the single palette source for BOTH DAG engines.
+  // cyto-dag.js reads colors exclusively through this hook so the
+  // edge-vocab-completeness CI gate (which greps the JOIN_COLORS_LIGHT/_DARK
+  // tables above) covers the Cytoscape engine too — no second palette table
+  // exists to drift. `edges` resolves the active light/dark variant, which
+  // is what fixes the handoff's documented dark-theme bug (the near-black
+  // `from` edges vanishing on dark backgrounds).
+  window.cuteDagPalette = function () {
+    return {
+      edges: dagEdges(),
+      roles: ROLE_FILLS,
+      roleLabels: ROLE_LABEL,
+      nodeText: "#1c1c1f",
+      selected: "#E91E63",
+      fallbackEdge: dagIsDark() ? "#5a606e" : "#9aa0ab"
+    };
+  };
+
+  var DATA = (function () {
+    var el = document.getElementById("cute-dbt-data");
+    if (!el) return { baseline: "", models: [] };
+    try {
+      return JSON.parse(el.textContent);
+    } catch (e) {
+      return { baseline: "", models: [] };
+    }
+  })();
+
+  // cute-dbt#180 — the active DAG engine. "mermaid" is the static default;
+  // theme.js owns the persisted choice (cute-dbt.appearance.v1) and pushes
+  // it here through window.__cuteSetDagEngine before re-rendering. Kept
+  // OUTSIDE `state` deliberately: appearance state belongs to theme.js, this
+  // var is only the render dispatcher's current target.
+  var dagEngine = "mermaid";
+
+  // cute-dbt#429 — Mermaid is initialized once (the tab-DAG boot above the
+  // zero-models early return may run it before the with-models path); this
+  // guard keeps the initialize() call once-only across both entry points.
+  var cuteMermaidInitialized = false;
+
+  var state = {
+    selectedModel:  null,
+    selectedTestId: null,
+    // cute-dbt#201 — the DAG node-detail shelf's selection. Non-null while
+    // the shelf is open; openNodeShelf/closeNodeShelf own it. (The old
+    // leftPanelMode is retired with the Inspect/All-inputs toggle: the
+    // left panel is always the Given panel now.)
+    selectedNodeId: null,
+    // cute-dbt#91 — false: show only updated tests (default); true: show
+    // all tests on the in-scope models. Global + persistent across model
+    // switches. Auto-set to true at boot when the diff updated 0 tests.
+    showAll:        false,
+    // cute-dbt#414 (Slice C) — the single-select 3-axis filter. One of
+    // "all" | "body" | "config" | "unit_test"; "all" (default) shows every
+    // in-scope model. Re-scopes the model <select> to the models whose
+    // selected change-axis fired. Inert in baseline mode (no model carries
+    // `axes`, so the segments are never built and the filter never narrows).
+    axisFilter:     "all"
+  };
+
+  // cute-dbt#139 — report-settings menu state. Both settings are PURE
+  // presentation over the existing payload: `contextLines` re-folds the block
+  // diffs (no recompute), `normalizeEquality` flips the per-cell change lens
+  // (normalized: compare the canonical `key`; strict: compare the authored
+  // `display` — both axes ship on the #138 wire, so neither needs a Rust
+  // round-trip). State is in-memory; loadSettings/saveSettings persist it to
+  // localStorage where available (a graceful in-memory fallback under file://,
+  // where some browsers throw on localStorage access).
+  var SETTINGS_KEY = "cute-dbt.settings.v1";
+  var settings = {
+    contextLines:      3,    // mirrors diffFoldPad's historical default (#132).
+    expandStep:        20,   // lines revealed per gutter-fold click; 0 = all.
+    normalizeEquality: true  // ON: hide format-only cell changes (default).
+  };
+
+  // Read persisted settings, clamping/coercing into the valid ranges. Any
+  // storage error (file:// SecurityError, disabled storage) leaves the
+  // in-memory defaults intact — never throws to the caller.
+  function loadSettings() {
+    var raw;
+    try {
+      raw = window.localStorage && window.localStorage.getItem(SETTINGS_KEY);
+    } catch (e) {
+      raw = null;
+    }
+    if (!raw) return;
+    var parsed;
+    try { parsed = JSON.parse(raw); } catch (e) { return; }
+    if (!parsed || typeof parsed !== "object") return;
+    if (typeof parsed.contextLines === "number" && isFinite(parsed.contextLines)) {
+      settings.contextLines = Math.max(0, Math.min(20, Math.round(parsed.contextLines)));
+    }
+    if (typeof parsed.expandStep === "number" && isFinite(parsed.expandStep)) {
+      settings.expandStep = Math.max(0, Math.min(500, Math.round(parsed.expandStep)));
+    }
+    if (typeof parsed.normalizeEquality === "boolean") {
+      settings.normalizeEquality = parsed.normalizeEquality;
+    }
+  }
+
+  // Persist the current settings. Swallows any storage error (file:// or
+  // disabled storage) so the in-memory state still drives the live UI.
+  function saveSettings() {
+    try {
+      if (window.localStorage) {
+        window.localStorage.setItem(SETTINGS_KEY, JSON.stringify(settings));
+      }
+    } catch (e) {
+      // In-memory only — zero-egress fallback, no rethrow.
+    }
+  }
+
+  $(function () {
+    initExplainHash();
+    // cute-dbt#139 — hydrate persisted settings BEFORE the first render so the
+    // initial diffs fold at the saved context-lines and the cell lens reflects
+    // the saved normalize-equality choice. diffFoldPad mirrors settings.
+    loadSettings();
+    diffFoldPad = settings.contextLines;
+    renderBanner();
+    // cute-dbt#269 — fill the project panel's hook-diff slots before the
+    // zero-models early return: a dbt_project.yml-only PR still renders
+    // its hook SQL diff.
+    renderProjectHookDiffs();
+    // cute-dbt#350 — the "Data tables" seed section, built before the
+    // zero-models early return: a seeds-only PR (no in-scope model) still
+    // renders its seed data. JS-built (no static markup) so the default
+    // HTML carries zero seed bytes — the section appears only when the
+    // gated `DATA.seed_cards` payload is present.
+    renderSeedDataTables();
+    // cute-dbt#424 — wire the subject-lens tab strip BEFORE the zero-models
+    // early return. A macro-only PR (the macro lens's primary case) has zero
+    // in-scope models, but its content now lives in the Macros tab — which
+    // would be permanently unreachable if the toggle were only bound in the
+    // with-models path below. The lens tabs are always present in the DOM, so
+    // the binding is unconditional (a pure in-place class/hidden/aria toggle,
+    // distinct from the DAG-shelf handler — territory partition with #398).
+    bindLensTabs();
+    // cute-dbt#429/#430/#431 — the tab-scoped change DAGs (the Models-tab
+    // mini-DAG + the Macros-tab macro DAG) are wired ABOVE the zero-models
+    // early return: a macro-only PR (zero in-scope models) must still render
+    // its Macros-tab DAG, and the mini-DAG is a standalone overview. Mermaid
+    // must be initialized before they render — init it ONCE here (idempotent;
+    // the with-models path no longer re-inits). bindTabDagHandlers wires the
+    // axis-filter + macro-pick re-render (delegated, so they survive the
+    // dropdown/section rebuilds) and __cuteRerenderDagTabs the engine flip.
+    if (typeof window.mermaid !== "undefined" && !cuteMermaidInitialized) {
+      window.mermaid.initialize({
+        startOnLoad: false,
+        securityLevel: "strict",
+        theme: "base",
+        fontFamily: 'system-ui,-apple-system,"Segoe UI",sans-serif'
+      });
+      cuteMermaidInitialized = true;
+    }
+    bindTabDagHandlers();
+    renderTabDags();
+    // The engine-flip + theme-re-tint hook must re-render the tab DAGs too
+    // (the settings picker swaps engines live). theme.js calls
+    // window.__cuteRerenderDag after pushing the engine via
+    // __cuteSetDagEngine; chain the tab-DAG re-render onto whatever the
+    // with-models path installs (or install a tab-only hook here when there
+    // are zero models, so the macro-only Macros-tab DAG still swaps engines).
+    if (!DATA.models.length) {
+      window.__cuteRerenderDag = function () { renderTabDags(); };
+    }
+    if (!DATA.models.length) {
+      $(".test-selection").hide();
+      $(".model-sql").hide();
+      $(".model-yaml").hide();
+      $(".cte-dag").hide();
+      $(".model-findings").hide();
+      $(".test-section").hide();
+      $(".panel-row").hide();
+      return;
+    }
+    // cute-dbt#91 — open in All-tests mode when the diff updated zero
+    // tests (the common SQL-only PR), so the reviewer lands on content
+    // rather than the empty 0-updated view.
+    state.showAll = (countUpdated() === 0);
+    // Default model foregrounds the diff's updates: first (deterministic
+    // order) model with >=1 updated test, else the first model.
+    state.selectedModel = defaultModelName();
+    // First VISIBLE test under the resolved mode (so auto-All lands on a
+    // real selected test with content, not the empty panel).
+    state.selectedTestId = firstVisibleTestId(currentModel());
+    // Mermaid config is static across model switches — initialize once
+    // (cute-dbt#40 CR-4). renderDag() then only invokes mermaid.render. The
+    // tab-DAG boot above (cute-dbt#429) may already have run this; the
+    // `cuteMermaidInitialized` guard keeps it once-only either way.
+    if (typeof window.mermaid !== "undefined" && !cuteMermaidInitialized) {
+      window.mermaid.initialize({
+        startOnLoad: false,
+        securityLevel: "strict",
+        theme: "base",
+        fontFamily: 'system-ui,-apple-system,"Segoe UI",sans-serif'
+      });
+      cuteMermaidInitialized = true;
+    }
+    renderTestModeToggle();
+    renderAxisFilter();
+    // cute-dbt#416 — the node-less REMOVED models summary renders once at
+    // boot (the set is fixed for the report; no per-model dependency).
+    renderRemovedModels();
+    // cute-dbt#420 — the top-of-report PR review-comment count button renders
+    // once at boot (the total is fixed for the report). No-op when
+    // DATA.pr_comments is absent (off-gate / no PR context / no comments).
+    renderPrCommentsCountButton();
+    renderModelSelector();
+    renderTestSelector();
+    renderForSelectedModel();
+    bindGlobalHandlers();
+    bindSettingsMenu();
+    // cute-dbt#178 — theme switches re-tint the DAG: re-render legend +
+    // the ACTIVE engine so edge/anchor colours pick up the light/dark
+    // variant. theme.js calls this hook after flipping [data-theme] /
+    // the .dark class (and after an engine flip, cute-dbt#180).
+    // cute-dbt#429 — chain the tab-scoped DAGs (mini-DAG + macro DAG) onto the
+    // same engine-flip / theme-re-tint hook so the settings picker swaps them
+    // live alongside the CTE DAG.
+    window.__cuteRerenderDag = function () { renderDagLegend(); renderDag(); renderTabDags(); };
+    // cute-dbt#180 — theme.js pushes the picker's engine here, then calls
+    // __cuteRerenderDag. A pure state setter: the render itself always goes
+    // through the renderDag() dispatcher (engine swap = destroy + rebuild,
+    // in place, no reload).
+    window.__cuteSetDagEngine = function (engine) {
+      dagEngine = engine === "cytoscape" ? "cytoscape" : "mermaid";
+    };
+    // cute-dbt#180 / cute-dbt#201 — the shared node-selection seam. The
+    // Cytoscape tap (cyto-dag.js) and the #194 findings pin both route
+    // through this one global; since the #201 restructure it opens the DAG
+    // node-detail shelf. Deliberately NO renderDag() call (the spike's
+    // no-renderDag-per-click rule: a rebuild would reset pan/zoom and wipe
+    // the in-place lineage highlight). Mermaid's own activate() keeps its
+    // full re-render — selection there is baked into the generated source.
+    window.__cuteSelectNode = function (id) {
+      openNodeShelf(id);
+    };
+    // cute-dbt#178 — delegated hover/focus handlers for the column-header
+    // tooltips (the th.has-col-meta triggers decorateColHeader marks).
+    bindColTooltips();
+    // cute-dbt#201 — the DAG node-detail shelf's ✕ close + the model-card
+    // column test-count expanders (delegated; shelf content is rebuilt per
+    // node selection).
+    bindShelf();
+    // cute-dbt#202 — delegated hover/focus handlers for the rich hover
+    // cards (model pills, format badges, the overrides badge).
+    bindModelTips();
+    // cute-dbt#232 — viewport-edge tagging for the pure-CSS bubble family
+    // (geometry annotation only; visibility stays CSS :hover/:focus).
+    bindTipEdgeTagger();
+    // cute-dbt#402 (epic #360) / cute-dbt#424 — the subject-lens tab strip
+    // (Models / Macros / Project) is wired ABOVE the zero-models early return
+    // (a macro-only PR must still reach its Macros tab), so it is NOT
+    // re-bound here: bindLensTabs uses delegated `$(document).on` handlers, so
+    // a second call would double-fire every tab click.
+  });
+
+  // cute-dbt#91 — scope-toggle helpers --------------------------------
+
+  // Count of updated (changed) tests across all in-scope models — the
+  // banner's "X updated" and the auto-All-on-load trigger.
+  function countUpdated() {
+    var n = 0;
+    DATA.models.forEach(function (m) {
+      m.tests.forEach(function (t) { if (t.changed) n += 1; });
+    });
+    return n;
+  }
+
+  // Updated-test count for a single model.
+  function updatedCount(m) {
+    return m.tests.filter(function (t) { return t.changed; }).length;
+  }
+
+  // The tests visible for a model under the current toggle mode.
+  function visibleTests(m) {
+    if (!m) return [];
+    return state.showAll ? m.tests : m.tests.filter(function (t) { return t.changed; });
+  }
+
+  // Default model on load: first model (deterministic order) with >=1
+  // updated test, else the first model.
+  function defaultModelName() {
+    var withUpdated = DATA.models.filter(function (m) { return updatedCount(m) > 0; })[0];
+    return (withUpdated || DATA.models[0]).name;
+  }
+
+  // First visible test id for a model under the resolved mode, or null.
+  function firstVisibleTestId(m) {
+    var vis = visibleTests(m);
+    return vis.length ? vis[0].id : null;
+  }
+
+  function initExplainHash() {
+    function sync() {
+      var on = window.location.hash === "#explain";
+      $(".error-card").toggleClass("is-visible", on);
+    }
+    sync();
+    $(window).on("hashchange", sync);
+  }
+
+  function renderBanner() {
+    // cute-dbt#91 — "K models in scope · X unit tests updated". K is the
+    // number of in-scope models; X is the count of updated (changed)
+    // tests across the RENDERED payload (not the domain's changed-set
+    // length — a changed test whose model didn't resolve is never
+    // rendered, so they can legitimately differ). The empty case keeps the
+    // locked BANNER_EMPTY_SCOPE string the server-side contract pins. JS
+    // rewrites the server text so swapping DATA in DevTools repaints
+    // consistently without a round trip (banner wording is JS-only;
+    // compose_banner_text stays untouched).
+    var k = DATA.models.length;
+    var x = countUpdated();
+    var text;
+    if (k === 0) {
+      text = "0 unit tests in scope";
+    } else {
+      var modelNoun = k === 1 ? "model" : "models";
+      var testNoun = x === 1 ? "unit test" : "unit tests";
+      text = k + " " + modelNoun + " in scope · " + x + " " + testNoun + " updated";
+    }
+    $(".diff-scope-text").text(text);
+    $(".diff-scope-baseline").text(DATA.baseline);
+    // cute-dbt#96 — affirm block-precision ran when it narrowed every test
+    // to context (k models in scope, 0 updated). The element exists only on
+    // the PR-diff path (absent in baseline mode); jQuery no-ops if absent.
+    // Copy is static in the template (LOCKED, CPO Finding A) — toggle only.
+    $(".zero-updated-affirm").prop("hidden", !(k > 0 && x === 0));
+  }
+
+  function currentModel() {
+    return DATA.models.filter(function (m) { return m.name === state.selectedModel; })[0];
+  }
+
+  function currentTest() {
+    var m = currentModel();
+    if (!m) return null;
+    return m.tests.filter(function (t) { return t.id === state.selectedTestId; })[0];
+  }
+
+  // cute-dbt#91 — the per-model selector count is toggle-dependent: the
+  // updated count in Updated-only mode, the total in All-tests mode.
+  function modelOptionLabel(m) {
+    var n = state.showAll ? m.tests.length : updatedCount(m);
+    return m.name + "  (" + n + ")";
+  }
+
+  // cute-dbt#414 (Slice C) — does this model fire the active filter axis?
+  // "all" matches every in-scope model; a specific axis matches only when
+  // the model's `axes.<axis>` bool is true. Baseline-mode models carry no
+  // `axes` object, so a specific axis never matches there — but the filter
+  // is never built in baseline mode (see renderAxisFilter), so the active
+  // filter is always "all" and every model matches.
+  function modelMatchesAxis(m) {
+    if (state.axisFilter === "all") return true;
+    return !!(m && m.axes && m.axes[state.axisFilter]);
+  }
+
+  // The in-scope models the active axis filter admits (DATA.models order
+  // preserved). "all" yields the full set; the dropdown is re-scoped to
+  // exactly these.
+  function filteredModels() {
+    return DATA.models.filter(modelMatchesAxis);
+  }
+
+  // cute-dbt#413 — group the model dropdown by shared schema.yml. Every
+  // model patched by the same `config_file` (its scheme-stripped
+  // patch_path) lands under one native <optgroup label="…">. Presentation
+  // only as far as #413 went; cute-dbt#414 layers the single-select axis
+  // filter on top — the dropdown is built from filteredModels(), so a
+  // selected axis re-scopes BOTH the flat options AND the <optgroup>s
+  // (a group with zero matching models under the active filter is never
+  // emitted, never an empty <optgroup>). In baseline mode no model carries
+  // a `config_file` OR `axes`, so this falls back to the flat, unfiltered
+  // list and the baseline goldens stay byte-identical. Order is preserved
+  // from DATA.models (deterministic, BTreeMap-backed), so the first model
+  // under each schema seeds that group's position; ungrouped models (no
+  // patch_path) keep their inline position.
+  function renderModelSelector() {
+    var $sel = $("#model-select").empty();
+    var models = filteredModels();
+    var anyGroup = models.some(function (m) { return m.config_file; });
+    if (!anyGroup) {
+      models.forEach(function (m) {
+        $sel.append($("<option>").val(m.name).text(modelOptionLabel(m)));
+      });
+    } else {
+      // Build groups in first-seen order; ungrouped models flush inline so
+      // the dropdown never silently drops a model that lacks a schema.yml.
+      // `Object.create(null)` (not `{}`): the map is keyed by the
+      // manifest-derived config_file, so a prototype-less object is the
+      // right idiom — a key like "constructor" / "__proto__" can never
+      // collide with an inherited member (gemini, PR #423).
+      var groups = Object.create(null);
+      var order = [];
+      models.forEach(function (m) {
+        var key = m.config_file || "";
+        if (!(key in groups)) { groups[key] = []; order.push(key); }
+        groups[key].push(m);
+      });
+      order.forEach(function (key) {
+        var bucket = groups[key];
+        if (key === "") {
+          bucket.forEach(function (m) {
+            $sel.append($("<option>").val(m.name).text(modelOptionLabel(m)));
+          });
+          return;
+        }
+        var $og = $("<optgroup>").attr("label", key);
+        bucket.forEach(function (m) {
+          $og.append($("<option>").val(m.name).text(modelOptionLabel(m)));
+        });
+        $sel.append($og);
+      });
+    }
+    // cute-dbt#414 — if the active axis filter dropped the currently-selected
+    // model, fall back to the first VISIBLE model (the #91/#413 default-
+    // selection logic) so the dropdown never lands on an empty selection.
+    // Reselect the test under the new model too, so the test card + DAG +
+    // findings all repaint for a model the filter actually admits.
+    var stillVisible = models.some(function (m) { return m.name === state.selectedModel; });
+    if (!stillVisible) {
+      state.selectedModel = models.length ? models[0].name : null;
+      state.selectedTestId = firstVisibleTestId(currentModel());
+    }
+    $sel.val(state.selectedModel);
+    $sel.off("change.cuteDbt").on("change.cuteDbt", function () {
+      state.selectedModel = $(this).val();
+      // Land on the first VISIBLE test under the (persistent) toggle mode.
+      // (renderForSelectedModel closes any open node shelf — cute-dbt#201.)
+      state.selectedTestId = firstVisibleTestId(currentModel());
+      renderTestSelector();
+      renderForSelectedModel();
+    });
+  }
+
+  // cute-dbt#414 (Slice C) — the single-select 3-axis filter toggle. One
+  // segment per axis plus an `All` reset, single-select (radiogroup
+  // semantics: aria-pressed marks the active segment). JS-BUILT into the
+  // static empty `.axis-filter` container, and ONLY when some in-scope
+  // model carries an `axes` object — i.e. --pr-diff mode (the #413
+  // axis-chip gating, mirrored exactly). In baseline mode the container
+  // stays empty + hidden, so the baseline goldens are byte-identical and
+  // state.axisFilter is permanently "all" (no segment to flip it). The
+  // segment labels mirror the #413 axis-chip labels (AXIS_LABELS). The
+  // active state is synced from state.axisFilter so a re-render after a
+  // filter flip repaints the pressed segment without rebuilding handlers.
+  var FILTER_SEGMENTS = [
+    { axis: "all", label: "All" },
+    { axis: "body", label: "Body" },
+    { axis: "config", label: "Config" },
+    { axis: "unit_test", label: "Unit test" }
+  ];
+  function renderAxisFilter() {
+    var $filter = $('[data-testid="axis-filter"]').empty();
+    var anyAxes = DATA.models.some(function (m) { return m.axes; });
+    if (!anyAxes) {
+      // Baseline mode (or a payload predating #413): no axis attribution,
+      // so the filter never appears and never narrows the dropdown.
+      $filter.prop("hidden", true);
+      return;
+    }
+    FILTER_SEGMENTS.forEach(function (seg) {
+      var active = seg.axis === state.axisFilter;
+      $filter.append(
+        $("<button>")
+          .attr("type", "button")
+          .attr("data-axis", seg.axis)
+          .attr("aria-pressed", active ? "true" : "false")
+          .toggleClass("is-active", active)
+          .text(seg.label)
+      );
+    });
+    $filter.prop("hidden", false);
+  }
+
+  // cute-dbt#201 — size a <select> to its WIDEST option (+ arrow/padding)
+  // so its width is static across selections instead of stretching to fill
+  // the row. Canvas-measured with the select's own computed font; the
+  // cached canvas is a measurement scratchpad only (never attached to the
+  // DOM, nothing rendered — zero-egress untouched). The CSS clamps the
+  // resulting width with `max-width: 100%` so a long test name can never
+  // push a narrow viewport (the #157 lesson).
+  function sizeSelectToWidest($sel) {
+    var el = $sel[0];
+    if (!el || !el.options.length) return;
+    var cs = window.getComputedStyle(el);
+    var c = sizeSelectToWidest._c || (sizeSelectToWidest._c = document.createElement("canvas"));
+    var ctx = c.getContext("2d");
+    ctx.font = cs.fontWeight + " " + cs.fontSize + " " + cs.fontFamily;
+    var max = 0;
+    for (var i = 0; i < el.options.length; i++) {
+      var w = ctx.measureText(el.options[i].text).width;
+      if (w > max) max = w;
+    }
+    el.style.width = Math.ceil(max + 46) + "px"; // text + arrow + L/R padding
+  }
+
+  function renderTestSelector() {
+    var $sel = $("#test-select").empty();
+    var m = currentModel();
+    var tests = visibleTests(m);
+    tests.forEach(function (t) {
+      $sel.append($("<option>").val(t.id).text(t.name));
+    });
+    $sel.val(state.selectedTestId);
+    sizeSelectToWidest($sel);
+    $sel.off("change.cuteDbt").on("change.cuteDbt", function () {
+      // (renderForSelectedModel closes any open node shelf — cute-dbt#201.)
+      state.selectedTestId = $(this).val();
+      renderForSelectedModel();
+    });
+    renderZeroUpdatedHint(m, tests);
+  }
+
+  // cute-dbt#91 — inline hint when a model has tests but none are visible
+  // in Updated-only mode (the empty #test-select case). Hidden in
+  // All-tests mode and whenever there are visible tests.
+  function renderZeroUpdatedHint(m, tests) {
+    var $hint = $(".zero-updated-hint");
+    var show = m && !state.showAll && tests.length === 0 && m.tests.length > 0;
+    if (show) {
+      var total = m.tests.length;
+      var noun = total === 1 ? "test" : "tests";
+      $hint
+        .text("0 updated — switch to All tests to see this model’s " + total + " " + noun + ".")
+        .prop("hidden", false);
+    } else {
+      $hint.text("").prop("hidden", true);
+    }
+  }
+
+  // cute-dbt#267 — config-tree provenance chips: why a config-widened
+  // model is in this report. One chip per attribution entry
+  // ("+materialized via dbt_project.yml · models.shop.marts"), rendered
+  // from the Rust-computed m.config_attributions (Rust computes, JS only
+  // renders). Hidden for models with no attribution — the element stays
+  // out of the accessibility tree (the #74 no-empty-landmark intent).
+  // cute-dbt#268 — var-reference chips ride the same row: this in-scope
+  // model references an edited dbt_project.yml var at the named tier
+  // ("reads var 'dq_threshold' · direct" / "· via add_dq_flags").
+  // Context only — a var edit never widened this model into scope.
+  // cute-dbt#413 — the per-model change-axis chips (Body / Config / Unit
+  // test): one labeled chip per axis that fired for the selected model,
+  // reflecting exactly which of dbt's state:modified sub-selectors this PR
+  // touched. Plus a NON-interactive config-file chip naming the model's
+  // schema.yml (the optgroup grouping key, surfaced inline so the grouping
+  // is legible without opening the dropdown). Rendered from the
+  // Rust-computed m.axes / m.config_file (Rust computes, JS only renders);
+  // both are present only in --pr-diff mode, so baseline mode renders zero
+  // axis chips. The chips reuse the design-system chip recipe family.
+  //
+  // A SEPARATE row from renderModelAttribution: the axis chips are a core
+  // PR-review affordance that must stay visible regardless of the
+  // project-state display toggle (html[data-project=off] hides the
+  // attribution row, never this one).
+  var AXIS_LABELS = { body: "Body", config: "Config", unit_test: "Unit test" };
+  var AXIS_ORDER = ["body", "config", "unit_test"];
+  function renderModelAxes(m) {
+    var $row = $('[data-testid="model-axes"]').empty();
+    var axes = (m && m.axes) || null;
+    var configFile = (m && m.config_file) || null;
+    // cute-dbt#416 — the top-level NEW state chip. The state is mutually
+    // exclusive (new | modified); only NEW renders an extra chip (the axis
+    // chips already say MODIFIED). Present only in --pr-diff mode (baseline
+    // omits `state`). REMOVED never reaches here — removed models are
+    // node-less and surfaced as the separate Models-lens summary.
+    var isNew = (m && m.state) === "new";
+    var firedAxes = axes
+      ? AXIS_ORDER.filter(function (k) { return axes[k]; })
+      : [];
+    if (!isNew && !firedAxes.length && !configFile) {
+      $row.prop("hidden", true);
+      return;
+    }
+    // The NEW state chip leads the row — the top-level lifecycle state sits
+    // ahead of the per-axis MODIFIED attribution.
+    if (isNew) {
+      $row.append(
+        $("<span>").addClass("axis-chip state-chip state-chip-new")
+          .attr("data-testid", "state-chip")
+          .attr("data-state", "new")
+          .text("New")
+      );
+    }
+    // Axis chips next — the primary 3-axis attribution affordance.
+    firedAxes.forEach(function (k) {
+      $row.append(
+        $("<span>").addClass("axis-chip axis-chip-" + k)
+          .attr("data-testid", "axis-chip")
+          .attr("data-axis", k)
+          .text(AXIS_LABELS[k])
+      );
+    });
+    // The non-interactive config-file chip — names the schema.yml that
+    // groups this model in the dropdown. NOT a button: it is contextual,
+    // never clickable (the filter toggle is the separate #414 Slice C).
+    if (configFile) {
+      $row.append(
+        $("<span>").addClass("config-file-chip")
+          .attr("data-testid", "config-file-chip")
+          .text(configFile)
+      );
+    }
+    $row.prop("hidden", false);
+  }
+
+  // cute-dbt#416 — the REMOVED models summary. Removed models are node-less
+  // (no current node → no dropdown detail), so they surface ONCE at the
+  // Models lens level as a count + a short path list, never as dropdown
+  // options. Rendered once at boot (the set is fixed for the report). The
+  // final placement is a #360 design-revisitable default.
+  function renderRemovedModels() {
+    var $row = $('[data-testid="removed-models"]');
+    if (!$row.length) { return; }
+    $row.empty();
+    var removed = (DATA && DATA.removed_models) || [];
+    if (!removed.length) { $row.prop("hidden", true); return; }
+    var label = removed.length === 1
+      ? "1 model removed"
+      : removed.length + " models removed";
+    $row.append(
+      $("<span>").addClass("axis-chip state-chip state-chip-removed")
+        .attr("data-testid", "removed-summary-chip")
+        .attr("data-removed-count", String(removed.length))
+        .text(label)
+    );
+    removed.forEach(function (path) {
+      $row.append(
+        $("<span>").addClass("removed-path-chip")
+          .attr("data-testid", "removed-path-chip")
+          .text(path)
+      );
+    });
+    $row.prop("hidden", false);
+  }
+
+  // ---- PR review comments (#419 / #420 / #421 / #422, epic #353) -------
+  //
+  // The whole comment surface is CLIENT-RENDERED from DATA.pr_comments (the
+  // pr_dag / seed_cards precedent) — the Rust side already ANCHORED each
+  // review thread onto a rendered diff line (the shipped #418 anchoring) and
+  // grouped the threads per model, so the JS only PLACES them: the top count
+  // button (#420), the per-model count tooltip (#421), and the inline thread
+  // at its Model-SQL diff line (#419). Absent the `pr-comments` experiment /
+  // no PR context / no comments, DATA.pr_comments is omitted and every
+  // function here no-ops — keeping every default golden byte-identical.
+
+  // The per-model comment bucket for model `m`, matched on the model's
+  // project-relative file path (`m.path` — the join key BOTH the model
+  // payload and the bucket carry; the model payload does NOT carry the node
+  // id). Null when the model carries no anchored comments.
+  function prCommentBucketFor(m) {
+    var pc = DATA && DATA.pr_comments;
+    if (!pc || !pc.by_model || !m || !m.path) { return null; }
+    for (var i = 0; i < pc.by_model.length; i++) {
+      if (pc.by_model[i].model_path === m.path) { return pc.by_model[i]; }
+    }
+    return null;
+  }
+
+  // Find the model NAME owning a comment bucket (so navigation can select it
+  // in the <select>, which keys on name). Matches on the bucket's
+  // model_path. Null when the bucket's model is not a current dropdown model.
+  function modelNameForBucket(bucket) {
+    for (var i = 0; i < DATA.models.length; i++) {
+      if (DATA.models[i].path === bucket.model_path) { return DATA.models[i].name; }
+    }
+    return null;
+  }
+
+  // #420 — the top-of-report total-count navigation button. Fills the
+  // always-present static .pr-comments-count container and unhides it;
+  // clicking navigates to the FIRST anchored thread (selects its model +
+  // scrolls to the anchored Model-SQL diff line). Rendered ONCE at boot (the
+  // total is fixed for the report). Absent comments ⇒ the container stays
+  // hidden (byte-identity-safe: it is identical empty markup in every report).
+  function renderPrCommentsCountButton() {
+    var $btn = $('[data-testid="pr-comments-count"]');
+    if (!$btn.length) { return; }
+    var pc = DATA && DATA.pr_comments;
+    var total = (pc && typeof pc.total === "number") ? pc.total : 0;
+    if (!pc || total < 1) { $btn.prop("hidden", true).empty(); return; }
+    var label = total === 1 ? "1 PR review comment" : total + " PR review comments";
+    $btn.empty()
+      .append($("<span>").addClass("pr-comments-count-icon").attr("aria-hidden", "true").text("💬"))
+      .append($("<span>").addClass("pr-comments-count-label").text(label))
+      .attr("aria-label", label + " — jump to the first comment")
+      .attr("data-total", String(total))
+      .prop("hidden", false);
+    // The first navigable target: the first thread of the first model bucket
+    // that maps to a current model (skip buckets whose model is not in the
+    // dropdown). Falls back to no-op navigation if none is anchorable.
+    $btn.off("click.cuteComments").on("click.cuteComments", function () {
+      navigateToFirstComment();
+    });
+  }
+
+  // A thread is a LIVE anchor candidate when it pins to a real new/old diff
+  // line (not outdated, has a line) — the navigation target the #420 button
+  // promises (an anchored diff line, never an outdated/unplaced tail entry).
+  // Visibility (folded-away) is resolved at injection time against the live
+  // DOM; here we pick by the payload's anchor facts.
+  function firstLiveThread(bucket) {
+    var threads = (bucket && bucket.threads) || [];
+    for (var j = 0; j < threads.length; j++) {
+      if (!threads[j].outdated && threads[j].line != null) { return threads[j]; }
+    }
+    return null;
+  }
+
+  // Navigate to the first anchored review thread: select its model in the
+  // dropdown (selectModelByName → renderForSelectedModel re-renders the
+  // Model-SQL diff and re-injects the threads), then scroll to that thread's
+  // anchored diff line. Scans for the FIRST bucket that BOTH maps to a
+  // selectable dropdown model AND has a live-anchored thread (CodeRabbit #439:
+  // a bucket whose model isn't selectable, or whose threads are all
+  // outdated/unplaced, must not stop the search). Falls back to the bucket's
+  // first thread only when the loop finds a selectable model with no live
+  // anchor (so the count button is never a dead click). In-page only (no
+  // fetch — the zero-egress contract).
+  function navigateToFirstComment() {
+    var pc = DATA && DATA.pr_comments;
+    if (!pc || !pc.by_model) { return; }
+    var fallback = null;
+    for (var i = 0; i < pc.by_model.length; i++) {
+      var bucket = pc.by_model[i];
+      var name = modelNameForBucket(bucket);
+      if (!name) { continue; }
+      var thread = firstLiveThread(bucket);
+      if (thread) {
+        selectModelByName(name);
+        scrollToThread(thread);
+        return;
+      }
+      // Remember the first selectable-but-anchorless bucket; only used if no
+      // bucket anywhere carries a live thread.
+      if (!fallback && bucket.threads && bucket.threads.length) {
+        fallback = { name: name, thread: bucket.threads[0] };
+      }
+    }
+    if (fallback) {
+      selectModelByName(fallback.name);
+      scrollToThread(fallback.thread);
+    }
+  }
+
+  // Scroll the injected comment thread (or its anchored diff line) into view.
+  // Defers a tick so the renderForSelectedModel re-render + injection settle.
+  // The thread is located by a NON-selector attribute scan (CodeRabbit #439):
+  // `thread.__key` is derived from the model path, which may contain `"`, `\`,
+  // or `]` — interpolating it into a CSS selector would break the query, so we
+  // compare `getAttribute("data-thread-key")` directly instead.
+  function scrollToThread(thread) {
+    setTimeout(function () {
+      var target = null;
+      if (thread && thread.__key) {
+        target = Array.prototype.find.call(
+          document.querySelectorAll(".pr-comment-thread[data-thread-key]"),
+          function (el) { return el.getAttribute("data-thread-key") === thread.__key; }
+        ) || null;
+      }
+      if (!target) {
+        target = document.querySelector(".model-sql .pr-comment-thread");
+      }
+      // Record the resolved navigation target's key so the headless test can
+      // assert the count button lands on the LIVE anchored thread (not an
+      // outdated/unplaced tail entry) — observability only, no behavior.
+      window.__cuteLastCommentNavKey = target
+        ? target.getAttribute("data-thread-key")
+        : null;
+      if (target && target.scrollIntoView) {
+        target.scrollIntoView({ behavior: "smooth", block: "start" });
+      }
+    }, 60);
+  }
+
+  // #421 — the per-model comment count, shown via the FOCUSABLE-BUBBLE
+  // tooltip (.expect-tooltip + .expect-tooltip-bubble): a focusable <button>
+  // whose CSS bubble paints on :hover AND :focus — NEVER a native `title`
+  // (hover-delayed, keyboard-unreachable, touch-invisible). aria-label
+  // carries the count for AT; the bubble is aria-hidden (never announced
+  // twice). Hidden when the selected model has no anchored comments.
+  function renderModelCommentCount(m) {
+    var $row = $('[data-testid="model-comments"]');
+    if (!$row.length) { return; }
+    $row.empty();
+    var bucket = prCommentBucketFor(m);
+    if (!bucket || !bucket.count) { $row.prop("hidden", true); return; }
+    var n = bucket.count;
+    var word = n === 1 ? "comment" : "comments";
+    var aria = n + " review " + word + " on this model";
+    var $btn = $("<button>").attr("type", "button")
+      .addClass("expect-tooltip model-comment-count-chip")
+      .attr("data-testid", "model-comment-count")
+      .attr("data-count", String(n))
+      .attr("aria-label", aria)
+      .text("💬 " + n + " " + word);
+    // The bubble: a per-thread summary (author of the first comment + a
+    // resolved/outdated marker), revealed on hover/focus.
+    var $bubble = $("<span>").addClass("expect-tooltip-bubble model-comment-bubble")
+      .attr("data-testid", "model-comment-bubble").attr("aria-hidden", "true");
+    bucket.threads.forEach(function (t) {
+      var who = (t.comments && t.comments[0] && t.comments[0].author) || "ghost";
+      var where = t.outdated ? "outdated"
+        : (t.line != null ? "line " + t.line : "—");
+      var stateMark = t.resolved ? " · resolved" : "";
+      $bubble.append(
+        $("<span>").addClass("model-comment-bubble-row")
+          .text("@" + who + " · " + where + stateMark)
+      );
+    });
+    $btn.append($bubble);
+    $row.append($btn).prop("hidden", false);
+  }
+
+  // Find the VISIBLE rendered diff line a thread anchors to (Right →
+  // data-newline, Left → data-oldline), or null when there is no live anchor
+  // (outdated, no line, no diff, or the only matching line is folded away /
+  // hidden). A `[hidden]` match is treated as NO anchor (CodeRabbit #439):
+  // injecting after an invisible line detaches the comment from its visible
+  // context, so such a thread is routed to the "not shown" tail instead.
+  function liveDiffAnchorFor(t, $diff) {
+    if (t.outdated || t.line == null || !$diff.length) { return null; }
+    var attr = (t.side === "Left") ? "data-oldline" : "data-newline";
+    // `:not([hidden])` excludes folded-away context lines, so a thread on a
+    // collapsed line falls through to `unplaced` (the tail) rather than being
+    // inserted after an invisible anchor.
+    var $line = $diff.find('.diff-line[' + attr + '="' + t.line + '"]:not([hidden])').first();
+    return ($line && $line.length) ? { $line: $line, key: attr + ":" + t.line } : null;
+  }
+
+  // #419 — inject each anchored review thread inline at its Model-SQL diff
+  // line. Runs AFTER renderModelSql has rebuilt the .model-sql diff for the
+  // selected model. A thread RESOLVED to a live, VISIBLE line (Right →
+  // data-newline, Left → data-oldline) is inserted right after that diff
+  // line; an OUTDATED thread (no live line), a thread whose line is folded
+  // away (hidden), and any thread whose anchored line is not in the rendered
+  // diff are appended to a per-model "outdated / unplaced comments" tail so
+  // reviewer context is never lost (the never-drop posture).
+  function anchorModelCommentThreads(m) {
+    var $section = $(".model-sql");
+    // Always clear a prior model's injected threads first (the
+    // renderModelSql rebuild contract — never leak across a model switch).
+    $section.find(".pr-comment-thread, .pr-comment-tail").remove();
+    var bucket = prCommentBucketFor(m);
+    if (!bucket || !bucket.threads.length) { return; }
+    var $diff = $section.find(".sql-diff-view");
+    var unplaced = [];
+    // Track the last thread injected at each anchor so multiple threads on the
+    // SAME diff line keep `bucket.threads` order (CodeRabbit #439: repeated
+    // `$line.after(...)` reverses them — insert after the previously injected
+    // thread, not the raw anchor, each time).
+    var lastInsertedByAnchor = Object.create(null);
+    bucket.threads.forEach(function (t, idx) {
+      // Give each thread a stable per-model key so the navigation scroll can
+      // target it (and the headless test can assert it).
+      t.__key = (m.path || m.name || "model") + "#" + idx;
+      var anchor = liveDiffAnchorFor(t, $diff);
+      if (anchor) {
+        var $thread = buildCommentThread(t);
+        var $insertAfter = lastInsertedByAnchor[anchor.key] || anchor.$line;
+        $insertAfter.after($thread);
+        lastInsertedByAnchor[anchor.key] = $thread;
+      } else {
+        unplaced.push(t);
+      }
+    });
+    if (unplaced.length) {
+      var $tail = $("<div>").addClass("pr-comment-tail").attr("data-testid", "pr-comment-tail");
+      $tail.append(
+        $("<p>").addClass("pr-comment-tail-label")
+          .text(unplaced.length === 1
+            ? "1 comment refers to a line that changed or is not shown:"
+            : unplaced.length + " comments refer to lines that changed or are not shown:")
+      );
+      unplaced.forEach(function (t) { $tail.append(buildCommentThread(t)); });
+      // Place the tail after the diff (or the section wrap) so it is visible.
+      var $wrap = $section.find(".model-sql-wrap");
+      ($wrap.length ? $wrap : $section).append($tail);
+    }
+  }
+
+  // Build one review thread's DOM — GitHub-style: a header (anchor/line +
+  // resolved/outdated badges) over the comment rows (author + body). All
+  // strings are set via .text()/textContent (NEVER innerHTML) so a
+  // manifest/PR-derived body is drawn as text, never parsed as HTML.
+  function buildCommentThread(t) {
+    var $thread = $("<div>").addClass("pr-comment-thread")
+      .attr("data-testid", "pr-comment-thread")
+      .attr("data-thread-key", t.__key || "");
+    if (t.resolved) { $thread.addClass("pr-comment-resolved"); }
+    if (t.outdated) { $thread.addClass("pr-comment-outdated"); }
+    var $head = $("<div>").addClass("pr-comment-thread-head");
+    var anchorText = t.outdated
+      ? (t.original_line != null ? "outdated · was line " + t.original_line : "outdated")
+      : (t.line != null ? "line " + t.line : "comment");
+    $head.append($("<span>").addClass("pr-comment-anchor").text(anchorText));
+    if (t.resolved) {
+      $head.append($("<span>").addClass("pr-comment-badge pr-comment-badge-resolved").text("resolved"));
+    }
+    if (t.outdated) {
+      $head.append($("<span>").addClass("pr-comment-badge pr-comment-badge-outdated").text("outdated"));
+    }
+    $thread.append($head);
+    // Resolved threads collapse: the body lives in a <details>, closed by
+    // default, so a resolved conversation is quiet but still inspectable.
+    var $body;
+    if (t.resolved) {
+      var $det = $("<details>").addClass("pr-comment-collapse");
+      $det.append($("<summary>").text(
+        (t.comments ? t.comments.length : 0) === 1
+          ? "1 comment (resolved)"
+          : (t.comments ? t.comments.length : 0) + " comments (resolved)"
+      ));
+      $body = $("<div>").addClass("pr-comment-body");
+      $det.append($body);
+      $thread.append($det);
+    } else {
+      $body = $("<div>").addClass("pr-comment-body");
+      $thread.append($body);
+    }
+    (t.comments || []).forEach(function (c) {
+      var $c = $("<div>").addClass("pr-comment");
+      $c.append($("<span>").addClass("pr-comment-author").text("@" + (c.author || "ghost")));
+      $c.append($("<div>").addClass("pr-comment-text").text(c.body || ""));
+      $body.append($c);
+    });
+    return $thread;
+  }
+
+  function renderModelAttribution(m) {
+    var $row = $('[data-testid="model-attribution"]').empty();
+    var list = (m && m.config_attributions) || [];
+    var vars = (m && m.var_references) || [];
+    if (!list.length && !vars.length) { $row.prop("hidden", true); return; }
+    list.forEach(function (a) {
+      $row.append(
+        $("<span>").addClass("config-attribution-chip")
+          .text("+" + a.key + " via dbt_project.yml · " + a.path)
+      );
+    });
+    vars.forEach(function (v) {
+      var how = v.tier === "macro" && v.via
+        ? "via " + v.via.split(".").pop()
+        : v.tier;
+      $row.append(
+        $("<span>").addClass("var-reference-chip")
+          .text("reads var '" + v.name + "' · " + how)
+      );
+    });
+    $row.prop("hidden", false);
+  }
+
+  function renderForSelectedModel() {
+    var m = currentModel();
+    var t = currentTest();
+    $(".recursive-banner").prop("hidden", !(m && m.is_recursive));
+    // cute-dbt#145 / cute-dbt#178 — the model-level incremental badge now
+    // lives in the Model-SQL code-card header, created per-render by
+    // renderModelSql (the static template span is gone), so no hidden-toggle
+    // is needed here.
+    // cute-dbt#145 — the .expected-panel .panel-header is a PERSISTENT static
+    // element. renderExpectedPanel (which clears + re-appends the mode badge /
+    // model pill) runs ONLY in the `if (t)` arm below, so a switch to a
+    // modified-but-untested model (currentTest() === null) would otherwise
+    // leak a prior incremental-mode test's badge + tooltip. Clear them here
+    // unconditionally too (targeted — never touches .expected-rowcount).
+    // (gemini review, PR #146.) cute-dbt#202 extends the #161 clear-list to
+    // .expected-model-badge; the expect-semantics bubble now rides INSIDE
+    // the mode badge (the retired ⓘ button needs no selector).
+    $(".expected-panel .panel-header .mode-badge, .expected-panel .panel-header .expected-model-badge").remove();
+    // cute-dbt#201 — a fresh model/test selection closes any open
+    // node-detail shelf BEFORE the DAG re-renders below, so the rebuilt
+    // DAG re-centers with no baked selection (the shelf's ✕ handler does
+    // its own renderDag; this path lets the one below suffice).
+    closeNodeShelf();
+    renderModelAxes(m);
+    renderModelAttribution(m);
+    // cute-dbt#421 — the per-model PR review-comment count tooltip (focusable
+    // bubble). No-op when the selected model carries no anchored comments.
+    renderModelCommentCount(m);
+    renderTestDetails(t);
+    renderModelSql(m);
+    // cute-dbt#419 — inject the anchored review threads inline at their
+    // Model-SQL diff lines (and an outdated/unplaced tail). MUST run AFTER
+    // renderModelSql rebuilds the diff for this model. No-op without comments.
+    anchorModelCommentThreads(m);
+    // cute-dbt#247 — the Model YAML section (peer of Model SQL): the
+    // model's authored schema-file `models:` entry, or its truthful
+    // degrade placeholder.
+    renderModelYaml(m);
+    // cute-dbt#170 — the coverage-checks panel is MODEL-scoped: it renders
+    // for the selected model in both scope-toggle views and regardless of
+    // whether a test is selected (findings are current-state facts).
+    renderFindingsPanel(m);
+    renderDagLegend();
+    renderDag();
+    if (t) {
+      renderLeftPanel();
+      renderExpectedPanel(t);
+    } else {
+      // (renderTestDetails(null) above already cleared the badge row, the
+      // description, and the details body — cute-dbt#201.)
+      // cute-dbt#91 — distinguish "model has tests but none are updated
+      // (Updated-only mode hides them)" from the genuine explorer-mode
+      // "model is modified but has zero unit tests" case.
+      var leftMsg = m && m.tests.length > 0
+        ? "No updated tests on this model — switch to All tests to inspect its "
+            + m.tests.length + (m.tests.length === 1 ? " test." : " tests.")
+        : "This model is modified but has no unit tests — consider adding one.";
+      $(".left-panel-body").empty().append(
+        $("<div>").addClass("no-tests-empty").text(leftMsg)
+      );
+      // cute-dbt#178 — the row-count badge is a PERSISTENT static element a
+      // prior render may have relocated into the fixture-view bar (inside
+      // .expected-body); return it to the header BEFORE the wipe so the
+      // .empty() below can never destroy it.
+      var $erc0 = $(".expected-panel .expected-rowcount");
+      $(".expected-panel .panel-header").append($erc0);
+      $erc0.text("0 rows");
+      $(".expected-body").empty().append(
+        $("<div>").addClass("no-tests-empty")
+          .text("No unit test selected.")
+      );
+    }
+    // Fresh diffs render default-folded; the per-diff fold toggles
+    // (buildFoldToggleBtn) are rebuilt with their headers each render, so
+    // they reset to "Expand all" naturally (cute-dbt#199 — the old
+    // top-of-report strip and its diffAllExpanded global are retired).
+  }
+
+  // cute-dbt#199 — per-diff global fold toggle. Lives in each diff's
+  // code-surface header (the old top-of-report "Expand all" strip was
+  // removed; expand/collapse over a file's hunks belongs with that file's
+  // diff affordances). getRoot() returns the diff <pre> whose folds this
+  // button drives via the same setAllFolds symmetric DOM mirror the
+  // __cuteExpandAllFolds hooks use.
+  //
+  // STATELESS by design (Gemini, PR #213): the click derives its intent
+  // from the DOM at activation time — any hidden folded line left in this
+  // diff means "expand all", else "collapse all". A cached boolean would
+  // desync the moment a hunk is stepped individually or the __cute hooks
+  // move fold state. The label/aria-pressed are owned by syncFoldToggles
+  // (driven from updateFoldControl after EVERY fold mutation), reading the
+  // root accessor stored on the element.
+  function buildFoldToggleBtn(getRoot) {
+    var $b = $("<button>").attr("type", "button").addClass("diff-fold-toggle")
+      .attr("aria-pressed", "false").text("Expand all");
+    $b[0].__cuteFoldRoot = getRoot;
+    $b.on("click", function (e) {
+      e.preventDefault();
+      var root = getRoot();
+      var anyHidden = $(root).find(".diff-folded").filter(function () { return this.hidden; }).length > 0;
+      // setAllFolds funnels through updateFoldControl -> syncFoldToggles,
+      // which relabels this button from the resulting DOM truth.
+      setAllFolds(anyHidden, root);
+    });
+    return $b;
+  }
+
+  // cute-dbt#201 — the always-open test card (founder decision on epic
+  // cute-dbt#197, 2026-06-10: NOT a drawer). The selector is the card's
+  // title; this populates the badge row (tag chips, the muted `untagged`
+  // fallback, the cute-dbt#202 overrides badge, meta chips), the
+  // larger-font description (hidden when empty: the #74
+  // no-empty-landmark intent carries over from the retired
+  // test-description-section), and the details body (defined-in fallback +
+  // the authoring-YAML drawer — the one piece that stays collapsible).
+  function renderTestDetails(t) {
+    var $badges = $(".test-badges").empty();
+    var desc = t && t.description ? t.description : "";
+    $(".test-description").text(desc).prop("hidden", !desc);
+    var $body = $(".test-details-body").empty();
+    if (!t) return;
+
+    // tag badges — chips replace the #178 "Tagged …"/"Untagged" text lines.
+    if (t.tags && t.tags.length) {
+      t.tags.forEach(function (tag) {
+        $badges.append($("<span>").addClass("tb-badge tb-tag").text(tag));
+      });
+    } else {
+      $badges.append($("<span>").addClass("tb-badge tb-muted").text("untagged"));
+    }
+    // cute-dbt#202 — `overrides · N` badge (N = total keys across the
+    // macros/vars/env_vars groups). Hover/focus shows the grouped
+    // key = value rows via the #ov-tooltip singleton (showOvTip); the
+    // badge's visible text is the AT surface. The payload field is the
+    // #200 TestPayload.overrides blob (native scalars, never on the wire
+    // when the test declares no override).
+    if (t.overrides && Object.keys(t.overrides).length) {
+      var ovCount = 0, ovJson = t.overrides;
+      Object.keys(ovJson).forEach(function (k) {
+        ovCount += ovJson[k] && typeof ovJson[k] === "object" ? Object.keys(ovJson[k]).length : 1;
+      });
+      $badges.append($("<span>").addClass("tb-badge tb-overrides has-ov-tip")
+        .attr("data-overrides", JSON.stringify(ovJson))
+        .attr("tabindex", "0").attr("role", "button")
+        .text("overrides · " + ovCount));
+    }
+    // meta chips — one chip per key (replaces the "No metadata" line; zero
+    // meta keys simply render zero chips).
+    var metaKeys = t.meta ? Object.keys(t.meta) : [];
+    metaKeys.forEach(function (k) {
+      $badges.append($("<span>").addClass("tb-badge tb-meta")
+        .text(k + ": " + String(t.meta[k])));
+    });
+
+    // cute-dbt#178 — the authoring-YAML drawer's code-card header now shows
+    // this path (GitHub-style). Keep this row ONLY as a fallback for tests
+    // with no authoring YAML (no drawer, no header), so the path is never
+    // lost.
+    if (t.defined_in && !t.authoring_yaml) {
+      $body.append(
+        $("<div>").addClass("td-line td-defined-in-line")
+          .append($("<span>").addClass("td-label").text("Defined in "))
+          .append($("<code>").addClass("td-defined-in").text(t.defined_in))
+      );
+    }
+    // cute-dbt#69 — Authoring YAML drawer. Source-of-truth: the raw
+    // unit_test block sliced by the domain layer (UnitTestYamlBlock),
+    // populated by the gather_authoring_yaml run-loop stage when a
+    // project root is resolvable AND the source file is readable.
+    // Absent → undefined → drawer omitted entirely (no empty-state
+    // copy: the panel above already surfaces tags/meta/defined-in).
+    if (t.authoring_yaml) {
+      // cute-dbt#96 concern 2 — when this test's own YAML block was edited
+      // in the diff, the renderer attaches a reconstructed inline diff
+      // (t.yaml_diff). The drawer then offers an Authored↔Diff toggle and
+      // defaults to the Diff view; absent a diff it shows the plain
+      // authored YAML exactly as cute-dbt#69 did.
+      var hasDiff = t.yaml_diff && t.yaml_diff.lines && t.yaml_diff.lines.length;
+      var $yamlDet = $("<details>").addClass("authoring-yaml").attr("open", "open");
+      // cute-dbt#247 — the drawer shows the SELECTED UNIT TEST's authored
+      // YAML block, so it is labeled "Unit test YAML" (the #233 D7
+      // "Model YAML" label was a spec naming error — that name now
+      // belongs to the model-level schema-entry section). No diff-variant
+      // suffix; the Diff/File toggle in the code header carries the diff
+      // affordance.
+      $yamlDet.append($("<summary>").text("Unit test YAML"));
+      var $yamlWrap = $("<div>").addClass("sql-block-wrap fixture-sql-block-wrap");
+      // cute-dbt#178 — GitHub-style code-card header: the file path sits left
+      // of the Diff/File toggle. `defined_in` is the test's authoring-YAML
+      // path (the manifest original_file_path).
+      var $yamlHeader = $("<div>").addClass("code-header");
+      if (t.defined_in) {
+        $yamlHeader.append($("<span>").addClass("code-filename")
+          .attr("title", t.defined_in).text(t.defined_in));
+      }
+      $yamlHeader.append($("<span>").addClass("code-header-spacer"));
+      if (hasDiff) {
+        var $authPre = $("<pre>").addClass("sql-block yaml-authored-view").prop("hidden", true).append(
+          $("<code>").html(highlightLinesYaml(t.authoring_yaml))
+        );
+        var $diffPre = $("<pre>").addClass("sql-block yaml-diff-view").html(
+          diffViewsHtml(t.yaml_diff, tokenizeYaml)
+        );
+        var $toggle = $("<div>").addClass("yaml-diff-toggle");
+        var $diffBtn = $("<button>").attr("type", "button")
+          .addClass("yaml-view-btn active").attr("data-view", "diff").text("Diff");
+        var $authBtn = $("<button>").attr("type", "button")
+          .addClass("yaml-view-btn").attr("data-view", "authored").text("File");
+        // cute-dbt#199 — per-diff fold toggle + copy-icon button ride the
+        // code header: fold toggle left of the view toggle, copy far right.
+        // PR #250 review (gemini): the fold toggle drives the DIFF pre's
+        // folds — its only meaningful target (the File view has no fold
+        // structure) — so it hides while the File view is active rather
+        // than mutating an invisible pre.
+        var $foldBtn = buildFoldToggleBtn(function () { return $diffPre[0]; });
+        $toggle.append($diffBtn).append($authBtn);
+        $toggle.on("click", ".yaml-view-btn", function () {
+          var view = $(this).attr("data-view");
+          $toggle.find(".yaml-view-btn").removeClass("active");
+          $(this).addClass("active");
+          $diffPre.prop("hidden", view !== "diff");
+          $authPre.prop("hidden", view !== "authored");
+          $foldBtn.prop("hidden", view !== "diff");
+        });
+        $yamlHeader.append($foldBtn);
+        $yamlHeader.append($toggle);
+        $yamlHeader.append(copyIconBtn(function () { return t.authoring_yaml; }));
+        $yamlWrap.append($yamlHeader).append($diffPre).append($authPre);
+      } else {
+        var $yamlPre = $("<pre>").addClass("sql-block").append(
+          $("<code>").html(highlightLinesYaml(t.authoring_yaml))
+        );
+        // The header is always emitted now (cute-dbt#199): it carries the
+        // copy-icon button even when there is no defined_in path to show.
+        $yamlHeader.append(copyIconBtn(function () { return t.authoring_yaml; }));
+        $yamlWrap.append($yamlHeader).append($yamlPre);
+      }
+      $yamlDet.append($yamlWrap);
+      $body.append($yamlDet);
+    }
+  }
+
+  // cute-dbt#47 / cute-dbt#111 — populate (or hide) the per-model Model SQL
+  // section. The askama template emits one static <section class="model-sql">;
+  // JS rebinds its body and Copy handler each time the user selects a
+  // different model. When `raw_sql` is missing, hide the whole section
+  // (defensive — dbt 1.8+ populates raw_code on every node).
+  //
+  // cute-dbt#111: when the selected model carries an inline SQL diff
+  // (m.sql_diff — PR-diff mode, the model's .sql changed), the section shows
+  // a Raw↔Diff toggle defaulting to Diff (rendered via renderBlockDiff with
+  // the SQL highlighter on context lines only). Absent a diff it renders the
+  // plain raw SQL exactly as cute-dbt#47 did. Copy always copies the raw SQL.
+  function renderModelSql(m) {
+    var $section = $(".model-sql");
+    var raw = m && m.raw_sql;
+    if (!raw) {
+      $section.hide();
+      return;
+    }
+    $section.show();
+    var hasDiff = m.sql_diff && m.sql_diff.lines && m.sql_diff.lines.length;
+    $section.find(".model-sql-summary-hint").text(hasDiff ? "diff" : "raw, with Jinja");
+    var $wrap = $section.find(".model-sql-wrap");
+    // Rebuild the wrap body each model switch so a prior model's toggle/diff
+    // never leaks. The copy affordance is the header's copy-icon button
+    // (cute-dbt#199) — the old absolutely-positioned text Copy is retired.
+    $wrap.empty();
+    // cute-dbt#178 — GitHub-style code-card header with the model's file
+    // path. cute-dbt#179: the payload carries the full project-relative
+    // `m.path` (`models/…/x.sql`); when the manifest had no
+    // original_file_path the fallback synthesizes `<name>.sql` (matches
+    // the DAG terminal-node label, cute-dbt#155).
+    var modelFile = (m && m.path) || ((m && m.name ? m.name : "model") + ".sql");
+    var $mHeader = $("<div>").addClass("code-header");
+    $mHeader.append($("<span>").addClass("code-filename").attr("title", modelFile).text(modelFile));
+    // cute-dbt#145 incremental badge — relocated here (cute-dbt#178), right
+    // of the file name. Created only when the model is incremental (the
+    // static template span is gone).
+    if (m && m.is_incremental) {
+      $mHeader.append($("<span>").addClass("incremental-badge").text("incremental"));
+    }
+    $mHeader.append($("<span>").addClass("code-header-spacer"));
+    if (hasDiff) {
+      var $diffPre = $("<pre>").addClass("sql-block model-sql-block sql-diff-view").html(
+        diffViewsHtml(m.sql_diff, tokenizeSql, "model-sql-code")
+      );
+      var $rawPre = $("<pre>").addClass("sql-block model-sql-block sql-raw-view").prop("hidden", true).append(
+        $("<code>").addClass("model-sql-code").html(highlightLinesSql(raw))
+      );
+      var $toggle = $("<div>").addClass("yaml-diff-toggle model-sql-toggle");
+      var $diffBtn = $("<button>").attr("type", "button")
+        .addClass("yaml-view-btn active").attr("data-view", "diff").text("Diff");
+      var $rawBtn = $("<button>").attr("type", "button")
+        .addClass("yaml-view-btn").attr("data-view", "raw").text("File");
+      // cute-dbt#199 — per-diff fold toggle + copy-icon button in the header.
+      // Copy always copies the raw SQL regardless of the active view.
+      // PR #250 review (gemini): the fold toggle hides while the File view
+      // is active — its only meaningful target is the diff pre's folds.
+      var $foldBtn = buildFoldToggleBtn(function () { return $diffPre[0]; });
+      $toggle.append($diffBtn).append($rawBtn);
+      $toggle.on("click", ".yaml-view-btn", function () {
+        var view = $(this).attr("data-view");
+        $toggle.find(".yaml-view-btn").removeClass("active");
+        $(this).addClass("active");
+        $diffPre.prop("hidden", view !== "diff");
+        $rawPre.prop("hidden", view !== "raw");
+        $foldBtn.prop("hidden", view !== "diff");
+      });
+      $mHeader.append($foldBtn);
+      $mHeader.append($toggle);
+      $mHeader.append(copyIconBtn(function () { return raw; }));
+      $wrap.append($mHeader).append($diffPre).append($rawPre);
+    } else {
+      $mHeader.append(copyIconBtn(function () { return raw; }));
+      var $pre = $("<pre>").addClass("sql-block model-sql-block").append(
+        $("<code>").addClass("model-sql-code").html(highlightLinesSql(raw))
+      );
+      $wrap.append($mHeader).append($pre);
+    }
+  }
+
+  // cute-dbt#247 — populate the per-model Model YAML section: the model's
+  // FULL authored schema-file `models:` entry (description, tags,
+  // model-level data tests, columns + column tests, meta), sliced by Rust
+  // from the file the manifest's patch_path names. Peer of Model SQL —
+  // same code-card idiom, same Diff/File toggle in --pr-diff mode
+  // (my.diff present), YAML highlighter on the File view.
+  //
+  // Honest degrade: when the block could not be surfaced, the payload
+  // carries my.missing (truthful copy naming exactly what is absent —
+  // composed in Rust, rendered verbatim here) and the section shows that
+  // placeholder instead of vanishing. Only a payload with NO model_yaml
+  // key at all (a render path that skipped the gather entirely) hides
+  // the section.
+  function renderModelYaml(m) {
+    var $section = $(".model-yaml");
+    var my = m && m.model_yaml;
+    if (!my) {
+      $section.hide();
+      return;
+    }
+    $section.show();
+    var $wrap = $section.find(".model-yaml-wrap");
+    // Rebuild the wrap body each model switch so a prior model's
+    // toggle/diff/placeholder never leaks (the renderModelSql contract).
+    $wrap.empty();
+    if (!my.raw) {
+      $section.find(".model-yaml-summary-hint").text("unavailable");
+      $wrap.append(
+        $("<p>").addClass("model-yaml-missing")
+          .text(my.missing || "The authored model YAML could not be surfaced.")
+      );
+      return;
+    }
+    var hasDiff = my.diff && my.diff.lines && my.diff.lines.length;
+    $section.find(".model-yaml-summary-hint").text(hasDiff ? "diff" : "authored schema entry");
+    // GitHub-style code-card header with the schema file path (the
+    // manifest patch_path, project-relative).
+    var $header = $("<div>").addClass("code-header");
+    if (my.path) {
+      $header.append($("<span>").addClass("code-filename").attr("title", my.path).text(my.path));
+    }
+    $header.append($("<span>").addClass("code-header-spacer"));
+    if (hasDiff) {
+      var $diffPre = $("<pre>").addClass("sql-block model-yaml-block yaml-diff-view").html(
+        diffViewsHtml(my.diff, tokenizeYaml, "model-yaml-code")
+      );
+      var $filePre = $("<pre>").addClass("sql-block model-yaml-block yaml-authored-view").prop("hidden", true).append(
+        $("<code>").addClass("model-yaml-code").html(highlightLinesYaml(my.raw))
+      );
+      var $toggle = $("<div>").addClass("yaml-diff-toggle model-yaml-toggle");
+      var $diffBtn = $("<button>").attr("type", "button")
+        .addClass("yaml-view-btn active").attr("data-view", "diff").text("Diff");
+      var $fileBtn = $("<button>").attr("type", "button")
+        .addClass("yaml-view-btn").attr("data-view", "file").text("File");
+      // cute-dbt#199 — per-diff fold toggle + copy-icon button in the
+      // header. Copy always copies the raw block regardless of view.
+      // PR #250 review (gemini): the fold toggle hides while the File view
+      // is active — its only meaningful target is the diff pre's folds.
+      var $foldBtn = buildFoldToggleBtn(function () { return $diffPre[0]; });
+      $toggle.append($diffBtn).append($fileBtn);
+      $toggle.on("click", ".yaml-view-btn", function () {
+        var view = $(this).attr("data-view");
+        $toggle.find(".yaml-view-btn").removeClass("active");
+        $(this).addClass("active");
+        $diffPre.prop("hidden", view !== "diff");
+        $filePre.prop("hidden", view !== "file");
+        $foldBtn.prop("hidden", view !== "diff");
+      });
+      $header.append($foldBtn);
+      $header.append($toggle);
+      $header.append(copyIconBtn(function () { return my.raw; }));
+      $wrap.append($header).append($diffPre).append($filePre);
+    } else {
+      $header.append(copyIconBtn(function () { return my.raw; }));
+      var $pre = $("<pre>").addClass("sql-block model-yaml-block").append(
+        $("<code>").addClass("model-yaml-code").html(highlightLinesYaml(my.raw))
+      );
+      $wrap.append($header).append($pre);
+    }
+  }
+
+  // cute-dbt#199 — copy-to-clipboard icon button (preserves the icon — a
+  // class flash, not a text swap). Used in the Model SQL + Model YAML code
+  // headers, far right. Inline SVG only (the README §2.4 functional-SVG
+  // iconography rule): no icon font, nothing external, so the zero-egress
+  // gate is untouched. A real focusable <button> with an aria-label (the
+  // #146 lesson — never a hover-only affordance).
+  //
+  // Outcome is TRUTHFUL (Gemini, PR #213): success flashes `copied`,
+  // failure flashes `copy-failed` — a rejected write never claims Copied.
+  // title AND aria-label both carry the outcome (so AT users get the same
+  // signal) and reset to the rest state after the flash. The write strategy
+  // mirrors copySql: navigator.clipboard.writeText first, then the shared
+  // execCommand fallbackCopy (which reports its own success) on rejection
+  // or where the async API is unavailable.
+  var COPY_SVG = '<svg class="icon" viewBox="0 0 24 24" aria-hidden="true">'
+    + '<rect x="9" y="9" width="11" height="11" rx="2"></rect>'
+    + '<path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg>';
+  function copyIconBtn(getText) {
+    var $b = $("<button>").attr("type", "button").addClass("code-copy-btn icon-btn")
+      .attr("aria-label", "Copy").attr("title", "Copy").html(COPY_SVG);
+    $b.on("click", function (e) {
+      e.preventDefault(); e.stopPropagation();
+      var done = function (ok) {
+        var msg = ok ? "Copied" : "Copy failed";
+        $b.addClass(ok ? "copied" : "copy-failed")
+          .attr("title", msg).attr("aria-label", msg);
+        setTimeout(function () {
+          $b.removeClass("copied copy-failed")
+            .attr("title", "Copy").attr("aria-label", "Copy");
+        }, 1200);
+      };
+      var text = getText();
+      if (navigator.clipboard && navigator.clipboard.writeText) {
+        navigator.clipboard.writeText(text).then(function () { done(true); }, function () {
+          fallbackCopy(text, done);
+        });
+      } else {
+        fallbackCopy(text, done);
+      }
+    });
+    return $b;
+  }
+
+  // cute-dbt#170 — the per-model coverage checklist panel ----------------
+  //
+  // Renders m.findings (Rust-computed FindingPayloads: the cute-dbt#169
+  // engine verdicts plus pin_node/sketches) and the payload-level
+  // check_specs catalog into the static .model-findings section. PURE
+  // rendering — no verdict/tier/suppression logic happens here. The trust
+  // model rules (epic cute-dbt#168): tiers are LABELED chips, never
+  // blended into a score; coverage is a three-valued checklist with
+  // counts, never a percentage; suppressed findings stay visible-but-quiet
+  // behind a collapsed count, never vanish.
+
+  var TIER_LABEL = { total: "TOTAL", high: "HIGH", advisory: "advisory" };
+  // Chip hover/focus bubble copy — mirrors the domain Tier docs.
+  var TIER_TIP = {
+    total: "TOTAL tier — deterministic over manifest facts; zero false positives by construction.",
+    high: "HIGH tier — high-confidence pattern match; rare false positives possible.",
+    advisory: "Advisory tier — heuristic advice; informational only."
+  };
+  var VERDICT_MARK = { covered: "✓", uncovered: "✕", unknown: "?" };
+  var INSTRUMENT_LABEL = { "unit-test": "unit test", "data-test": "data test", "both": "unit or data test" };
+
+  function renderFindingsPanel(m) {
+    var $body = $(".model-findings .findings-body").empty();
+    var findings = (m && m.findings) || [];
+    if (!findings.length) {
+      // Deliberate quiet empty state — the panel never hides.
+      $body.append($("<p>").addClass("findings-empty")
+        .attr("data-testid", "findings-empty")
+        .text("No coverage checks fired for this model."));
+      return;
+    }
+    var active = findings.filter(function (f) { return !f.suppressed; });
+    var suppressed = findings.filter(function (f) { return f.suppressed; });
+    var counts = { covered: 0, uncovered: 0, unknown: 0 };
+    active.forEach(function (f) {
+      var s = f.verdict && f.verdict.status;
+      // Own-property guard: a prototype name (`toString`, …) can never
+      // count — only the three verdict keys above tally.
+      if (Object.prototype.hasOwnProperty.call(counts, s)) counts[s] += 1;
+    });
+    var parts = [];
+    ["covered", "uncovered", "unknown"].forEach(function (s) {
+      if (counts[s]) parts.push(counts[s] + " " + s);
+    });
+    if (parts.length) {
+      $body.append($("<p>").addClass("findings-tally").text(parts.join(" · ")));
+    }
+    var $list = $("<ul>").addClass("findings-checklist");
+    active.forEach(function (f) { $list.append(buildFindingRow(f)); });
+    $body.append($list);
+    if (suppressed.length) {
+      // cute-dbt#171/#170 — visible-but-quiet: a collapsed count that
+      // reveals the acknowledged findings WITH their reasons on demand.
+      var $det = $("<details>").addClass("findings-suppressed")
+        .attr("data-testid", "findings-suppressed");
+      $det.append($("<summary>").text(
+        suppressed.length + " suppressed finding" + (suppressed.length === 1 ? "" : "s")
+      ));
+      var $slist = $("<ul>").addClass("findings-checklist findings-checklist-suppressed");
+      suppressed.forEach(function (f) { $slist.append(buildFindingRow(f)); });
+      $det.append($slist);
+      $body.append($det);
+    }
+  }
+
+  // One checklist row = one (construct, check) finding: a <details> whose
+  // summary is the three-valued checklist line (mark + verdict + check
+  // name + construct + tier chip) and whose body carries attribution,
+  // evidence, the DAG pin, the recommendation + copyable sketches, the
+  // suppression acknowledgement, and the inline rationale drawer.
+  function buildFindingRow(f) {
+    var spec = (DATA.check_specs && DATA.check_specs[f.check]) || {};
+    var status = (f.verdict && f.verdict.status) || "unknown";
+    var $li = $("<li>").addClass("finding-row verdict-" + status)
+      .attr("data-check", f.check)
+      .attr("data-construct", f.construct);
+    if (f.suppressed) $li.addClass("is-suppressed");
+    var $det = $("<details>").addClass("finding-details");
+    var $sum = $("<summary>").addClass("finding-summary");
+    $sum.append($("<span>").addClass("f-mark").attr("aria-hidden", "true")
+      .text(VERDICT_MARK[status] || "?"));
+    $sum.append($("<span>").addClass("f-verdict").text(status));
+    $sum.append($("<span>").addClass("f-name").text(spec.name || f.check));
+    $sum.append($("<code>").addClass("f-construct").text(f.construct));
+    // Tier chip — the #146/#188 tooltip contract: a focusable trigger
+    // with a CSS-positioned bubble on hover AND focus; aria-label is the
+    // AT surface; never a native title, never an info icon.
+    var tierTip = TIER_TIP[f.tier] || String(f.tier);
+    $sum.append($("<span>")
+      .addClass("tier-chip tier-" + f.tier + " has-finding-tip")
+      .attr("tabindex", "0")
+      .attr("aria-label", tierTip)
+      .attr("data-tip", tierTip)
+      .text(TIER_LABEL[f.tier] || f.tier));
+    // cute-dbt#259 — the degraded-backing chip: shown only when EVERY
+    // attributing test carries a weakening config (severity: warn, a
+    // where filter, or a limit cap), i.e. the construct has no
+    // full-strength backing at all. A partially degraded attribution
+    // (≥1 clean test) keeps the summary quiet — the per-test causes
+    // below carry the in-row honesty either way. Pure rendering: the
+    // domain computed `degraded` (causes per test); this only compares
+    // it against the attribution.
+    var degraded = f.degraded || [];
+    var coveredBy = (status === "covered" && f.verdict.by) || [];
+    if (degraded.length && coveredBy.length && degraded.length === coveredBy.length) {
+      var degTip = "Degraded backing — every test attributing this coverage carries a " +
+        "weakening config (severity: warn, a where row filter, or a limit cap). " +
+        "The construct is covered, but the guarantee is weaker than a default " +
+        "error-severity full-table test. Causes are enumerated below.";
+      $sum.append($("<span>")
+        .addClass("degraded-chip has-finding-tip")
+        .attr("tabindex", "0")
+        .attr("aria-label", degTip)
+        .attr("data-tip", degTip)
+        .text("degraded backing"));
+    }
+    if (f.suppressed) {
+      $sum.append($("<span>").addClass("suppress-chip")
+        .text("suppressed · " + f.suppressed.source));
+    }
+    $det.append($sum);
+
+    var $bd = $("<div>").addClass("finding-body");
+    if (f.suppressed) {
+      var why = f.suppressed.reason
+        ? "Acknowledged via " + f.suppressed.source + " — " + f.suppressed.reason
+        : "Acknowledged via " + f.suppressed.source + " (no reason given).";
+      $bd.append($("<p>").addClass("finding-suppress-reason").text(why));
+    }
+    if (status === "covered" && f.verdict.by && f.verdict.by.length) {
+      var $cov = $("<p>").addClass("finding-covered-by")
+        .append($("<span>").addClass("f-label").text("Covered by "));
+      f.verdict.by.forEach(function (id, i) {
+        if (i > 0) $cov.append(document.createTextNode(", "));
+        $cov.append($("<code>").text(id));
+      });
+      $bd.append($cov);
+    }
+    // cute-dbt#259 — per-test degraded-backing causes, rendered beside
+    // the attribution (in-row honesty: enumerated causes, never a
+    // silent downgrade). Rust composed the copy; this only lays it out.
+    if (degraded.length) {
+      var $degList = $("<ul>").addClass("finding-degraded");
+      degraded.forEach(function (d) {
+        var $dli = $("<li>").addClass("finding-degraded-test")
+          .append($("<code>").text(d.by));
+        (d.causes || []).forEach(function (cause) {
+          $dli.append($("<div>").addClass("f-degraded-cause").text(cause));
+        });
+        $degList.append($dli);
+      });
+      $bd.append($("<p>").addClass("finding-degraded-label")
+        .append($("<span>").addClass("f-label").text("Degraded backing")));
+      $bd.append($degList);
+    }
+    if (f.evidence && f.evidence.length) {
+      var $ev = $("<ul>").addClass("finding-evidence");
+      f.evidence.forEach(function (e) {
+        $ev.append($("<li>")
+          .append($("<span>").addClass("f-ev-label").text(e.label))
+          .append(document.createTextNode(": "))
+          .append($("<span>").addClass("f-ev-value").text(e.value)));
+      });
+      $bd.append($ev);
+    }
+    if (f.pin_node) {
+      // Evidence pinning — selects + scrolls to the construct in the DAG.
+      $bd.append($("<button>").attr("type", "button").addClass("finding-pin")
+        .attr("data-pin", f.pin_node)
+        .text("Show in DAG → " + f.pin_node));
+    }
+    if (f.recommendation) {
+      var instr = INSTRUMENT_LABEL[f.instrument];
+      $bd.append($("<p>").addClass("finding-recommendation")
+        .append($("<span>").addClass("f-label")
+          .text("Recommended fix" + (instr ? " (" + instr + ")" : "") + ": "))
+        .append(document.createTextNode(f.recommendation)));
+    }
+    // Copyable given-row YAML sketches (the cute-dbt#172 recommendation
+    // payload) — the #188 copy-button pattern on a yaml-highlighted block.
+    (f.sketches || []).forEach(function (sketch) {
+      var $wrap = $("<div>").addClass("sql-block-wrap finding-sketch");
+      var $copy = $("<button>").attr("type", "button").addClass("sql-copy").text("Copy")
+        .on("click", function (e) {
+          e.preventDefault();
+          e.stopPropagation();
+          copySql(sketch, $(this));
+        });
+      var $pre = $("<pre>").addClass("sql-block")
+        .append($("<code>").html(highlightLinesYaml(sketch)));
+      $wrap.append($copy).append($pre);
+      $bd.append($wrap);
+    });
+    $bd.append(buildFindingRationale(f, spec));
+    $det.append($bd);
+    return $li.append($det);
+  }
+
+  // The inline "what is this check?" drawer — fully offline: rationale,
+  // conditions, and exclusions all ride the payload's check_specs
+  // catalog. The book reference is a plain click-only outbound anchor
+  // (URL computed in Rust, carried on the payload; nothing here fetches).
+  function buildFindingRationale(f, spec) {
+    var $det = $("<details>").addClass("finding-rationale");
+    $det.append($("<summary>").text("What is this check?"));
+    var $bd = $("<div>").addClass("finding-rationale-body");
+    if (spec.rationale) {
+      $bd.append($("<p>").addClass("f-rationale").text(spec.rationale));
+    }
+    if (spec.conditions && spec.conditions.length) {
+      $bd.append($("<p>").addClass("f-spec-label").text("Fires when"));
+      var $c = $("<ul>").addClass("f-spec-list");
+      spec.conditions.forEach(function (s) { $c.append($("<li>").text(s)); });
+      $bd.append($c);
+    }
+    if (spec.exclusions && spec.exclusions.length) {
+      $bd.append($("<p>").addClass("f-spec-label").text("Deliberately silent on"));
+      var $x = $("<ul>").addClass("f-spec-list");
+      spec.exclusions.forEach(function (s) { $x.append($("<li>").text(s)); });
+      $bd.append($x);
+    }
+    if (spec.book_href) {
+      $bd.append($("<p>").addClass("f-book-link").append(
+        $("<a>").attr("href", spec.book_href)
+          .attr("target", "_blank")
+          .attr("rel", "noopener noreferrer")
+          .text("Full check reference: " + f.check + " (opens the cute-dbt book)")
+      ));
+    }
+    $det.append($bd);
+    return $det;
+  }
+
+  function renderDagLegend() {
+    var $roles = $(".dag-legend-roles").empty();
+    [
+      { role: "import",    label: ROLE_LABEL.import },
+      { role: "transform", label: ROLE_LABEL.transform },
+      { role: "final",     label: ROLE_LABEL.final }
+    ].forEach(function (r) {
+      $roles.append(
+        $("<span>").addClass("legend-item role-" + r.role)
+          .append($("<span>").addClass("legend-swatch"))
+          .append($("<span>").addClass("legend-label").text(r.label))
+      );
+    });
+
+    var $joins = $(".dag-legend-joins").empty();
+    ["from", "inner", "left", "right", "full", "cross", "union_all", "union_distinct"].forEach(function (j) {
+      var $edge = $("<span>").addClass("legend-edge");
+      if (j === "union_all" || j === "union_distinct") {
+        $edge.addClass("is-dashed").css({
+          background: "transparent",
+          "border-top": "3px dashed " + dagEdges()[j],
+          height: "0"
+        });
+      } else {
+        $edge.css("background", dagEdges()[j]);
+      }
+      $joins.append(
+        $("<span>").addClass("legend-item join-" + j)
+          .append($edge)
+          .append($("<span>").addClass("legend-label").text(j))
+      );
+    });
+  }
+
+  function buildMermaidSource(m) {
+    var lines = ["graph LR"];
+    var dag = m.dag;
+    if (!dag || !dag.nodes.length) {
+      lines.push("    empty[\"(no DAG available)\"]");
+      return lines.join("\n");
+    }
+    var safeIds = {};
+    dag.nodes.forEach(function (n, i) { safeIds[n.id] = "n" + i; });
+
+    dag.nodes.forEach(function (n) {
+      // Display the label (e.g. `orders.sql` for the terminal) when present;
+      // fall back to the stable id for CTE nodes. Node identity stays keyed
+      // by id everywhere else (cute-dbt#155).
+      var safe = String(n.label || n.id).replace(/"/g, "&quot;");
+      var shape;
+      if (n.role === "import") {
+        shape = "([\"" + safe + "\"])";
+      } else if (n.role === "final") {
+        shape = "{{\"" + safe + "\"}}";
+      } else {
+        shape = "[\"" + safe + "\"]";
+      }
+      lines.push("    " + safeIds[n.id] + shape + ":::role_" + n.role);
+    });
+    dag.edges.forEach(function (e) {
+      lines.push("    " + safeIds[e.from] + " --> " + safeIds[e.to]);
+    });
+
+    lines.push("    classDef role_import fill:#e8f1f8,stroke:#0072B2,stroke-width:1.5px,color:#1c1c1f;");
+    lines.push("    classDef role_transform fill:#f4f4f5,stroke:#8a8a90,stroke-width:1.5px,color:#1c1c1f;");
+    lines.push("    classDef role_final fill:#fdf2dc,stroke:#b07400,stroke-width:2px,color:#1c1c1f;");
+    lines.push("    classDef selected stroke:#E91E63,stroke-width:4px;");
+
+    dag.edges.forEach(function (e, i) {
+      var color = dagEdges()[e.edge_type] || "#8a8a90";
+      var dash = (e.edge_type === "union_all" || e.edge_type === "union_distinct") ? ",stroke-dasharray:5 3" : "";
+      lines.push("    linkStyle " + i + " stroke:" + color + ",stroke-width:1.8px,fill:none" + dash + ";");
+    });
+
+    if (state.selectedNodeId) {
+      var safeSel = safeIds[state.selectedNodeId];
+      if (safeSel) lines.push("    class " + safeSel + " selected;");
+    }
+    return lines.join("\n");
+  }
+
+  // cute-dbt#180 — the engine dispatcher. Mermaid is the static default;
+  // Cytoscape is the opt-in interactive engine (the settings-panel picker,
+  // persisted by theme.js). The flip happens IN PLACE: toggle the two host
+  // sections, tear down the inactive engine, build the chosen one. Full
+  // rebuilds run here only — model-switch, engine-switch, theme re-tint —
+  // never per node click (the spike's no-renderDag-per-click rule).
+  function renderDag() {
+    var useCyto = dagEngine === "cytoscape"
+      && typeof window.cytoscape === "function"
+      && window.CuteCyto;
+    $(".cte-dag-mermaid").toggleClass("is-hidden", !!useCyto);
+    $(".cte-dag-cyto").toggleClass("is-hidden", !useCyto);
+    if (useCyto) {
+      $(".cte-dag-mermaid").empty();
+      window.CuteCyto.render(currentModel(), state.selectedNodeId);
+    } else {
+      if (window.CuteCyto) window.CuteCyto.destroy();
+      renderMermaidDag();
+    }
+  }
+
+  function renderMermaidDag() {
+    var m = currentModel();
+    var $host = $(".cte-dag-mermaid").empty();
+    if (!m) return;
+    var src = buildMermaidSource(m);
+
+    var id = "mermaid-" + Math.random().toString(36).slice(2, 9);
+    window.mermaid.render(id, src).then(function (out) {
+      $host.html(out.svg);
+      if (out.bindFunctions) out.bindFunctions($host[0]);
+      bindDagNodeClicks($host[0], m, id);
+    }).catch(function (err) {
+      $host.empty().append(
+        $("<pre>").addClass("mermaid-error").text("Mermaid render failed: " + String(err))
+      );
+    });
+  }
+
+  function bindDagNodeClicks(svgHost, m, svgId) {
+    // Mermaid 11.14+ stamps each node group with the id
+    // "{svgElementId}-flowchart-{nodeName}-{counter}". Use the runtime
+    // svgId to anchor a precise selector — broken selectors are visible
+    // in DevTools (fail-loud), not silent.
+    var prefix = svgId + "-flowchart-";
+    var groups = svgHost.querySelectorAll('g.node[id^="' + prefix + '"]');
+    if (!groups.length) {
+      // Fallback substring selector — version-agnostic across Mermaid
+      // 11.14+ point releases. (The precise prefix above misses if
+      // Mermaid changes its internal id structure; the substring catches
+      // any "flowchart-" stamped id.)
+      groups = svgHost.querySelectorAll('g.node[id*="-flowchart-"]');
+    }
+    var safeToOriginal = {};
+    var safeToLabel = {};
+    m.dag.nodes.forEach(function (n, i) {
+      safeToOriginal["n" + i] = n.id;
+      safeToLabel["n" + i] = n.label || n.id;
+    });
+    groups.forEach(function (g) {
+      var raw = g.getAttribute("id") || "";
+      var stripped = raw.replace(prefix, "");
+      // Trailing "-{counter}" — find last hyphen-digit boundary.
+      var m2 = stripped.match(/^(.+?)-\d+$/);
+      if (!m2) return;
+      var safeId = m2[1];
+      var originalId = safeToOriginal[safeId];
+      if (!originalId) return;
+      g.classList.add("dag-node");
+      g.setAttribute("data-node-id", originalId);
+      // Keyboard accessibility (cute-dbt#41): tabindex makes the SVG
+      // group focusable; role="button" announces it to screen readers;
+      // Enter/Space activate via the same selection path as click.
+      g.setAttribute("tabindex", "0");
+      g.setAttribute("role", "button");
+      // Announce the visible label (e.g. `orders.sql`), not the internal id
+      // — they diverge for the terminal node (cute-dbt#155).
+      g.setAttribute("aria-label", "Inspect DAG node: " + (safeToLabel[safeId] || originalId));
+      g.style.cursor = "pointer";
+      function activate() {
+        // cute-dbt#201 — open the node-detail shelf, then re-render: the
+        // Mermaid selection ring is baked into the generated source, so
+        // the Mermaid path keeps its full re-render (Cytoscape's tap goes
+        // through __cuteSelectNode and never re-renders — the #180 rule).
+        openNodeShelf(originalId);
+        renderDag();
+      }
+      g.addEventListener("click", activate);
+      g.addEventListener("keydown", function (ev) {
+        if (ev.key === "Enter" || ev.key === " ") {
+          ev.preventDefault();
+          activate();
+        }
+      });
+    });
+    // Keyboard a11y (cute-dbt#41): activate() triggers a full SVG
+    // re-render, which detaches the previously focused <g>. Restore
+    // focus to the selected node so Tab+Enter chains feel continuous.
+    // `:focus-visible` reflects the originating input heuristic, so
+    // mouse activation does not produce a stuck outline here.
+    if (state.selectedNodeId) {
+      var escaped = String(state.selectedNodeId).replace(/[\\"]/g, "\\$&");
+      var sel = svgHost.querySelector('g.dag-node[data-node-id="' + escaped + '"]');
+      if (sel) sel.focus({ preventScroll: true });
+    }
+  }
+
+  // ===================================================================
+  // cute-dbt#429 / #430 / #431 — the tab-scoped change DAGs (epic #427).
+  //
+  // ONE engine-aware renderer family shared by the Models-tab PR-scope
+  // mini-DAG (#429) and the Macros-tab macro lineage DAG (#431). Both
+  // dispatch on the SAME persisted `dagEngine` pref the CTE DAG uses (the
+  // settings-panel Mermaid <-> Cytoscape picker, #180), and both build their
+  // OWN independent Cytoscape instances (the CTE DAG keeps its `CuteCyto`
+  // singleton; the tab DAGs each create + own a cy in `tabCyInstances`, keyed
+  // by host element). REPORT-PAGE Cytoscape contract: the first-party preset
+  // longest-path layout (CuteCyto.layoutPositions) — NEVER cytoscape-dagre
+  // (dagre is the explore page's layout only, AGENTS.md).
+  //
+  // Each DAG node carries a render-tier (`tierOf`): the mini-DAG uses the
+  // modified/connector/halo/deleted state taxonomy; the macro DAG uses the
+  // user/downstream role. The tier drives both engines' node styling AND the
+  // legend, kept 1:1 across Mermaid + Cytoscape (the CTE-DAG dual-engine rule).
+  // A node click selects that model in the report's #model-select (zero-egress,
+  // the mini-DAG click contract) — deleted ghosts are not selectable.
+  // ===================================================================
+
+  // Tier palettes (fill / stroke / dashed-border) for the tab DAGs. Light
+  // fills + dark labels read at AA on any theme (the CTE-DAG node posture);
+  // only structural dashing distinguishes the dimmed/ghost tiers. Mirrored 1:1
+  // between the Mermaid classDefs and the Cytoscape stylesheet below.
+  var TAB_DAG_TIERS = {
+    modified:   { fill: "#fdf2dc", stroke: "#b07400", w: 2.5, dash: null, round: true },
+    connector:  { fill: "#f4f4f5", stroke: "#8a8a90", w: 1.2, dash: null, round: false },
+    halo:       { fill: "#fafafb", stroke: "#b8b8be", w: 1.0, dash: "3 2", round: false },
+    deleted:    { fill: "#f7f7f8", stroke: "#b0392c", w: 1.5, dash: "5 3", round: false },
+    user:       { fill: "#fdf2dc", stroke: "#b07400", w: 2.5, dash: null, round: true },
+    downstream: { fill: "#f4f4f5", stroke: "#8a8a90", w: 1.2, dash: null, round: false }
+  };
+
+  // The render tier for a tab-DAG node. Mini-DAG nodes carry state +
+  // is_connector/is_halo; macro-DAG nodes carry a `role` string. The first
+  // matching predicate wins (connector/halo before deleted before modified —
+  // the Rust descriptor's branch order, kept in lockstep).
+  function tabDagTierOf(n) {
+    if (n.role) return n.role === "user" ? "user" : "downstream";
+    if (n.is_connector) return "connector";
+    if (n.is_halo) return "halo";
+    if (n.state === "deleted") return "deleted";
+    return "modified";
+  }
+
+  // Build a `graph LR` Mermaid source for a tab DAG `{nodes,edges}`. Labels
+  // are tier-classed; the mini-DAG appends the per-node ±lines chip. Node ids
+  // are mapped to safe `t<i>` handles (the CTE-DAG safeId idiom).
+  function buildTabDagMermaid(graph) {
+    var lines = ["graph LR"];
+    var safeIds = {};
+    graph.nodes.forEach(function (n, i) { safeIds[n.id] = "t" + i; });
+    graph.nodes.forEach(function (n) {
+      var label = String(n.name || n.id).replace(/"/g, "&quot;");
+      var a = n.lines_added || 0, r = n.lines_removed || 0;
+      if (a !== 0 || r !== 0) label += " (+" + a + "/-" + r + ")";
+      var tier = tabDagTierOf(n);
+      var t = TAB_DAG_TIERS[tier];
+      var shape = t.round ? '(["' + label + '"])' : '["' + label + '"]';
+      lines.push("    " + safeIds[n.id] + shape + ":::tabdag_" + tier);
+    });
+    graph.edges.forEach(function (e) {
+      var f = safeIds[e.from], tt = safeIds[e.to];
+      if (f && tt) lines.push("    " + f + " --> " + tt);
+    });
+    Object.keys(TAB_DAG_TIERS).forEach(function (tier) {
+      var t = TAB_DAG_TIERS[tier];
+      var dash = t.dash ? ",stroke-dasharray:" + t.dash : "";
+      lines.push("    classDef tabdag_" + tier + " fill:" + t.fill + ",stroke:" + t.stroke +
+        ",stroke-width:" + t.w + "px,color:#1c1c1f" + dash + ";");
+    });
+    return lines.join("\n");
+  }
+
+  // The independent Cytoscape instances for the tab DAGs — one per CANVAS
+  // element (NOT the CTE-DAG singleton). Tracked as {canvas, cy} pairs so the
+  // mini-DAG and the macro DAG can each be Cytoscape simultaneously without one
+  // tearing down the other (the panels are mutually exclusive in the UI, but
+  // the renderers run independently). `destroyTabCyFor(canvas)` tears down only
+  // that canvas's prior instance before a rebuild; the headless seam below
+  // exposes the live instances.
+  var tabCyInstances = [];
+  function destroyTabCyFor(canvas) {
+    tabCyInstances = tabCyInstances.filter(function (rec) {
+      if (rec.canvas === canvas) { try { rec.cy.destroy(); } catch (e) { /* ignore */ } return false; }
+      return true;
+    });
+  }
+
+  // The Cytoscape stylesheet for the tab DAGs — the tier fills/strokes above,
+  // plus the shared edge color (the palette hook's fallback edge). Canvas-text
+  // labels, system fonts, no workers (the report-page #180 contract).
+  function tabDagStyleSheet() {
+    var pal = window.cuteDagPalette ? window.cuteDagPalette() : { nodeText: "#1c1c1f", fallbackEdge: "#9aa0ab" };
+    var sheet = [
+      { selector: "node", style: {
+        "label": "data(label)",
+        "text-valign": "center",
+        "text-halign": "center",
+        "font-family": 'ui-monospace, SFMono-Regular, Menlo, Consolas, monospace',
+        "font-size": 12,
+        "color": pal.nodeText,
+        "text-wrap": "wrap",
+        "text-max-width": 150,
+        "width": "label",
+        "height": "label",
+        "padding": "9px",
+        "shape": "rectangle"
+      }},
+      { selector: "edge", style: {
+        "curve-style": "bezier",
+        "width": 1.6,
+        "target-arrow-shape": "triangle",
+        "line-color": pal.fallbackEdge,
+        "target-arrow-color": pal.fallbackEdge,
+        "arrow-scale": 1.0
+      }}
+    ];
+    Object.keys(TAB_DAG_TIERS).forEach(function (tier) {
+      var t = TAB_DAG_TIERS[tier];
+      var style = {
+        "background-color": t.fill,
+        "border-color": t.stroke,
+        "border-width": t.w,
+        "shape": t.round ? "round-rectangle" : "rectangle"
+      };
+      if (t.dash) {
+        style["border-style"] = "dashed";
+      }
+      sheet.push({ selector: 'node[tier = "' + tier + '"]', style: style });
+    });
+    return sheet;
+  }
+
+  // Render a tab DAG into `hostEl` with Cytoscape (an independent instance).
+  // `onSelect(id)` is the click handler (model selection). Uses the first-party
+  // preset layout — NO dagre.
+  function renderTabDagCyto(hostEl, graph, onSelect) {
+    if (typeof window.cytoscape !== "function" || !window.CuteCyto ||
+        typeof window.CuteCyto.layoutPositions !== "function") {
+      return false;
+    }
+    var canvas = hostEl.querySelector(".cyto-canvas");
+    if (!canvas) return false;
+    destroyTabCyFor(canvas); // tear down only THIS canvas's prior instance
+    var pos = window.CuteCyto.layoutPositions(graph.nodes, graph.edges || []);
+    var els = [];
+    graph.nodes.forEach(function (n) {
+      els.push({
+        group: "nodes",
+        data: { id: n.id, label: String(n.name || n.id), tier: tabDagTierOf(n) },
+        position: pos[n.id] || { x: 0, y: 0 }
+      });
+    });
+    (graph.edges || []).forEach(function (e, i) {
+      els.push({ group: "edges", data: { id: "te" + i, source: e.from, target: e.to } });
+    });
+    canvas.textContent = "";
+    var cy = window.cytoscape({
+      container: canvas,
+      elements: els,
+      style: tabDagStyleSheet(),
+      layout: { name: "preset", fit: true, padding: 24 },
+      minZoom: 0.35,
+      maxZoom: 2.2,
+      boxSelectionEnabled: false,
+      autoungrabify: true
+    });
+    cy.on("tap", "node", function (evt) {
+      var id = evt.target.id();
+      var n = graph.nodes.filter(function (g) { return g.id === id; })[0];
+      // Deleted ghosts are not selectable (the mini-DAG click contract).
+      if (n && n.state === "deleted") return;
+      if (typeof onSelect === "function") onSelect(n);
+    });
+    window.requestAnimationFrame(function () { try { cy.fit(24); } catch (e) { /* ignore */ } });
+    tabCyInstances.push({ canvas: canvas, cy: cy });
+    return true;
+  }
+
+  // Render a tab DAG into `hostEl` with Mermaid (the static default).
+  // `mermaidHost` is the .…-mermaid div; `cytoWrap` the .…-cyto wrapper.
+  // `onSelect(node)` is the model-selection click handler.
+  function renderTabDag(mermaidHost, cytoWrap, graph, onSelect) {
+    var useCyto = dagEngine === "cytoscape" &&
+      typeof window.cytoscape === "function" && window.CuteCyto;
+    if (mermaidHost) mermaidHost.classList.toggle("is-hidden", !!useCyto);
+    if (cytoWrap) cytoWrap.classList.toggle("is-hidden", !useCyto);
+    if (useCyto && cytoWrap) {
+      if (mermaidHost) mermaidHost.innerHTML = "";
+      var ok = renderTabDagCyto(cytoWrap, graph, onSelect);
+      if (ok) return;
+      // Cytoscape unavailable — fall back to Mermaid in place.
+      if (mermaidHost) mermaidHost.classList.remove("is-hidden");
+      if (cytoWrap) cytoWrap.classList.add("is-hidden");
+    }
+    // Mermaid path: tear down any prior Cytoscape instance for this host's
+    // canvas (a flip back to Mermaid, or a re-render with no engine change).
+    if (cytoWrap) {
+      var staleCanvas = cytoWrap.querySelector(".cyto-canvas");
+      if (staleCanvas) destroyTabCyFor(staleCanvas);
+    }
+    if (!mermaidHost || typeof window.mermaid === "undefined") return;
+    var src = buildTabDagMermaid(graph);
+    var id = "tabdag-" + Math.random().toString(36).slice(2, 9);
+    window.mermaid.render(id, src).then(function (out) {
+      mermaidHost.innerHTML = out.svg;
+      if (out.bindFunctions) out.bindFunctions(mermaidHost);
+      bindTabDagNodeClicks(mermaidHost, graph, id, onSelect);
+    }).catch(function (err) {
+      mermaidHost.textContent = "Mermaid render failed: " + String(err);
+    });
+  }
+
+  // Bind click/keyboard selection to the Mermaid-rendered tab-DAG node groups
+  // (the CTE-DAG bindDagNodeClicks idiom, model-selection variant).
+  function bindTabDagNodeClicks(svgHost, graph, svgId, onSelect) {
+    var prefix = svgId + "-flowchart-";
+    var groups = svgHost.querySelectorAll('g.node[id^="' + prefix + '"]');
+    if (!groups.length) groups = svgHost.querySelectorAll('g.node[id*="-flowchart-"]');
+    var safeToNode = {};
+    graph.nodes.forEach(function (n, i) { safeToNode["t" + i] = n; });
+    Array.prototype.forEach.call(groups, function (g) {
+      var raw = g.getAttribute("id") || "";
+      var m2 = raw.replace(prefix, "").match(/^(.+?)-\d+$/);
+      if (!m2) return;
+      var n = safeToNode[m2[1]];
+      if (!n) return;
+      g.classList.add("tab-dag-node");
+      g.setAttribute("data-model", n.name);
+      if (n.state === "deleted") {
+        g.setAttribute("aria-label", "Deleted model: " + n.name);
+        return;
+      }
+      g.setAttribute("tabindex", "0");
+      g.setAttribute("role", "button");
+      g.setAttribute("aria-label", "Select model: " + n.name);
+      g.style.cursor = "pointer";
+      function activate() { if (typeof onSelect === "function") onSelect(n); }
+      g.addEventListener("click", activate);
+      g.addEventListener("keydown", function (ev) {
+        if (ev.key === "Enter" || ev.key === " ") { ev.preventDefault(); activate(); }
+      });
+    });
+  }
+
+  // Select a model in the report's #model-select by bare name (the mini-DAG /
+  // macro-DAG node click contract — zero-egress, no fetch). No-op when the
+  // model is not an option (a downstream/halo context model the filter dropped,
+  // or a node not in scope). Scrolls the test selection into view on success.
+  function selectModelByName(name) {
+    var sel = document.getElementById("model-select");
+    if (!sel) return;
+    var has = false;
+    for (var i = 0; i < sel.options.length; i++) {
+      if (sel.options[i].value === name) { has = true; break; }
+    }
+    if (!has) return;
+    if (window.jQuery) {
+      window.jQuery(sel).val(name).trigger("change.cuteDbt");
+    } else {
+      sel.value = name;
+      sel.dispatchEvent(new Event("change"));
+    }
+    var anchor = document.querySelector(".test-selection");
+    if (anchor && anchor.scrollIntoView) {
+      anchor.scrollIntoView({ behavior: "smooth", block: "start" });
+    }
+  }
+
+  // ---- the Models-tab PR-scope mini-DAG (#429 / #430) ----------------
+
+  // The mini-DAG view (graph + descriptor counts + collapse flag) for the
+  // ACTIVE axis filter (#430). "all" reads the top-level payload; a specific
+  // axis reads the PRE-COMPUTED `by_axis[axis]` set (connectors + halo already
+  // derived in Rust — the JS only swaps, never re-derives topology). A payload
+  // predating #430 carries no `by_axis`, so a specific axis falls back to the
+  // top-level "all" view (graceful, byte-identity-safe).
+  function activeMiniDagView() {
+    var pd = DATA.pr_dag;
+    if (!pd) return null;
+    if (state.axisFilter !== "all" && pd.by_axis && pd.by_axis[state.axisFilter]) {
+      return pd.by_axis[state.axisFilter];
+    }
+    return pd;
+  }
+
+  // Render the Models-tab mini-DAG for the active axis filter + engine.
+  // Repaints the descriptor counts to the active view, renders (or collapses)
+  // the graph, and wires node clicks to the model selector. Safe to call with
+  // no pr_dag payload (no-op) and with zero in-scope models (the mini-DAG is
+  // a standalone overview).
+  function renderPrMiniDag() {
+    var panel = document.querySelector(".pr-minidag-panel");
+    if (!panel) return;
+    var view = activeMiniDagView();
+    if (!view) return;
+    // Descriptor: reflect the ACTIVE view's counts (the #430 filtered subset).
+    var desc = panel.querySelector(".pr-minidag-descriptor");
+    if (desc) {
+      desc.setAttribute("data-modified", view.modified_count);
+      desc.setAttribute("data-connectors", view.connector_count);
+      desc.setAttribute("data-halo", view.halo_count);
+      desc.setAttribute("data-deleted", view.deleted_count);
+    }
+    var mermaidHost = panel.querySelector(".pr-minidag-mermaid");
+    var cytoWrap = panel.querySelector(".pr-minidag-cyto");
+    var collapsed = panel.querySelector(".pr-minidag-collapsed");
+    var graph = view.graph || { nodes: [], edges: [] };
+    // Collapsed (over the Rust node cap) OR empty subset: show the summary /
+    // empty line, hide both engine hosts.
+    if (view.collapsed || !graph.nodes.length) {
+      if (mermaidHost) { mermaidHost.classList.add("is-hidden"); mermaidHost.innerHTML = ""; }
+      if (cytoWrap) {
+        cytoWrap.classList.add("is-hidden");
+        var emptyCanvas = cytoWrap.querySelector(".cyto-canvas");
+        if (emptyCanvas) destroyTabCyFor(emptyCanvas);
+      }
+      if (collapsed) {
+        collapsed.hidden = false;
+        collapsed.textContent = view.collapsed
+          ? ("PR scope spans " + graph.nodes.length + " models — too large to render inline. Use the model selector below to navigate.")
+          : "No models match this change-axis filter.";
+      }
+      return;
+    }
+    if (collapsed) collapsed.hidden = true;
+    renderTabDag(mermaidHost, cytoWrap, graph, function (n) {
+      if (n && n.name) selectModelByName(n.name);
+    });
+  }
+
+  // ---- the Macros-tab macro lineage DAG (#431) ----------------------
+
+  // The macro_dag for the currently-visible macro (#424 picker). The selector
+  // reveals exactly one `.macro-lens-macro` section; we read its data-macro and
+  // match it against DATA.macro_lens.macros[i].macro_dag. Returns null when no
+  // macro lens / no visible macro / empty DAG.
+  function activeMacroDag() {
+    var ml = DATA.macro_lens;
+    if (!ml || !ml.macros || !ml.macros.length) return null;
+    var sel = document.querySelector(".macro-select");
+    var name = sel ? sel.value : null;
+    if (!name) {
+      // No picker (degenerate) — the first macro is the default-visible one.
+      name = ml.macros[0].name;
+    }
+    var mac = ml.macros.filter(function (m) { return m.name === name; })[0];
+    if (!mac || !mac.macro_dag || !mac.macro_dag.nodes || !mac.macro_dag.nodes.length) return null;
+    return { name: name, dag: mac.macro_dag };
+  }
+
+  // Render the Macros-tab macro DAG for the selected macro + active engine.
+  // Hosts are per-macro (keyed by data-macro), so only the visible macro's
+  // host is painted; the others stay empty static markup (zero bytes of cy).
+  function renderMacroDag() {
+    var active = activeMacroDag();
+    if (!active) return;
+    var host = document.querySelector('.macro-lens-dag-panel[data-macro="' +
+      String(active.name).replace(/["\\]/g, "\\$&") + '"]');
+    if (!host) return;
+    var mermaidHost = host.querySelector(".macro-lens-dag-mermaid");
+    var cytoWrap = host.querySelector(".macro-lens-dag-cyto");
+    renderTabDag(mermaidHost, cytoWrap, active.dag, function (n) {
+      if (n && n.name) selectModelByName(n.name);
+    });
+  }
+
+  // Re-render every visible tab DAG (mini-DAG + macro DAG) — the hook the
+  // engine-switch / theme re-tint / axis-filter / macro-pick paths call.
+  function renderTabDags() {
+    renderPrMiniDag();
+    renderMacroDag();
+  }
+
+  // cute-dbt#429/#430/#431 — the headless-test introspection seam for the
+  // tab-scoped DAGs (the CuteCyto.cyInstance() precedent). `cyForCanvas`
+  // returns the live Cytoscape instance painted into a given `.cyto-canvas`
+  // element (null when none / Mermaid is active); `count` is the live tab-cy
+  // instance count. Lets the headless suite assert both engines render + the
+  // mini-DAG re-scopes on a filter change without coupling to internals.
+  window.CuteTabDags = {
+    cyForCanvas: function (canvas) {
+      for (var i = 0; i < tabCyInstances.length; i++) {
+        if (tabCyInstances[i].canvas === canvas) return tabCyInstances[i].cy;
+      }
+      return null;
+    },
+    count: function () { return tabCyInstances.length; }
+  };
+
+  // cute-dbt#430/#431 — wire the tab-DAG re-render to the affordances that
+  // re-scope them: the #414 axis filter (mini-DAG → the pre-computed per-axis
+  // subgraph) and the #424 macro picker (macro DAG → the picked macro's
+  // lineage) + lens-tab activation (a freshly-revealed tab's Cytoscape canvas
+  // may have been 0-sized while hidden, so a re-render re-fits it). All
+  // DELEGATED on `document` so they survive the JS rebuilds of the filter
+  // buttons / the macro <select> sections (and a macro-only PR, where the
+  // model-bearing handlers never bind). state.axisFilter is updated by the
+  // existing `.axis-filter` handler in bindGlobalHandlers BEFORE this fires
+  // (jQuery preserves bind order), so the mini-DAG reads the new filter.
+  function bindTabDagHandlers() {
+    // The #424 macro picker re-targets the Macros-tab DAG to the picked
+    // macro's lineage. Delegated so it survives the gated macro <select>
+    // markup + binds in the macro-only path (no model-bearing handlers).
+    $(document).on("change", ".macro-select", function () {
+      // Defer one tick so the gated inline macro-picker script (which unhides
+      // the picked macro's `.macro-lens-macro` section) runs first — Mermaid
+      // must render into the now-VISIBLE section, not the still-hidden one.
+      window.setTimeout(renderMacroDag, 0);
+    });
+    // A newly-shown Cytoscape canvas needs a re-fit (it was 0-sized while the
+    // lens panel was hidden). Defer one tick so the panel is `hidden=false`
+    // first (bindLensTabs flips it on the same click).
+    $(document).on("click", ".lens-tab", function () {
+      window.setTimeout(renderTabDags, 0);
+    });
+    // cute-dbt#404/#429 — the settings-panel viewer toggle (show/hide the
+    // PR-scope mini-DAG panel). Server-rendered in the settings panel, gated
+    // by has_pr_dag; the panel now lives in the Models lens (relocated #429),
+    // but the toggle's show/hide contract is unchanged.
+    var prdagToggle = document.getElementById("settings-prdag-input");
+    var prdagPanel = document.querySelector(".pr-minidag-panel");
+    if (prdagToggle && prdagPanel) {
+      prdagToggle.addEventListener("change", function () {
+        prdagPanel.hidden = !prdagToggle.checked;
+      });
+    }
+  }
+
+  // cute-dbt#91 — sync the Updated-only ↔ All-tests toggle's active state
+  // to state.showAll. (renderSegmentedToggle is retired with the
+  // Inspect/All-inputs panel toggle — cute-dbt#201.)
+  function renderTestModeToggle() {
+    var active = state.showAll ? "all" : "updated";
+    $(".test-mode-toggle [data-test-mode]").each(function () {
+      var mode = $(this).attr("data-test-mode");
+      $(this).attr("aria-pressed", mode === active ? "true" : "false");
+      $(this).toggleClass("is-active", mode === active);
+    });
+  }
+
+  function bindGlobalHandlers() {
+    // cute-dbt#91 — global Updated-only ↔ All-tests toggle. state.showAll
+    // is global and persists across model switches (the model-change
+    // handler never resets it). On flip, keep the selection valid: if the
+    // currently selected test is no longer visible, fall back to the first
+    // visible test (or null). (renderForSelectedModel closes any open node
+    // shelf — cute-dbt#201.)
+    $(".test-mode-toggle").on("click", "[data-test-mode]", function () {
+      var nextShowAll = $(this).attr("data-test-mode") === "all";
+      if (nextShowAll === state.showAll) return;
+      state.showAll = nextShowAll;
+      var vis = visibleTests(currentModel());
+      var stillVisible = vis.some(function (t) { return t.id === state.selectedTestId; });
+      if (!stillVisible) state.selectedTestId = vis.length ? vis[0].id : null;
+      renderTestModeToggle();
+      renderModelSelector();
+      renderTestSelector();
+      renderForSelectedModel();
+    });
+    // cute-dbt#414 (Slice C) — the single-select 3-axis filter. Re-scopes
+    // the model <select> to the models whose chosen change-axis fired
+    // ("all" restores every in-scope model). Pure client-side: flips
+    // state.axisFilter, repaints the segments + re-builds the dropdown
+    // (renderModelSelector reads filteredModels() + handles the
+    // selected-model fallback when the active model is filtered out), then
+    // re-renders the test selector + the selected model's content. A click
+    // on the already-active segment is a no-op. Delegated on .axis-filter so
+    // it survives the JS rebuild of its buttons.
+    $(".axis-filter").on("click", "[data-axis]", function () {
+      var next = $(this).attr("data-axis");
+      if (next === state.axisFilter) return;
+      state.axisFilter = next;
+      renderAxisFilter();
+      renderModelSelector();
+      renderTestSelector();
+      renderForSelectedModel();
+      // cute-dbt#430 — re-scope the Models-tab mini-DAG to the PRE-COMPUTED
+      // per-axis subgraph (connectors + halo already derived in Rust). Bound
+      // here (in the with-models path) so it runs synchronously right after
+      // state.axisFilter flips; the macro-only path relies on the delegated
+      // bindTabDagHandlers fallback.
+      renderPrMiniDag();
+    });
+    // cute-dbt#132 / cute-dbt#199 — hunk-fold reveal. ONE delegated listener
+    // set (diff blocks are .empty()'d and rebuilt on model/test switch, so
+    // delegate on `document` to survive re-renders). Reveal is PARENT-SCOPED:
+    // closest("code, tbody") isolates `.fold-<id>` to the SAME unified <code>
+    // block or split <tbody>, so the call-local fold ids never collide across
+    // the YAML drawer vs the model-SQL block. Directional step-expansion
+    // (#199, supersedes the #136 click-toggles-all): activating the band (or
+    // the gutter `+` stepper) reveals settings.expandStep lines toward the
+    // hunk; `−` re-hides a step; the far-right "collapse all" restores the
+    // fully-folded state. Clicks originating on the explicit controls must
+    // not double-fire the band (the closest() guard).
+    $(document).on("click", ".diff-fold", function (e) {
+      // explicit controls (steppers / collapse-all) handle their own clicks.
+      if ($(e.target).closest(".fold-expand, .fold-contract, .fold-collapse-all").length) return;
+      expandFold($(this));   // clicking the band = expand by one step
+    });
+    $(document).on("click", ".fold-expand", function (e) {
+      e.stopPropagation();
+      expandFold($(this).closest(".diff-fold"));
+    });
+    $(document).on("click", ".fold-contract", function (e) {
+      e.stopPropagation();
+      contractFold($(this).closest(".diff-fold"));
+    });
+    $(document).on("click", ".fold-collapse-all", function (e) {
+      e.stopPropagation();
+      collapseFold($(this).closest(".diff-fold"));
+    });
+    // cute-dbt#170 — evidence pinning: select the cited construct's DAG
+    // node (the same selection path a node click takes), smooth-scroll
+    // the DAG into view, and flash the section briefly. Delegated on
+    // `document` because finding rows are rebuilt per model switch.
+    $(document).on("click", ".finding-pin", function () {
+      var id = $(this).attr("data-pin");
+      if (!id || !currentModel()) return;
+      // cute-dbt#180 contract: per-click Cytoscape interaction mutates
+      // classes IN PLACE — never a renderDag() rebuild (which would
+      // reset pan/zoom). Emitting a tap on the node runs the exact
+      // bound click path (lineage highlight + __cuteSelectNode → the
+      // node-detail shelf, cute-dbt#201). Mermaid keeps its
+      // render-driven selection.
+      var cyto = dagEngine === "cytoscape" && window.CuteCyto
+        ? window.CuteCyto.cyInstance()
+        : null;
+      var cyEle = cyto ? cyto.getElementById(String(id)) : null;
+      if (cyEle && cyEle.length) {
+        cyEle.emit("tap");
+      } else {
+        openNodeShelf(id);
+        renderDag();
+      }
+      var dag = document.querySelector(".cte-dag");
+      if (dag) {
+        if (dag.scrollIntoView) dag.scrollIntoView({ behavior: "smooth", block: "start" });
+        // One flash at a time: a rapid re-pin clears the pending
+        // removal so an old timer can't cut the new animation short.
+        if (dag.__cutePinFlashTimer) clearTimeout(dag.__cutePinFlashTimer);
+        dag.classList.remove("pin-flash");
+        void dag.offsetWidth; // restart the CSS flash animation
+        dag.classList.add("pin-flash");
+        dag.__cutePinFlashTimer = setTimeout(function () {
+          dag.classList.remove("pin-flash");
+          dag.__cutePinFlashTimer = null;
+        }, 1600);
+      }
+    });
+    $(document).on("keydown", ".diff-fold", function (e) {
+      // Enter or Space activates the band (expand-by-step); preventDefault
+      // on Space stops page scroll.
+      if (e.key === "Enter" || e.key === " " || e.key === "Spacebar") {
+        e.preventDefault();
+        expandFold($(this));
+      }
+    });
+  }
+
+  // ---- manifest helpers (model details from DATA.manifest_nodes) ---------
+  // cute-dbt#201 — the node-detail shelf's model card reads the #200
+  // manifest_nodes lookup. All graceful: a model absent from the lookup
+  // simply renders no card (compiled SQL still shows).
+  function refTargetName(input) {
+    var mm = /^ref\(['"](.+)['"]\)$/.exec(input || "");
+    return mm ? mm[1] : null;
+  }
+  function manifestNode(name) {
+    return (DATA.manifest_nodes && DATA.manifest_nodes[name]) || null;
+  }
+  // The manifest model a DAG node maps to: the final-select node => this
+  // model; an import node => the ref() target of its bound given (the #131
+  // bound_to_node seam); anything else only when the node id itself names a
+  // known model. Null => no card.
+  function nodeManifestName(node, t, m) {
+    if (!node) return null;
+    if (node.role === "final") return m ? m.name : null;
+    if (node.role === "import") {
+      var g = ((t && t.given) || []).filter(function (x) { return x.bound_to_node === node.id; })[0];
+      if (g) {
+        var r = refTargetName(g.input);
+        if (r && manifestNode(r)) return r;
+      }
+    }
+    return manifestNode(node.id) ? node.id : null;
+  }
+  // Structured-test chips: accent keyword + one value chip per argument —
+  // the #166/#189 ColumnTestPayload display shape ({name, values?,
+  // detail?}), reused for the shelf card's model-level + column tests.
+  function testChipsHtml(tt) {
+    var h = '<span class="dt-key">' + escapeHtml(tt.name) + "</span>";
+    if (tt.values && tt.values.length) {
+      h += '<span class="dt-vals">'
+        + tt.values.map(function (v) { return '<span class="dt-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (tt.detail) {
+      // split a multi-arg detail ("field: x, datepart: day, interval: 2")
+      // into one chip per argument.
+      h += '<span class="dt-vals">'
+        + String(tt.detail).split(/,\s*/).map(function (a) {
+            return '<span class="dt-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return h;
+  }
+  // The shelf's model-detail card: tags → name + materialized →
+  // description → model-level tests → columns (each with a
+  // click-to-expand test-count chip). Built from manifest_nodes[name].
+  function buildModelDetailCard(name) {
+    var info = manifestNode(name);
+    var $c = $("<div>").addClass("model-detail-card");
+    // tags first (above the name), then the name + materialized, then desc.
+    if (info && info.tags && info.tags.length) {
+      var $tg = $("<div>").addClass("mdc-tags");
+      info.tags.forEach(function (tag) { $tg.append($("<span>").addClass("mdc-tag").text(tag)); });
+      $c.append($tg);
+    }
+    var $h = $("<div>").addClass("mdc-head");
+    $h.append($("<code>").addClass("mdc-name").text(name));
+    if (info && info.materialized) $h.append($("<span>").addClass("mdc-mat").text(info.materialized));
+    $c.append($h);
+    if (info && info.description) $c.append($("<p>").addClass("mdc-desc").text(info.description));
+    // model-level data tests (below the description, above the columns).
+    if (info && info.model_tests && info.model_tests.length) {
+      var $mt = $("<div>").addClass("mdc-mtests");
+      $mt.append($("<div>").addClass("mdc-section-label")
+        .text("Model tests · " + info.model_tests.length));
+      var $mul = $("<ul>").addClass("mdc-mtest-list");
+      info.model_tests.forEach(function (tt) {
+        $mul.append($("<li>").addClass("dt-row").html(testChipsHtml(tt)));
+      });
+      $mt.append($mul);
+      $c.append($mt);
+    }
+    if (info && info.columns && info.columns.length) {
+      $c.append($("<div>").addClass("mdc-section-label").text("Columns · " + info.columns.length));
+      var $cols = $("<ul>").addClass("mdc-cols");
+      info.columns.forEach(function (col) {
+        var $li = $("<li>");
+        var $nrow = $("<div>").addClass("mdc-col-nrow");
+        $nrow.append($("<code>").addClass("mdc-col-name").text(col.name));
+        if (col.type) $nrow.append($("<span>").addClass("mdc-col-type").text(col.type));
+        // test-count chip — click to expand this column's tests.
+        var hasTests = col.tests && col.tests.length;
+        if (hasTests) {
+          $nrow.append($("<button>").attr("type", "button")
+            .addClass("mdc-col-testcount").attr("aria-expanded", "false")
+            .attr("aria-label", col.tests.length + " data test" + (col.tests.length === 1 ? "" : "s"))
+            .text(col.tests.length));
+        }
+        $li.append($nrow);
+        if (col.description) $li.append($("<div>").addClass("mdc-col-desc").text(col.description));
+        if (hasTests) {
+          var $tl = $("<ul>").addClass("mdc-col-tests").prop("hidden", true);
+          col.tests.forEach(function (tt) {
+            $tl.append($("<li>").addClass("dt-row").html(testChipsHtml(tt)));
+          });
+          $li.append($tl);
+        }
+        $cols.append($li);
+      });
+      $c.append($cols);
+    }
+    return $c;
+  }
+
+  // ---- node-detail shelf (slides out beside the DAG; cute-dbt#201) -------
+  function openNodeShelf(nodeId) {
+    state.selectedNodeId = nodeId;
+    $(".dag-shelf").prop("hidden", false);
+    $(".dag-stage").addClass("shelf-open");
+    renderNodeDetail(currentTest());
+  }
+  // Hide + clear the shelf. Pure state/DOM reset — the DAG re-render is
+  // the CALLER's choice: the ✕ handler re-renders (re-centers, clears the
+  // baked Mermaid selection) while renderForSelectedModel calls this just
+  // before its own renderDag() so a fresh selection never double-renders.
+  function closeNodeShelf() {
+    state.selectedNodeId = null;
+    $(".dag-shelf").prop("hidden", true);
+    $(".dag-stage").removeClass("shelf-open");
+    $(".dag-shelf-body").empty();
+  }
+  function bindShelf() {
+    $(document).on("click", ".dag-shelf-close", function () {
+      closeNodeShelf();
+      renderDag(); // ✕ re-centers the DAG (the cute-dbt#201 close contract)
+    });
+    // expand/collapse a column's tests in the shelf model-detail card.
+    $(document).on("click", ".mdc-col-testcount", function () {
+      var $btn = $(this);
+      var open = $btn.attr("aria-expanded") === "true";
+      $btn.attr("aria-expanded", open ? "false" : "true");
+      $btn.closest("li").find(".mdc-col-tests").prop("hidden", open);
+    });
+    // cute-dbt#446 (S3, code→DAG) — click a line in the source-map pane to
+    // select the DAG node whose compiled span owns that line. Routes through
+    // the #180 in-place selection seam (a Cytoscape `tap` when live, else
+    // openNodeShelf) — never a renderDag rebuild that would reset pan/zoom.
+    // Delegated on `document` because the shelf body is rebuilt per selection.
+    $(document).on("click", ".code-map-pre .code-line", function () {
+      var line = parseInt(this.getAttribute("data-line"), 10);
+      if (!line) return;
+      var m = currentModel();
+      var owner = nodeOwningLine(m, line);
+      if (!owner) return;
+      selectNodeInPlace(owner);
+    });
+  }
+
+  // cute-dbt#446 (S3) — select a DAG node through the #180 in-place seam: emit
+  // a Cytoscape `tap` when the live instance has the node (runs the bound
+  // click path: lineage highlight + the node-detail shelf), else fall back to
+  // openNodeShelf. Shared by the code→DAG line click and any other in-place
+  // selector. Never calls renderDag (which would reset pan/zoom — the #180
+  // rule).
+  function selectNodeInPlace(id) {
+    var cyto = dagEngine === "cytoscape" && window.CuteCyto
+      ? window.CuteCyto.cyInstance()
+      : null;
+    var cyEle = cyto ? cyto.getElementById(String(id)) : null;
+    if (cyEle && cyEle.length) {
+      cyEle.emit("tap");
+    } else {
+      openNodeShelf(id);
+    }
+  }
+
+  // cute-dbt#402 (epic #360) — the subject-lens tab strip. Implements the
+  // WAI-ARIA tablist keyboard pattern: clicking OR keyboard-navigating a tab
+  // flips which lens panel is visible via a pure in-place class + `hidden` +
+  // aria + roving-`tabindex` toggle — never a fetch or a re-render (the
+  // zero-egress posture, no `src`). Roving tabindex: exactly one tab is in
+  // the Tab order (`tabindex="0"`, the active one); the rest are `-1` and
+  // reached with the Arrow/Home/End keys. The Models lens (today's whole
+  // report) is the active panel by default. Kept deliberately separate from
+  // bindShelf (the DAG node-detail handler) — territory partition with
+  // cute-dbt#398's shelf work.
+  function bindLensTabs() {
+    var $tabs = $(".lens-tab");
+    if (!$tabs.length) return;
+    function activate(lens) {
+      $tabs.each(function () {
+        // Roving tabindex: the active tab is the single Tab-order stop; the
+        // others are programmatically focusable only (Arrow/Home/End).
+        var on = $(this).attr("data-lens") === lens;
+        $(this)
+          .toggleClass("is-active", on)
+          .attr("aria-selected", on ? "true" : "false")
+          .attr("tabindex", on ? "0" : "-1");
+      });
+      $(".lens-panel").each(function () {
+        var on = $(this).attr("data-lens") === lens;
+        $(this).toggleClass("is-active", on).prop("hidden", !on);
+      });
+    }
+    $(document).on("click", ".lens-tab", function () {
+      activate($(this).attr("data-lens"));
+    });
+    // WAI-ARIA tablist arrow-key navigation (wrapping) + Home/End. Moving to
+    // a tab focuses AND activates it (the automatic-activation tablist
+    // pattern, matching the single-panel-at-a-time toggle above).
+    $(document).on("keydown", ".lens-tab", function (e) {
+      var key = e.key;
+      var count = $tabs.length;
+      var current = $tabs.index(this);
+      if (current < 0) return;
+      var target = -1;
+      if (key === "ArrowRight" || key === "ArrowDown") {
+        target = (current + 1) % count;
+      } else if (key === "ArrowLeft" || key === "ArrowUp") {
+        target = (current - 1 + count) % count;
+      } else if (key === "Home") {
+        target = 0;
+      } else if (key === "End") {
+        target = count - 1;
+      } else {
+        return;
+      }
+      e.preventDefault();
+      var $next = $tabs.eq(target);
+      activate($next.attr("data-lens"));
+      $next.trigger("focus");
+    });
+  }
+
+  // cute-dbt#201 — the left panel is the Given panel: always every given
+  // input (the Inspect/All-inputs segmented toggle is retired; node detail
+  // lives in the DAG shelf). The #131 ordinal binding rides the render
+  // loop index in renderAllInputs — t.given is the full in-order list.
+  function renderLeftPanel() {
+    var t = currentTest();
+    if (!t) return;
+    renderAllInputs(t);
+  }
+
+  // cute-dbt#201 — render the selected DAG node into the shelf: the
+  // model-detail card (when manifest_nodes knows the node's model) + the
+  // node's compiled SQL with the role badge riding the summary row. The
+  // unit-test given fixtures are deliberately NOT shown here — they live
+  // in the Given panel below (the pre-#201 duplicate ordinal-bound
+  // cell-diff rendering disappears with this rule), so the shelf stays
+  // focused on the node: model details + compiled SQL.
+  function renderNodeDetail(t) {
+    var $wrap = $(".dag-shelf-body").empty();
+    var m = currentModel();
+    if (!state.selectedNodeId) {
+      $wrap.append($("<div>").addClass("empty-hint").text("Click a node to inspect."));
+      return;
+    }
+    var node = m && m.dag.nodes.filter(function (n) { return n.id === state.selectedNodeId; })[0];
+    if (!node) {
+      $wrap.append($("<div>").addClass("empty-hint").text("Node not found."));
+      return;
+    }
+    $(".dag-shelf-title").text(node.label || node.id);
+    var $detail = $("<div>").addClass("node-detail")
+      .attr("data-node-id", node.id)
+      .attr("data-node-role", node.role);
+
+    var mfName = nodeManifestName(node, t, m);
+    if (mfName) $detail.append(buildModelDetailCard(mfName));
+
+    // cute-dbt#398 — when the selected DAG node is an import CTE backed by a
+    // SEED, surface the seed's data table in the shelf (the same affordance
+    // the explore page gives seed nodes). The seed table is already inlined
+    // on DATA.seed_cards (cute-dbt#350/#387) — zero-egress, no view-time
+    // fetch. Reuses buildSeedCard (the #350 grid + the #126 "data
+    // unavailable" degrade) verbatim, then runs the shared DataTables init on
+    // the injected grid (renderNodeDetail rebuilds the shelf body, so the
+    // init that renderSeedDataTables runs at load does not cover it). A
+    // non-seed import node (a model/snapshot/source ref) resolves to no seed
+    // card and this is a no-op — non-seed nodes render unchanged.
+    var seedCard = seedCardForNode(node, t);
+    if (seedCard) $detail.append(buildSeedCard(seedCard));
+
+    // role badge rides the Compiled SQL summary row (saves vertical space).
+    var sql = (m.compiled_sql && m.compiled_sql[node.id]) || "-- compiled SQL not available";
+    var $det = $("<details>").addClass("compiled-sql").attr("open", "open");
+    var $sum = $("<summary>");
+    $sum.append($("<span>").addClass("cs-label").text("Compiled SQL"));
+    $sum.append($("<span>").addClass("node-role-badge role-" + node.role).text(node.role));
+    $det.append($sum);
+    var $sqlWrap = $("<div>").addClass("sql-block-wrap");
+    var $copy = $("<button>").attr("type", "button").addClass("sql-copy").text("Copy")
+      .on("click", function (e) { e.preventDefault(); e.stopPropagation(); copySql(sql, $(this)); });
+    var $pre = $("<pre>").addClass("sql-block").append($("<code>").html(highlightLinesSql(sql)));
+    $sqlWrap.append($copy).append($pre);
+    $det.append($sqlWrap);
+    $detail.append($det);
+
+    // cute-dbt#446 (S3) — the bidirectional compiled-CTE↔code sync pane. The
+    // full `code_map.compiled` text (the single faithful source #445 derives
+    // every per-node slice from) renders as ONE numbered <pre>; the selected
+    // node's compiled span flashes, and clicking any line selects the node
+    // whose span owns it (the #180 in-place selection seam — never a
+    // renderDag rebuild). Present only for compiled models (code_map None on a
+    // seed/source); the per-node Compiled SQL slice above stays unchanged.
+    var $codeMap = buildCodeMapPane(m, node.id);
+    if ($codeMap) $detail.append($codeMap);
+
+    $wrap.append($detail);
+    // Initialize the (lazy-init) DataTables on the seed grid only after it is
+    // in the document — initDataTablesIn is a no-op when no seed card was
+    // appended (the non-seed-node path).
+    if (seedCard) initDataTablesIn($wrap);
+    // cute-dbt#446 (S3, DAG→code) — flash the selected node's compiled span in
+    // the source-map pane and scroll it into view, AFTER the shelf body is in
+    // the document. A no-op when this model has no code_map.
+    flashNodeSpan(node.id);
+  }
+
+  // cute-dbt#446 (S3) — the model's compiled source map: the line range a DAG
+  // node's compiled span covers, derived from `code_map.node_spans[id]`
+  // (#445). 1-based, inclusive `[startLine, endLine]`. The span end is the
+  // half-open byte end carried with its 1-based `end.line`; when the exclusive
+  // end sits at column 1 of `end.line` (the slice ends exactly at a newline)
+  // that line carries no node bytes, so it is excluded. `null` when the model
+  // has no code_map or the node id has no span (a seed/source/import-only id).
+  function nodeSpanLines(m, nodeId) {
+    if (!m || !m.code_map || !m.code_map.node_spans) return null;
+    var sp = m.code_map.node_spans[nodeId];
+    if (!sp || !sp.start || !sp.end) return null;
+    var startLine = sp.start.line;
+    var endLine = sp.end.line;
+    // Exclusive end at column 1 ⇒ the end line holds no node bytes.
+    if (sp.end.col != null && sp.end.col <= 1 && endLine > startLine) endLine -= 1;
+    if (endLine < startLine) endLine = startLine;
+    return { start: startLine, end: endLine };
+  }
+
+  // cute-dbt#446 (S3) — the node whose compiled span CONTAINS `line` (1-based),
+  // or null. The compiled CTE bodies tile the text without overlap (#445), so
+  // at most one node owns a line; ties (a line on a shared boundary) resolve to
+  // the most specific (smallest) span, which is the inner node.
+  function nodeOwningLine(m, line) {
+    if (!m || !m.code_map || !m.code_map.node_spans) return null;
+    var spans = m.code_map.node_spans;
+    var best = null;
+    var bestSize = Infinity;
+    Object.keys(spans).forEach(function (id) {
+      var r = nodeSpanLines(m, id);
+      if (!r) return;
+      if (line >= r.start && line <= r.end) {
+        var size = r.end - r.start;
+        if (size < bestSize) { bestSize = size; best = id; }
+      }
+    });
+    return best;
+  }
+
+  // cute-dbt#446 (S3) — build the source-map pane: the full `code_map.compiled`
+  // text as ONE numbered <pre> of `.code-line` rows, each tagged `data-line`
+  // (1-based) so the DAG→code flash can target a line range and the code→DAG
+  // click can read the line a row owns. Returns null when the model has no
+  // code_map (seed/source). NOTE: highlightLinesSql already emits a `.code-line`
+  // per source line in order, so the gutter number === the 1-based line — we
+  // only annotate each row with its line index for the sync handlers.
+  function buildCodeMapPane(m, selectedNodeId) {
+    if (!m || !m.code_map || !m.code_map.compiled) return null;
+    var $det = $("<details>").addClass("code-map").attr("open", "open");
+    var $sum = $("<summary>");
+    $sum.append($("<span>").addClass("cs-label").text("Source map"));
+    $sum.append(
+      $("<span>").addClass("code-map-hint").text("click a line to select its node")
+    );
+    $det.append($sum);
+    var $pre = $("<pre>").addClass("sql-block code-map-pre")
+      .attr("data-model", m.name);
+    var $code = $("<code>").html(highlightLinesSql(m.code_map.compiled));
+    $pre.append($code);
+    // Annotate every rendered line with its 1-based index (highlightLinesSql
+    // emits one `.code-line` per source line, in order).
+    $code.children(".code-line").each(function (i) {
+      this.setAttribute("data-line", String(i + 1));
+    });
+    $det.append($pre);
+    // Mark the selected node's lines so the initial render shows the owned
+    // range even before the flash animation (idempotent re-highlight).
+    void selectedNodeId;
+    return $det;
+  }
+
+  // cute-dbt#446 (S3, DAG→code) — flash the lines the node's compiled span
+  // owns and scroll the first into view. Uses the same `pin-flash`-style
+  // restart trick the finding pin uses (#170): toggle the class off, force a
+  // reflow, toggle it on, then clear after the animation. A no-op when the
+  // pane is absent (no code_map) or the node has no span.
+  function flashNodeSpan(nodeId) {
+    var m = currentModel();
+    var range = nodeSpanLines(m, nodeId);
+    var pre = document.querySelector(".code-map-pre");
+    if (!pre || !range) return;
+    var lines = pre.querySelectorAll(".code-line");
+    var first = null;
+    for (var i = 0; i < lines.length; i++) {
+      var n = parseInt(lines[i].getAttribute("data-line"), 10);
+      var owned = n >= range.start && n <= range.end;
+      lines[i].classList.toggle("code-line-owned", owned);
+      if (owned) {
+        if (!first) first = lines[i];
+        lines[i].classList.remove("code-flash");
+      }
+    }
+    if (!first) return;
+    // Restart the flash animation on the owned run.
+    void pre.offsetWidth;
+    for (var j = 0; j < lines.length; j++) {
+      var k = parseInt(lines[j].getAttribute("data-line"), 10);
+      if (k >= range.start && k <= range.end) lines[j].classList.add("code-flash");
+    }
+    if (first.scrollIntoView) first.scrollIntoView({ behavior: "smooth", block: "nearest" });
+    if (pre.__cuteCodeFlashTimer) clearTimeout(pre.__cuteCodeFlashTimer);
+    pre.__cuteCodeFlashTimer = setTimeout(function () {
+      pre.querySelectorAll(".code-flash").forEach(function (el) {
+        el.classList.remove("code-flash");
+      });
+      pre.__cuteCodeFlashTimer = null;
+    }, 1600);
+  }
+
+  // cute-dbt#398 — the seed card (DATA.seed_cards entry) a DAG node maps to,
+  // or null. ONLY an import-CTE node bound to a seed input resolves: scan the
+  // current test's givens for the one whose `bound_to_node` is this node id,
+  // parse its `ref('<name>')` target, and match a seed card by that bare
+  // name. Seed names are globally unique handles and DATA.seed_cards holds
+  // ONLY seeds (the #350 in-scope projection), so a name hit is a seed hit —
+  // a model import shares no name with a seed (dbt ref() names are unique
+  // across resource types). Null for any non-seed node (final select, a model
+  // import, a transform CTE) and when the seed is not in scope (no card).
+  function seedCardForNode(node, t) {
+    if (!node || node.role !== "import") return null;
+    var cards = DATA.seed_cards;
+    if (!cards || !cards.length) return null;
+    var g = ((t && t.given) || []).filter(function (x) { return x.bound_to_node === node.id; })[0];
+    if (!g) return null;
+    var refName = refTargetName(g.input);
+    if (!refName) return null;
+    return cards.filter(function (c) { return c.name === refName; })[0] || null;
+  }
+
+  // cute-dbt#98 — the test's cell-level data diff for THIS given input, or
+  // undefined. `data_diff` is present only in PR-diff mode and only when the
+  // test's own block was edited; `given[]` carries an entry only for inputs
+  // whose fixture data carried a real cell change (the domain already gated on
+  // `has_real_change()`), so presence === "default this table to Diff".
+  // cute-dbt#131 — resolve a given's cell diff by its SOURCE ORDINAL (its
+  // position in the test's `given:` list), NOT its `input` text. Two givens
+  // can share the same `ref(...)` input, and `dataDiff.given` is filtered to
+  // changed givens only — so neither `input` nor a dense index into
+  // `dataDiff.given` identifies a given. The ordinal (carried on each
+  // NamedTableDiff) is the stable key; the caller passes the render loop index,
+  // which is the same source position since `t.given` is the full in-order list.
+  function givenDataDiff(dataDiff, ordinal) {
+    if (!dataDiff || !dataDiff.given) return undefined;
+    var hit = dataDiff.given.filter(function (g) { return g.ordinal === ordinal; })[0];
+    return hit ? hit.diff : undefined;
+  }
+
+  // ---- rich hover cards: model-ref, format-block, overrides (#202) -------
+  //
+  // Three body-appended singleton tips (#model-tooltip / #fmt-tooltip /
+  // #ov-tooltip) behind delegated trigger classes, shown on mouseenter AND
+  // focusin (every trigger is focusable — the #146 rule: no native title as
+  // a primary affordance). The prior-model-state this-badge and the
+  // incremental-branch mode badge deliberately do NOT ride this mechanism:
+  // per the founder decision on epic cute-dbt#197 they keep the #146
+  // focusable-trigger CSS bubble (badge-borne, the pass-2 visual styling) —
+  // exactly one tooltip mechanism per affordance, so the pass-2's JS
+  // info-tip twin for those badges is not ported.
+
+  // The raw token of a Current-view cell: the canonical key value when the
+  // wire carries one, else the authored display ("" for null/absent — the
+  // dbt empty-equals-null convention round-trips as the empty token).
+  function cellRaw(cell) {
+    if (!cell) return "";
+    var k = cell.key || {};
+    return k.v != null ? String(k.v) : (cell.display != null ? String(cell.display) : "");
+  }
+  // The token as a YAML scalar: numbers/bools verbatim, strings quoted only
+  // when YAML would re-parse them (colon/space/quote/comment characters).
+  function cellYaml(cell) {
+    var k = (cell && cell.key) || {};
+    if (k.t === "number" || k.t === "bool" || k.t === "boolean") return cellRaw(cell);
+    var s = cellRaw(cell);
+    return /[:\s'"#]/.test(s) ? JSON.stringify(s) : s;
+  }
+  // Reconstruct the fixture as authored, purely client-side from the
+  // existing payload (no new data): sql => the raw SELECT; csv => header +
+  // comma rows; dict/other => a YAML list of row dicts.
+  function reconstructFormatBlock(given) {
+    if (isSqlCodeBlockFixture(given)) return { text: String(given.rows || ""), lang: "sql" };
+    var table = given.table || { columns: [], rows: [] };
+    var cols = table.columns || [], rows = table.rows || [];
+    if ((given.format || "") === "csv") {
+      var lines = [cols.join(",")];
+      rows.forEach(function (r) {
+        var c = r.cells || [];
+        lines.push(cols.map(function (_, i) { return cellRaw(c[i]); }).join(","));
+      });
+      return { text: lines.join("\n"), lang: "csv" };
+    }
+    var out = [];
+    rows.forEach(function (r) {
+      var c = r.cells || [];
+      var parts = cols.map(function (col, i) { return col + ": " + cellYaml(c[i]); });
+      out.push("- " + parts.join("\n  "));
+    });
+    return { text: out.join("\n"), lang: "yaml" };
+  }
+  // Plaintext gutter-numbered lines for the fmt tip's csv branch — the same
+  // [gutter][code] line DOM the SQL/YAML highlighters emit, no tokenizing.
+  function highlightLinesPlain(text) {
+    var rows = String(text || "").replace(/\n+$/, "").split("\n");
+    return rows.map(function (r, i) {
+      return '<span class="code-line"><span class="code-gutter" aria-hidden="true">' + (i + 1)
+           + '</span><span class="diff-code">' + escapeHtml(r) + "</span></span>";
+    }).join("");
+  }
+  // The `format: <fmt>` badge — shown for ALL formats (the pre-#202 dict
+  // suppression is retired). It becomes a #fmt-tooltip trigger only when
+  // there is data to reconstruct (an UNLOADED external fixture has no rows
+  // in the payload — its badge stays plain, the same graceful-absence
+  // posture as a model pill without a manifest_nodes entry).
+  function formatBadge(given) {
+    var fmt = isSqlCodeBlockFixture(given) ? "sql" : (given.format || "");
+    var block = reconstructFormatBlock(given);
+    var $b = $("<span>").addClass("format-badge")
+      .attr("data-fmt", fmt)
+      .text("format: " + fmt);
+    if (block.text) {
+      $b.addClass("has-fmt-tip")
+        .attr("tabindex", "0").attr("role", "button")
+        .attr("data-fmt-lang", block.lang)
+        .attr("data-fmt-block", block.text);
+    }
+    return $b;
+  }
+  // Given header title: `ref(` + highlighted model pill + `)` — the
+  // `given ·` prefix is dropped (the whole panel is already the Given
+  // block). The pill is a #model-tooltip trigger only when manifest_nodes
+  // knows that upstream model (graceful absence); non-ref inputs (`this`,
+  // `source(...)`) render verbatim.
+  function buildGivenTitle(given) {
+    var $h = $("<h4>").addClass("table-title");
+    var ref = refTargetName(given.input);
+    if (ref) {
+      $h.append($("<span>").addClass("gt-prefix").text("ref("));
+      var $mid = $("<span>").addClass("gt-model").text(ref);
+      if (manifestNode(ref)) {
+        $mid.addClass("has-model-tip").attr("data-model", ref)
+          .attr("tabindex", "0").attr("role", "button");
+      }
+      $h.append($mid).append($("<span>").addClass("gt-prefix").text(")"));
+    } else {
+      $h.append($("<span>").addClass("gt-model").text(given.input));
+    }
+    return $h;
+  }
+  // Lazily create (or re-class) the body-appended singleton tip `id`. Same
+  // posture as ensureColTooltip: aria-hidden (the trigger's visible text /
+  // aria-label is the AT surface), hidden until shown.
+  function ensureTip(id, cls) {
+    var el = document.getElementById(id);
+    if (!el) {
+      el = document.createElement("div");
+      el.id = id;
+      el.hidden = true;
+      el.setAttribute("aria-hidden", "true");
+      document.body.appendChild(el);
+    }
+    el.className = cls;
+    return el;
+  }
+  function hideTip(id) { var el = document.getElementById(id); if (el) el.hidden = true; }
+  // Model-ref tip (given pill + expected badge): materialized + tags +
+  // description + model-level tests. No name (you're hovering it) and no
+  // columns (the table below shows them) — keeps the bubble small.
+  function showModelTip(trigger) {
+    var name = trigger.getAttribute("data-model"), info = manifestNode(name);
+    if (!info) return;
+    var el = ensureTip("model-tooltip", "col-tooltip model-tooltip");
+    var h = "";
+    var hasChips = info.materialized || (info.tags && info.tags.length);
+    if (hasChips) {
+      h += '<div class="mt-chips">';
+      if (info.materialized) h += '<span class="mt-mat">' + escapeHtml(info.materialized) + "</span>";
+      (info.tags || []).forEach(function (tg) { h += '<span class="mt-tag">' + escapeHtml(tg) + "</span>"; });
+      h += "</div>";
+    }
+    if (info.description) h += '<div class="ct-desc">' + escapeHtml(info.description) + "</div>";
+    // cute-dbt#235 — model tests ride the column tooltip's own
+    // .ct-test/.ct-key/.ct-vals anatomy (the shared ctTestHtml builder),
+    // so the model tip and the column tip stay visually consistent.
+    // The shelf card keeps its light-surface .dt-* chips (testChipsHtml).
+    if (info.model_tests && info.model_tests.length) {
+      h += '<div class="ct-tests-label">Model tests</div>';
+      h += '<div class="ct-tests">' + info.model_tests.map(ctTestHtml).join("") + "</div>";
+    }
+    el.innerHTML = h;
+    el.hidden = false;
+    positionTipNear(el, trigger);
+  }
+  // Format tip: the fixture reconstructed in its authored format, in a
+  // framed mini code-surface (sql/yaml ride the #132 highlighters).
+  //
+  // cute-dbt#246 — the bubble is pointer-events:none and hides on
+  // mouseleave/blur, so anything laid below a height fold could never be
+  // scrolled to or read (the old fixed 22rem + overflow:auto pairing left
+  // below-fold rows silently unreachable for EVERY input modality). Bound
+  // the content instead of folding it: show at most FMT_TIP_MAX_LINES
+  // lines — the tip is a glance surface; the grid below the badge
+  // carries the full data — then trim by REAL MEASUREMENT (PR #286
+  // review): remove rendered lines until the laid-out content fits the
+  // bubble's viewport-capped box. Measuring the live layout (never a
+  // px-per-line estimate) keeps the bound correct under browser zoom, a
+  // forced minimum font size (a11y), wrapped long lines, and any future
+  // rem-based sizing — and the truthful elision row counts exactly what
+  // was trimmed, however it was trimmed.
+  var FMT_TIP_MAX_LINES = 20;
+  function fmtTipMoreText(more) {
+    return "… +" + more + " more line" + (more === 1 ? "" : "s");
+  }
+  function showFmtTip(trigger) {
+    var fmt = trigger.getAttribute("data-fmt"), lang = trigger.getAttribute("data-fmt-lang");
+    var block = trigger.getAttribute("data-fmt-block") || "";
+    var lines = String(block).replace(/\n+$/, "").split("\n");
+    var shown = Math.min(lines.length, FMT_TIP_MAX_LINES);
+    var more = lines.length - shown;
+    var code = lang === "sql" ? highlightLinesSql(lines.slice(0, shown).join("\n"))
+             : lang === "yaml" ? highlightLinesYaml(lines.slice(0, shown).join("\n"))
+             : highlightLinesPlain(lines.slice(0, shown).join("\n"));
+    var el = ensureTip("fmt-tooltip", "fmt-tooltip");
+    el.innerHTML = '<div class="code-header"><span class="code-filename">format: ' + escapeHtml(fmt)
+      + '</span></div><pre class="sql-block"><code>' + code + "</code></pre>"
+      + (more > 0 ? '<div class="fmt-more">' + fmtTipMoreText(more) + "</div>" : "");
+    el.hidden = false;
+    // Measure the live layout at full width (reset a stale edge-clamped
+    // left BEFORE measuring — the #232 D18 lesson: a squeezed box wraps
+    // differently and mis-measures).
+    el.style.left = "0px";
+    // The elision row participates in the measured height, so it must
+    // exist before trimming starts (created here if the ceiling alone
+    // didn't already call for one).
+    var moreEl = el.querySelector(".fmt-more");
+    if (!moreEl && el.scrollHeight > el.clientHeight + 1) {
+      moreEl = document.createElement("div");
+      moreEl.className = "fmt-more";
+      el.appendChild(moreEl);
+    }
+    var rows = el.querySelectorAll(".code-line");
+    var keep = rows.length;
+    while (keep > 1 && el.scrollHeight > el.clientHeight + 1) {
+      keep -= 1;
+      rows[keep].parentNode.removeChild(rows[keep]);
+      more += 1;
+    }
+    if (moreEl) moreEl.textContent = fmtTipMoreText(more);
+    positionTipNear(el, trigger);
+  }
+  // Overrides tip: key = value rows grouped by macros / vars / env_vars.
+  // Values arrive as NATIVE scalars (#200) and are stringified here.
+  function showOvTip(trigger) {
+    var ov = {};
+    try { ov = JSON.parse(trigger.getAttribute("data-overrides") || "{}"); } catch (e) { ov = {}; }
+    var el = ensureTip("ov-tooltip", "col-tooltip ov-tooltip");
+    var h = '<div class="ct-tests-label">Overrides</div>';
+    Object.keys(ov).forEach(function (grp) {
+      h += '<div class="ov-grp"><div class="ov-grp-name">' + escapeHtml(grp) + "</div>";
+      var v = ov[grp];
+      if (v && typeof v === "object") {
+        Object.keys(v).forEach(function (k) {
+          h += '<div class="ov-row"><code class="ov-key">' + escapeHtml(k)
+            + '</code><span class="ov-eq">=</span><span class="ov-val">'
+            + escapeHtml(String(v[k])) + "</span></div>";
+        });
+      } else {
+        h += '<div class="ov-row"><span class="ov-val">' + escapeHtml(String(v)) + "</span></div>";
+      }
+      h += "</div>";
+    });
+    el.innerHTML = h;
+    el.hidden = false;
+    positionTipNear(el, trigger);
+  }
+  // Delegated hover/focus handlers for the three singleton tips — the same
+  // mouseenter+focusin / mouseleave+focusout pairing bindColTooltips uses.
+  function bindModelTips() {
+    $(document)
+      .on("mouseenter focusin", ".has-model-tip", function () { showModelTip(this); })
+      .on("mouseleave focusout", ".has-model-tip", function () { hideTip("model-tooltip"); })
+      .on("mouseenter focusin", ".has-fmt-tip", function () { showFmtTip(this); })
+      .on("mouseleave focusout", ".has-fmt-tip", function () { hideTip("fmt-tooltip"); })
+      .on("mouseenter focusin", ".has-ov-tip", function () { showOvTip(this); })
+      .on("mouseleave focusout", ".has-ov-tip", function () { hideTip("ov-tooltip"); });
+  }
+
+  // cute-dbt#232 (audit D1) — geometry-only edge tagger for the CSS-bubble
+  // tip family: the .has-mode-tip badges (incremental branch · prior model
+  // state) and the static .expect-tooltip DAG hint. These bubbles are pure
+  // CSS (the #146 contract: focusable trigger, reveal on :hover AND
+  // :focus/:focus-visible — never JS-controlled visibility), so a trigger
+  // near a viewport edge clipped its fixed-direction bubble. This tagger
+  // never touches visibility: it measures the trigger against the viewport
+  // on the same delegated mouseenter/focusin moment that precedes the CSS
+  // reveal and annotates `data-tip-edge="right"|"left"` (or removes it) so
+  // the stylesheet can anchor the bubble away from the hugged edge.
+  function tagTipEdge(trigger) {
+    var bubble = trigger.querySelector(".expect-tooltip-bubble");
+    if (!bubble) return;
+    var vw = document.documentElement.clientWidth;
+    var r = trigger.getBoundingClientRect();
+    // The bubble is laid out even while visibility:hidden, so its real
+    // box (20rem capped at min(70vw, 100vw - 16px), plus padding) is
+    // measurable without revealing it.
+    var w = bubble.offsetWidth;
+    // Default anchors: badge-borne bubbles open rightward from the
+    // trigger's left edge; the .expect-tooltip hint centers on it.
+    var defLeft = trigger.classList.contains("has-mode-tip")
+      ? r.left
+      : (r.left + r.right) / 2 - w / 2;
+    if (defLeft + w > vw - 8 && r.right - w >= 8) {
+      trigger.setAttribute("data-tip-edge", "right");
+    } else if (defLeft < 8) {
+      trigger.setAttribute("data-tip-edge", "left");
+    } else {
+      trigger.removeAttribute("data-tip-edge");
+    }
+  }
+  function bindTipEdgeTagger() {
+    $(document).on("mouseenter focusin", ".has-mode-tip, .expect-tooltip", function () {
+      tagTipEdge(this);
+    });
+  }
+
+  // cute-dbt#240 (defect B) — the human name of the node that OWNS a
+  // given's columns, for the metadata-less fallback bubble: the bare model
+  // name for ref('x'), `source.table` for source('source','table'), the
+  // target model for `this`. Unparseable inputs fall back to the raw input
+  // string (still truthful, never empty). Both quote styles match (PR #244
+  // review): dbt-fusion ships the AUTHORED input string verbatim — a
+  // `ref("stg_payments")` given compiles onto the manifest wire
+  // double-quoted, unnormalized (verified against a real fusion
+  // 2.0-preview.177 compile) — so ref("x") / source("a", "b") reach this
+  // parser exactly as written. The backreference pins matching open/close
+  // quotes (a mixed-quote token stays unparsed → raw-input fallback).
+  function givenOwnerLabel(given, targetModel) {
+    if (given.is_this) return targetModel || "this";
+    var input = given.input || "";
+    var ref = /^ref\(\s*(['"])([^'"]+)\1\s*\)$/.exec(input);
+    if (ref) return ref[2];
+    var src = /^source\(\s*(['"])([^'"]+)\1\s*,\s*(['"])([^'"]+)\3\s*\)$/.exec(input);
+    if (src) return src[2] + "." + src[4];
+    return input;
+  }
+
+  function renderGivenSection(given, dataDiff, ordinal, targetModel) {
+    // The section keeps `data-input-name` (the test seam) even though the
+    // visible title is now the #202 ref() pill rendering.
+    var $sec = $("<section>").addClass("given-section").attr("data-input-name", given.input);
+    var $hdr = $("<div>").addClass("table-header");
+    $hdr.append(buildGivenTitle(given));
+    // cute-dbt#145 — mark a `given: - input: this` as the model's prior
+    // state. $hdr is freshly built per call (renderAllInputs .empty()s the
+    // wrap), so no idempotent clear is needed here. cute-dbt#202 — the
+    // badge carries an explanatory tip via the #146 focusable-trigger CSS
+    // bubble (badge-borne, the founder-decided mechanism — see the #202
+    // section header above). Copy stays strategy-invariant (the #159/#161
+    // lesson): "incremental branch", never a merge/insert-specific verb.
+    if (given.is_this) {
+      var thisTip = "The model's rows BEFORE this run — dbt feeds these to an "
+        + "incremental model as its existing table (the `this` relation), so "
+        + "the test can exercise the incremental branch.";
+      $hdr.append(
+        $("<span>").addClass("this-badge has-mode-tip")
+          .attr("tabindex", "0")
+          .attr("aria-label", thisTip)
+          .text("prior model state")
+          .append($("<span>").addClass("expect-tooltip-bubble")
+            .attr("aria-hidden", "true").text(thisTip))
+      );
+    }
+    // cute-dbt#126 — provenance chip when the data was LOADED from an external
+    // fixture file (`fixture:` set AND rows present, i.e. the reader inlined
+    // it). Shown in BOTH the grid and the sql-code-block paths below; the
+    // UNREADABLE case (rows still null) keeps the buildExternalFixtureAffordance
+    // instead. Inline fixtures omit `fixture`, so the chip never fires for them
+    // and existing reports stay byte-identical.
+    if (isLoadedExternalFixture(given)) {
+      $hdr.append(buildFixtureProvenanceChip(given.fixture));
+    }
+    if (isSqlCodeBlockFixture(given)) {
+      $hdr.append(formatBadge(given));
+      $sec.append($hdr);
+      $sec.append(buildSqlCodeBlock(given.rows));
+      return $sec;
+    }
+    // cute-dbt#98 / #126 — external-fixture guard. When the data lives in an
+    // external fixture file (`fixture:` set AND `rows == null`, so the rows are
+    // NOT in the manifest), render an affordance pointing at the file + a
+    // pointer to the Authoring YAML drawer rather than a silently-empty grid.
+    if (isExternalFixture(given)) {
+      if (given.format) {
+        $hdr.append(formatBadge(given));
+      }
+      $sec.append($hdr);
+      $sec.append(buildExternalFixtureAffordance(given.fixture));
+      return $sec;
+    }
+    // cute-dbt#138 — the Current view renders the Rust-computed POD directly.
+    // cute-dbt#202 — the badge shows for ALL formats now, dict included (its
+    // hover reconstructs the fixture in its authored format).
+    var table = given.table || { columns: [], rows: [] };
+    var rowCount = table.rows.length;
+    if (given.format) {
+      $hdr.append(formatBadge(given));
+    }
+    $hdr.append($("<span>").addClass("row-count-badge")
+      .text(rowCount + " row" + (rowCount === 1 ? "" : "s")));
+    $sec.append($hdr);
+    var diff = givenDataDiff(dataDiff, ordinal);
+    $sec.append(buildFixtureView(table, "given-table", diff, given.column_meta,
+      givenOwnerLabel(given, targetModel)));
+    return $sec;
+  }
+
+  function renderAllInputs(t) {
+    var $wrap = $(".left-panel-body").empty().attr("data-mode", "inputs");
+    if (!t.given.length) {
+      $wrap.append($("<div>").addClass("empty-hint").text("No fixtures defined for this test."));
+      return;
+    }
+    t.given.forEach(function (given, idx) {
+      $wrap.append(renderGivenSection(given, t.data_diff, idx, t.target_model));
+    });
+    initDataTablesIn($wrap);
+  }
+
+  // cute-dbt#202 — the Expected panel's model pill: the target model's
+  // name in the same .gt-model dress as the given ref() pills, a
+  // #model-tooltip trigger when manifest_nodes knows the model (graceful
+  // absence otherwise). Returns an empty set for a missing model so
+  // callers can append unconditionally.
+  function buildExpectedModelBadge(m) {
+    if (!m) return $();
+    var $b = $("<span>").addClass("gt-model expected-model-badge").text(m.name);
+    if (manifestNode(m.name)) {
+      $b.addClass("has-model-tip").attr("data-model", m.name)
+        .attr("tabindex", "0").attr("role", "button");
+    }
+    return $b;
+  }
+
+  function renderExpectedPanel(t) {
+    var $panel = $(".expected-panel");
+    var $exHdr = $panel.find(".panel-header");
+    // cute-dbt#178 — the row-count badge is a PERSISTENT static element. A
+    // prior render may have relocated it into the fixture-view bar (below);
+    // return it to the header BEFORE the body wipe so $body.empty() can
+    // never destroy it.
+    var $rowcount = $panel.find(".expected-rowcount");
+    $exHdr.append($rowcount);
+    // cute-dbt#145 — per-test mode badge + expect-semantics tooltip.
+    // The .panel-header is a PERSISTENT static element reused across
+    // renders, so first clear any prior mode badge / model pill (targeted —
+    // must NOT touch the static .expected-rowcount; the #161 idempotent
+    // clear-list extends to .expected-model-badge with cute-dbt#202, and
+    // the expect-semantics bubble now rides INSIDE the mode badge so the
+    // retired ⓘ button needs no selector). The mode badge shows only when
+    // the enclosing model is incremental; the tooltip rides the
+    // AUTHORITATIVE bool (is_incremental_mode === true), NEVER the
+    // `this`-given proxy, and NEVER on the full-refresh branch (there
+    // `expect` IS the final table). Emitted BEFORE the sql / external
+    // early-returns: the mode is about the test, not the fixture format.
+    $exHdr.find(".mode-badge, .expected-model-badge").remove();
+    var $body = $panel.find(".expected-body").empty();
+    var em = currentModel();
+    if (em && em.is_incremental) {
+      var incrementalMode = t.is_incremental_mode === true;
+      var $modeBadge = $("<span>")
+        .addClass("mode-badge " + (incrementalMode ? "mode-incremental" : "mode-full-refresh"))
+        .text(incrementalMode ? "incremental branch" : "full-refresh branch");
+      if (incrementalMode) {
+        var tip = "Expected is the output of the model's compiled SELECT on the "
+          + "incremental branch — the rows the configured incremental strategy "
+          + "will apply to the table — not the table's final state after the run.";
+        // cute-dbt#202 (founder decision, epic #197) — the badge ITSELF is
+        // the focusable trigger carrying a CSS-rendered bubble shown on
+        // hover AND keyboard focus (the #146 mechanism, pass-2 badge-borne
+        // styling; the separate ⓘ button is retired). A native `title` is
+        // hover-delayed, keyboard-unreachable, and frequently never paints
+        // (cute-dbt#146 review). The bubble is the visual surface;
+        // `aria-label` carries the same text for assistive tech (bubble is
+        // aria-hidden so it is not announced twice). Pure CSS — no asset,
+        // no JS tooltip lib — so the zero-egress gate holds.
+        $modeBadge.addClass("has-mode-tip")
+          .attr("tabindex", "0")
+          .attr("aria-label", tip)
+          .append($("<span>")
+            .addClass("expect-tooltip-bubble")
+            .attr("aria-hidden", "true")
+            .text(tip));
+      }
+      $exHdr.append($modeBadge);
+    }
+    // cute-dbt#126 — external-fixture provenance for the expect side (loaded
+    // from a file). `$body` is emptied each render, so no cleanup is needed.
+    if (isLoadedExternalFixture(t.expected)) {
+      $body.append(buildFixtureProvenanceChip(t.expected.fixture));
+    }
+    if (isSqlCodeBlockFixture(t.expected)) {
+      $panel.find(".expected-rowcount").text("format: sql");
+      $body.append(buildSqlCodeBlock(t.expected.rows));
+      return;
+    }
+    // cute-dbt#98 / #126 — external-fixture guard for the expect side.
+    if (isExternalFixture(t.expected)) {
+      $panel.find(".expected-rowcount").text("external fixture");
+      $body.append(buildExternalFixtureAffordance(t.expected.fixture));
+      return;
+    }
+    // cute-dbt#138 — the Current view renders the Rust-computed POD directly.
+    var table = t.expected.table || { columns: [], rows: [] };
+    var rowCount = table.rows.length;
+    $rowcount.text(rowCount + " row" + (rowCount === 1 ? "" : "s"));
+    // The expect side's cell diff, when the test's expect fixture carried a
+    // real change (cute-dbt#98). `data_diff.expect` already passed
+    // `has_real_change()`, so its presence === "default this table to Diff".
+    var diff = (t.data_diff && t.data_diff.expect) || undefined;
+    $body.append(buildFixtureView(table, "expected-table", diff, t.expected.column_meta,
+      t.target_model));
+    // cute-dbt#178 / cute-dbt#202 / cute-dbt#232 — the meta row ALWAYS
+    // rides a .fixture-view-bar below the "Expected" title, mirroring the
+    // Given headers (the pass-2 spec; audit D2): reading order
+    // [model pill] [mode badge] [N rows], with the Diff/File toggle — when
+    // an expect cell diff built one — pushed to the far right (CSS
+    // margin-left:auto). buildFixtureView returns a bare grid when there
+    // is no expect diff, so create the bar then (the right-aligned,
+    // order-reversed header fallback is retired). .prepend() MOVES the
+    // nodes (no copy), so nothing is duplicated. The sql / external
+    // early-returns above keep the header placement (spec-consistent).
+    var $bar = $body.find(".fixture-view-bar").first();
+    if (!$bar.length) {
+      $bar = $("<div>").addClass("fixture-view-bar");
+      $body.prepend($bar);
+    }
+    $bar.prepend($rowcount);
+    $bar.prepend($exHdr.find(".mode-badge")); // empty set when not incremental
+    $bar.prepend(buildExpectedModelBadge(em)); // empty set when no model
+    initDataTablesIn($body);
+  }
+
+  // dbt's unit-test fixture format determines what shape `rows` arrives
+  // in (cute-dbt#66, 2026-05-26 — engine divergence finding):
+  //
+  //   format | dbt-core 1.11+    | dbt-fusion 2.0-preview
+  //   -------|-------------------|-----------------------
+  //   dict   | array of dicts    | array of dicts
+  //   csv    | array of dicts    | raw CSV string
+  //   sql    | raw SELECT string | raw SELECT string
+  //
+  // The two engines diverge on csv normalization — core pre-parses the
+  // CSV body at compile time, fusion passes it through. cute-dbt must
+  // tabulate both csv paths so reports look identical regardless of
+  // which engine compiled the manifest. `format: sql` arrives as a raw
+  // SELECT string; cute-dbt#137 tabulates the LITERAL-ROW subset in Rust
+  // (so `obj.table` is populated), and only a NON-literal sql (a real
+  // FROM/operator/cast/function) falls back to the syntax-highlighted
+  // code block here.
+
+  // Whether `obj` is a raw `format: sql` fixture string.
+  function isSqlStringFixture(obj) {
+    return Boolean(obj && obj.format === "sql" && typeof obj.rows === "string");
+  }
+
+  // Whether to render `obj` as the sql CODE BLOCK fallback: a raw sql
+  // string AND the domain produced no tabulated `table` POD (cute-dbt#137 —
+  // a literal-row SELECT tabulates, so `obj.table` is present and we render
+  // the data grid instead). An accepted literal-sql always yields ≥1 row, so
+  // a truthy `obj.table` reliably distinguishes "tabulated" from "fell back".
+  function isSqlCodeBlockFixture(obj) {
+    return isSqlStringFixture(obj) && !obj.table;
+  }
+
+  // Render a sql-format fixture as a syntax-highlighted code block.
+  // Reuses the compiled-SQL drawer's `.sql-block-wrap` shell so visual
+  // style stays consistent. No copy button: the source of truth is the
+  // fixture in the manifest, not the rendered DOM. This is the ONE
+  // remaining non-tabulatable fallback path: a `format: sql` fixture has no
+  // cells, so the domain returns no `table` POD (cute-dbt#138) and the JS
+  // renders the raw SELECT here.
+  function buildSqlCodeBlock(sql) {
+    var $wrap = $("<div>").addClass("sql-block-wrap fixture-sql-block-wrap");
+    var $pre = $("<pre>").addClass("sql-block").append(
+      $("<code>").html(highlightLinesSql(String(sql)))
+    );
+    $wrap.append($pre);
+    return $wrap;
+  }
+
+  // cute-dbt#138 — the JS csv/dict table parsers (`parseCsvRows`,
+  // `extractRowsFromFixture`, `collectColumns`) are RETIRED. The Current view
+  // now renders the Rust-computed `FixtureTable` POD that the domain ships on
+  // each given/expect payload (`table`), so csv/dict tabulation — and the
+  // engine-divergence handling above — happens once, in Rust
+  // (`table_from_manifest_rows`), and the template is a pure renderer. CSV
+  // RFC 4180 correctness is now owned by the Rust `parse_csv_rows` unit tests
+  // (the JS `__cuteParseCsv` headless seam retired with the function).
+
+  function escapeHtml(s) {
+    return String(s)
+      .replace(/&/g, "&amp;")
+      .replace(/</g, "&lt;")
+      .replace(/>/g, "&gt;")
+      .replace(/"/g, "&quot;")
+      .replace(/'/g, "&#39;");
+  }
+
+  function escapeHtmlForBody(s) {
+    return String(s).replace(/&/g, "&amp;").replace(/</g, "&lt;").replace(/>/g, "&gt;");
+  }
+  // cute-dbt#132 — token-stream syntax highlighters.
+  //
+  // Replaces the old single-`String.replace` highlighters (which matched a
+  // whole `{{ ref('stg_orders') }}` as one opaque jinja blob, so the inner
+  // `'stg_orders'` never read as a string). The new approach is a
+  // position-advancing, mode-stack scanner that returns a GAP-FREE token
+  // stream `[{text, cls}, ...]` over the RAW input (cls "" = plaintext). Two
+  // hard invariants hold by construction:
+  //   1. Gap-free: tokenizeSql(s).map(t=>t.text).join("") === s for all s.
+  //   2. Raw-in / escape-at-emit: tokens carry RAW slices; HTML escaping
+  //      happens only at `emitTokens` time, so renderBlockDiff's codepoint
+  //      `emphasis` offsets (Rust `chars()` indices) never drift.
+  // Every branch advances pos by >=1 (single-char fallthrough is the
+  // backstop), so unterminated fragments — renderBlockDiff feeds per-line
+  // pieces of multi-line constructs — flush-to-end instead of hanging.
+
+  // SQL keyword set (lowercase). `from_date` does NOT highlight `from`
+  // because identifiers are scanned WHOLE then looked up (cute-dbt#31/#40).
+  var SQL_KEYWORDS = {};
+  ("select from where with recursive as join inner left right full outer cross " +
+   "lateral natural on using group by order having limit offset fetch union " +
+   "intersect except all distinct case when then else end and or not in is null " +
+   "like ilike between exists any some asc desc nulls first last over partition " +
+   "qualify window cast try_cast coalesce sum count avg min max row_number true " +
+   "false").split(" ").forEach(function (k) { SQL_KEYWORDS[k] = true; });
+  // Jinja control words stay violet inside `{{ }}` / `{% %}`.
+  var JINJA_KEYWORDS = {};
+  ("if elif else endif for in endfor set endset macro endmacro call endcall " +
+   "filter endfilter block endblock do with without as import from include " +
+   "extends is not and or loop recursive raw endraw").split(" ")
+    .forEach(function (k) { JINJA_KEYWORDS[k] = true; });
+  // Known dbt functions: their name reads violet even without a trailing `(`.
+  var JINJA_FUNCS = {};
+  ("ref source config var env_var is_incremental this target model graph " +
+   "builtins flags modules doc run_query statement log print adapter return " +
+   "dispatch").split(" ").forEach(function (k) { JINJA_FUNCS[k] = true; });
+
+  function isWordChar(ch) {
+    return ch !== undefined && /[A-Za-z0-9_]/.test(ch);
+  }
+
+  // Scan the inside of a `{{ }}` (expr) or `{% %}` (stmt) construct, pushing
+  // tokens onto `out`. `src` is the whole string, `i` the index just past the
+  // open delimiter. `expr` selects the close delimiter. Returns the index past
+  // the close (or src.length if the fragment is unterminated). Strings inside
+  // jinja are `sql-string` (the headline fix: `ref('stg_orders')` reads teal).
+  function scanJinjaBody(src, i, out, expr) {
+    var n = src.length;
+    while (i < n) {
+      var ch = src[i];
+      // Close delim: `-}}`/`}}` for expr, `-%}`/`%}` for stmt.
+      if (expr) {
+        if (ch === "-" && src[i + 1] === "}" && src[i + 2] === "}") {
+          out.push({ text: "-}}", cls: "sql-jinja" }); return i + 3;
+        }
+        if (ch === "}" && src[i + 1] === "}") {
+          out.push({ text: "}}", cls: "sql-jinja" }); return i + 2;
+        }
+      } else {
+        if ((ch === "-" || ch === "+") && src[i + 1] === "%" && src[i + 2] === "}") {
+          out.push({ text: ch + "%}", cls: "sql-jinja" }); return i + 3;
+        }
+        if (ch === "%" && src[i + 1] === "}") {
+          out.push({ text: "%}", cls: "sql-jinja" }); return i + 2;
+        }
+      }
+      // String inside jinja -> sql-string.
+      if (ch === "'" || ch === '"') {
+        var q = ch, j = i + 1;
+        while (j < n) {
+          if (src[j] === "\\") { j += 2; continue; }
+          if (src[j] === q) { j += 1; break; }
+          j += 1;
+        }
+        out.push({ text: src.slice(i, Math.min(j, n)), cls: "sql-string" });
+        i = Math.min(j, n);
+        continue;
+      }
+      // Identifier: jinja keyword / dbt function / function-call -> jinja; else plain.
+      if (/[A-Za-z_]/.test(ch)) {
+        var k = i + 1;
+        while (k < n && isWordChar(src[k])) k += 1;
+        var word = src.slice(i, k);
+        var lower = word.toLowerCase();
+        var rest = k;
+        while (rest < n && (src[rest] === " " || src[rest] === "\t")) rest += 1;
+        var isCall = src[rest] === "(";
+        if (JINJA_KEYWORDS[lower] || JINJA_FUNCS[lower] || isCall) {
+          out.push({ text: word, cls: "sql-jinja" });
+        } else {
+          out.push({ text: word, cls: "" });
+        }
+        i = k;
+        continue;
+      }
+      // Number / operator / other -> plaintext (coalesce a run of non-word,
+      // non-quote, non-close-delim chars into one plaintext token).
+      var s = i;
+      i += 1;
+      out.push({ text: src.slice(s, i), cls: "" });
+    }
+    return i; // unterminated fragment: flushed to end, no hang.
+  }
+
+  // Tokenize SQL (with embedded jinja). Gap-free over RAW `src`.
+  function tokenizeSql(src) {
+    src = String(src);
+    var out = [];
+    var n = src.length;
+    var i = 0;
+    var plainStart = -1;
+    function flushPlain(upTo) {
+      if (plainStart !== -1 && upTo > plainStart) {
+        out.push({ text: src.slice(plainStart, upTo), cls: "" });
+      }
+      plainStart = -1;
+    }
+    while (i < n) {
+      var c0 = src[i], c1 = src[i + 1];
+      // 1. jinja comment {# ... #} (opaque; unterminated -> flush rest).
+      if (c0 === "{" && c1 === "#") {
+        flushPlain(i);
+        var end = src.indexOf("#}", i + 2);
+        var stop = end === -1 ? n : end + 2;
+        out.push({ text: src.slice(i, stop), cls: "sql-comment" });
+        i = stop;
+        continue;
+      }
+      // 2/3. jinja expr `{{` or stmt `{%` (optional -/+ handled by scanner).
+      if (c0 === "{" && (c1 === "{" || c1 === "%")) {
+        flushPlain(i);
+        var expr = c1 === "{";
+        // Emit open delim (with optional whitespace-control sign).
+        var sign = (src[i + 2] === "-" || src[i + 2] === "+") ? src[i + 2] : "";
+        var open = src.slice(i, i + 2 + sign.length);
+        out.push({ text: open, cls: "sql-jinja" });
+        i = scanJinjaBody(src, i + 2 + sign.length, out, expr);
+        continue;
+      }
+      // 4. block comment /* ... */ (unterminated -> flush rest).
+      if (c0 === "/" && c1 === "*") {
+        flushPlain(i);
+        var be = src.indexOf("*/", i + 2);
+        var bstop = be === -1 ? n : be + 2;
+        out.push({ text: src.slice(i, bstop), cls: "sql-comment" });
+        i = bstop;
+        continue;
+      }
+      // 5. line comment -- ... to EOL.
+      if (c0 === "-" && c1 === "-") {
+        flushPlain(i);
+        var nl = src.indexOf("\n", i);
+        var lstop = nl === -1 ? n : nl;
+        out.push({ text: src.slice(i, lstop), cls: "sql-comment" });
+        i = lstop;
+        continue;
+      }
+      // 6. single-quoted string '...' with '' doubling and \' tolerance. If a
+      //    jinja open appears inside, jinja wins: emit string-so-far, scan the
+      //    jinja, then resume the string.
+      if (c0 === "'") {
+        flushPlain(i);
+        var sBegin = i;
+        var j = i + 1;
+        while (j < n) {
+          var cj = src[j];
+          if (cj === "\\") { j += 2; continue; }
+          if (cj === "'" && src[j + 1] === "'") { j += 2; continue; } // doubled
+          if (cj === "{" && (src[j + 1] === "{" || src[j + 1] === "%" || src[j + 1] === "#")) {
+            // jinja wins inside the string body.
+            if (j > sBegin) out.push({ text: src.slice(sBegin, j), cls: "sql-string" });
+            // Re-enter the main loop at the jinja by handling it inline.
+            var jc1 = src[j + 1];
+            if (jc1 === "#") {
+              var je = src.indexOf("#}", j + 2);
+              var jstop = je === -1 ? n : je + 2;
+              out.push({ text: src.slice(j, jstop), cls: "sql-comment" });
+              j = jstop;
+            } else {
+              var jexpr = jc1 === "{";
+              var jsign = (src[j + 2] === "-" || src[j + 2] === "+") ? src[j + 2] : "";
+              out.push({ text: src.slice(j, j + 2 + jsign.length), cls: "sql-jinja" });
+              j = scanJinjaBody(src, j + 2 + jsign.length, out, jexpr);
+            }
+            sBegin = j;
+            continue;
+          }
+          if (cj === "'") { j += 1; break; } // closing quote
+          j += 1;
+        }
+        if (j > sBegin) out.push({ text: src.slice(sBegin, Math.min(j, n)), cls: "sql-string" });
+        i = Math.min(j, n);
+        if (i === sBegin) i += 1; // safety: never stall
+        continue;
+      }
+      // 7. double-quoted identifier "..." -> plaintext (NOT a string).
+      if (c0 === '"') {
+        flushPlain(i);
+        var dj = i + 1;
+        while (dj < n) {
+          if (src[dj] === "\\") { dj += 2; continue; }
+          if (src[dj] === '"') { dj += 1; break; }
+          dj += 1;
+        }
+        out.push({ text: src.slice(i, Math.min(dj, n)), cls: "" });
+        i = Math.min(dj, n);
+        continue;
+      }
+      // 10. identifier -> keyword lookup (case-insensitive) or plaintext.
+      if (/[A-Za-z_]/.test(c0)) {
+        flushPlain(i);
+        var k2 = i + 1;
+        while (k2 < n && isWordChar(src[k2])) k2 += 1;
+        var w = src.slice(i, k2);
+        out.push({ text: w, cls: SQL_KEYWORDS[w.toLowerCase()] ? "sql-keyword" : "" });
+        i = k2;
+        continue;
+      }
+      // 8/9/11. number, multi-char op, single char -> plaintext (coalesced).
+      if (plainStart === -1) plainStart = i;
+      i += 1;
+    }
+    flushPlain(n);
+    return out;
+  }
+
+  // Tokenize YAML. Gap-free over RAW `src`. No jinja nesting (not needed
+  // for the authoring-YAML drawer in v0.x). Strings are matched before the
+  // `#` comment arm so a `#` inside a quoted scalar never opens a comment.
+  function tokenizeYaml(src) {
+    src = String(src);
+    var out = [];
+    var n = src.length;
+    var i = 0;
+    var plainStart = -1;
+    function flushPlain(upTo) {
+      if (plainStart !== -1 && upTo > plainStart) {
+        out.push({ text: src.slice(plainStart, upTo), cls: "" });
+      }
+      plainStart = -1;
+    }
+    // Track whether we are at logical start-of-line content (after optional
+    // indent + a single `- ` list sigil) so the key arm only fires there.
+    var atLineStart = true;     // true right after a newline / at index 0
+    var sawListSigil = false;
+    while (i < n) {
+      var c = src[i];
+      if (c === "\n") {
+        flushPlain(i);
+        out.push({ text: "\n", cls: "" });
+        i += 1;
+        atLineStart = true;
+        sawListSigil = false;
+        continue;
+      }
+      // String scalars (single/double) — matched before the comment arm.
+      if (c === "'" || c === '"') {
+        flushPlain(i);
+        var q = c, j = i + 1;
+        while (j < n) {
+          if (src[j] === "\\") { j += 2; continue; }
+          if (q === "'" && src[j] === "'" && src[j + 1] === "'") { j += 2; continue; }
+          if (src[j] === q) { j += 1; break; }
+          if (src[j] === "\n") break; // YAML scalars don't span our per-line fragments
+          j += 1;
+        }
+        out.push({ text: src.slice(i, Math.min(j, n)), cls: "yaml-string" });
+        i = Math.min(j, n);
+        atLineStart = false;
+        continue;
+      }
+      // # line comment (to EOL).
+      if (c === "#") {
+        flushPlain(i);
+        var nl = src.indexOf("\n", i);
+        var stop = nl === -1 ? n : nl;
+        out.push({ text: src.slice(i, stop), cls: "yaml-comment" });
+        i = stop;
+        atLineStart = false;
+        continue;
+      }
+      // At start-of-line: consume indent, then an optional `- ` list sigil
+      // (which keeps us at start-of-line so `- name:` highlights `name`).
+      if (atLineStart) {
+        if (c === " " || c === "\t") {
+          if (plainStart === -1) plainStart = i;
+          i += 1;
+          continue;
+        }
+        if (!sawListSigil && c === "-" && (src[i + 1] === " " || src[i + 1] === "\t")) {
+          // Emit indent-so-far + the `- ` as plaintext.
+          if (plainStart === -1) plainStart = i;
+          i += 2;
+          // consume following spaces of the sigil run
+          while (i < n && (src[i] === " " || src[i] === "\t")) i += 1;
+          flushPlain(i);
+          sawListSigil = true;
+          continue; // still at line start (key may follow the sigil)
+        }
+        // Identifier followed by `:` -> yaml-key.
+        if (/[A-Za-z_]/.test(c)) {
+          var k = i + 1;
+          while (k < n && /[A-Za-z0-9_\-]/.test(src[k])) k += 1;
+          // peek past optional trailing spaces is NOT done: YAML keys abut `:`.
+          if (src[k] === ":") {
+            flushPlain(i);
+            out.push({ text: src.slice(i, k), cls: "yaml-key" });
+            i = k;
+            atLineStart = false;
+            continue;
+          }
+        }
+        atLineStart = false; // first non-indent, non-key char ends the key zone
+      }
+      // Default: coalesce into a plaintext run.
+      if (plainStart === -1) plainStart = i;
+      i += 1;
+    }
+    flushPlain(n);
+    return out;
+  }
+
+  // Emit a token stream as HTML. RAW slices are escaped here (and ONLY here),
+  // wrapped in a class span unless the token is plaintext (cls === "").
+  function emitTokens(tokens) {
+    var html = "";
+    for (var i = 0; i < tokens.length; i++) {
+      var t = tokens[i];
+      if (t.cls) {
+        html += '<span class="' + t.cls + '">' + escapeHtmlForBody(t.text) + "</span>";
+      } else {
+        html += escapeHtmlForBody(t.text);
+      }
+    }
+    return html;
+  }
+
+  // Thin wrappers: tokenize -> emit. Same observable contract as the old
+  // regex highlighters for context lines, PLUS nested-jinja correctness.
+  function highlightSql(sql) { return emitTokens(tokenizeSql(sql)); }
+  function highlightYaml(yaml) { return emitTokens(tokenizeYaml(yaml)); }
+
+  // cute-dbt#178 — plain (non-diff) code block with a GitHub-style single
+  // line-number gutter. Splits on newlines and tokenizes each line
+  // independently (same per-line contract the diff path already relies on),
+  // wrapping each in a .code-line so a robust numbered gutter renders without
+  // depending on <pre> newline counting. The trailing blank line is trimmed
+  // so we don't show a stray empty numbered row.
+  function highlightLines(text, tokenizeFn) {
+    var src = String(text == null ? "" : text).replace(/\n+$/, "");
+    var rows = src.split("\n");
+    var out = "";
+    for (var i = 0; i < rows.length; i++) {
+      out += '<span class="code-line">'
+           + '<span class="code-gutter" aria-hidden="true">' + (i + 1) + '</span>'
+           + '<span class="diff-code">' + emitTokens(tokenizeFn(rows[i])) + '</span>'
+           + '</span>';
+    }
+    return out;
+  }
+  function highlightLinesSql(sql) { return highlightLines(sql, tokenizeSql); }
+  function highlightLinesYaml(yaml) { return highlightLines(yaml, tokenizeYaml); }
+
+  // Expose for headless JS unit tests (cute-dbt#69 / cute-dbt#132 — parallels
+  // the `__cuteRenderYamlDiff` headless seam).
+  window.__cuteHighlightYaml = highlightYaml;
+  window.__cuteTokenizeSql = tokenizeSql;
+  window.__cuteTokenizeYaml = tokenizeYaml;
+  // cute-dbt#446 (S3) — expose the span↔line resolution for the headless E2E:
+  // which line range a node owns, and which node owns a line (the two halves
+  // of the bidirectional sync). Read the live currentModel internally so the
+  // test drives the same data the handlers do.
+  window.__cuteNodeSpanLines = function (nodeId) {
+    return nodeSpanLines(currentModel(), nodeId);
+  };
+  window.__cuteNodeOwningLine = function (line) {
+    return nodeOwningLine(currentModel(), line);
+  };
+
+  // Overlay the codepoint `emphasis` range [a,b) onto a token stream. Walks
+  // the tokens tracking a running CODEPOINT offset; each token is intersected
+  // with [a,b) and split at the boundary so the in-range pieces sit inside a
+  // <strong> while every piece keeps its syntax class. The range can straddle
+  // token boundaries (e.g. a changed run crossing a keyword into the next
+  // token), so this is a true split — NOT a single-token lookup. Slicing is by
+  // CODEPOINT (Array.from), matching Rust `chars()` offsets.
+  function emitTokensWithEmphasis(tokens, a, b) {
+    var html = "";
+    var pos = 0; // running codepoint offset across the whole line
+    for (var t = 0; t < tokens.length; t++) {
+      var cps = Array.from(tokens[t].text);
+      var cls = tokens[t].cls;
+      var len = cps.length;
+      var start = pos, endp = pos + len;
+      pos = endp;
+      // Within this token, codepoint indices [0,len) map to line offsets
+      // [start, endp). Emphasis covers token-local [lo, hi).
+      var lo = Math.max(a, start) - start;
+      var hi = Math.min(b, endp) - start;
+      function wrap(text, emph) {
+        if (text === "") return "";
+        var esc = escapeHtmlForBody(text);
+        var inner = emph ? "<strong>" + esc + "</strong>" : esc;
+        return cls ? '<span class="' + cls + '">' + inner + "</span>" : inner;
+      }
+      if (hi <= 0 || lo >= len || hi <= lo) {
+        // No overlap with this token.
+        html += wrap(cps.join(""), false);
+      } else {
+        // Split this token at [lo, hi): pre | emphasized | post. Each piece
+        // keeps the token's class; the middle piece is wrapped in <strong>.
+        // To keep CSS-descendant nesting valid and the class on every piece,
+        // emit one span per non-empty piece.
+        html += wrap(cps.slice(0, lo).join(""), false);
+        html += wrap(cps.slice(lo, hi).join(""), true);
+        html += wrap(cps.slice(hi).join(""), false);
+      }
+    }
+    return html;
+  }
+
+  // cute-dbt#96 concern 2 / cute-dbt#111 / cute-dbt#132 — render a
+  // reconstructed inline block diff (domain::pr_diff::BlockDiff) as HTML for a
+  // <pre><code>. Content-agnostic: `tokenizeFn` is a TOKENIZER (tokenizeYaml
+  // for the test drawer, tokenizeSql for the Model SQL section). Each line is a
+  // block-level `.diff-line` span (so a full-width add/remove row tint fills
+  // the line) — block layout provides the breaks, so the spans are joined with
+  // "" (a literal "\n" would render as a blank row between blocks in a <pre>);
+  // each is prefixed by a +/-/space sigil.
+  //
+  // cute-dbt#132: CHANGED lines are now ALSO syntax-highlighted. We tokenize
+  // every line (context and changed), then overlay the intra-line `emphasis`
+  // range as <strong> by SPLITTING the token stream at the codepoint range —
+  // so a changed line shows syntax colors AND the emphasis marker. (Previously
+  // changed lines bypassed highlighting entirely and rendered plain escaped
+  // text.) The offsets are CODEPOINT indices (Rust `chars()`), sliced via
+  // Array.from — UTF-16 `.slice` would drift on astral chars.
+  // cute-dbt#132 — hunk contraction (GitHub-style fold). Long unchanged
+  // stretches collapse behind a click/keyboard "Show N unchanged line(s)"
+  // control. Frontend-only over the existing DiffLine[] payload.
+  //   diffFoldPad     — context lines kept adjacent to a change (head/tail).
+  //                     User-configurable live via the .diff-context-input
+  //                     control (PR-diff mode only); defaults to 3.
+  //                     renderBlockDiff reads it, or an explicit per-call
+  //                     override (the headless tests pass one directly).
+  //   FOLD_MIN_HIDDEN — only fold when >=2 lines would actually hide, so a
+  //                     short YAML test block (a change + a couple context
+  //                     lines) NEVER folds.
+  // The expand-all/collapse-all ops (per-diff fold toggle + the
+  // __cuteExpandAllFolds hooks) are a symmetric DOM mirror (setAllFolds),
+  // NOT a re-render, so they never disturb the SQL File<->Diff view or
+  // mermaid (#136 invariant carried into the #199 step-expansion model).
+  var diffFoldPad = 3;
+  var FOLD_MIN_HIDDEN = 2;
+
+  // Render a single DiffLine to its `.diff-line` span via the shared
+  // tokenize + emphasis-overlay path. `extraClass` appends to the kind class
+  // (the folded middle passes `diff-folded fold-<id>`); `hidden` toggles the
+  // `hidden` attribute (relies on the #122 `[hidden]{display:none!important}`
+  // rule, since Sakura's `.diff-line{display:block}` defeats a bare [hidden]).
+  // cute-dbt#178 — GitHub-style two-column gutter (old line no | new line
+  // no). `null` => blank cell (added lines have no old number; removed have
+  // no new). aria-hidden so screen readers read the code, not the numbering
+  // scaffold.
+  function diffGutter(o, n) {
+    return '<span class="diff-gutter" aria-hidden="true">'
+         + '<span class="dln dln-o">' + (o == null ? "" : o) + '</span>'
+         + '<span class="dln dln-n">' + (n == null ? "" : n) + '</span></span>';
+  }
+
+  // Shared: the inner code HTML for a diff line (syntax + optional word-level
+  // emphasis overlay). Used by both the unified and split renderers.
+  function diffBody(ln, tokenizeFn) {
+    var tokens = tokenizeFn(ln.text);
+    if (ln.kind !== "context" && ln.emphasis) {
+      return emitTokensWithEmphasis(tokens, ln.emphasis[0], ln.emphasis[1]);
+    }
+    return emitTokens(tokens);
+  }
+
+  // Old/new line-number pair for every line, in document order (unified-diff
+  // numbering): added has no old number, removed has no new number.
+  function diffNumbers(lines) {
+    var nums = new Array(lines.length), o = 1, nw = 1;
+    for (var p = 0; p < lines.length; p++) {
+      var kk = lines[p].kind;
+      if (kk === "added")        { nums[p] = { o: null, n: nw++ }; }
+      else if (kk === "removed") { nums[p] = { o: o++,  n: null }; }
+      else                       { nums[p] = { o: o++,  n: nw++ }; }
+    }
+    return nums;
+  }
+
+  function renderOneDiffLine(ln, tokenizeFn, extraClass, hidden, num) {
+    var cls = ln.kind === "added" ? "diff-added"
+            : ln.kind === "removed" ? "diff-removed"
+            : "diff-context";
+    var sigil = ln.kind === "added" ? "+" : ln.kind === "removed" ? "-" : " ";
+    // cute-dbt#419 — carry the old/new line numbers as data attributes so
+    // the PR-comment injector (anchorModelCommentThreads) can find the exact
+    // rendered diff line a review thread anchors to (Right side → new line,
+    // Left side → old line). Decorative positional metadata only (the
+    // .diff-gutter precedent); absent the pr-comments surface they are inert.
+    var oAttr = (num && num.o != null) ? ' data-oldline="' + num.o + '"' : "";
+    var nAttr = (num && num.n != null) ? ' data-newline="' + num.n + '"' : "";
+    return '<span class="diff-line ' + cls + (extraClass ? " " + extraClass : "") + '"'
+         + oAttr + nAttr
+         + (hidden ? " hidden" : "") + '>'
+         + diffGutter(num ? num.o : null, num ? num.n : null)
+         + '<span class="diff-sigil">' + sigil + '</span>'
+         + '<span class="diff-code">' + diffBody(ln, tokenizeFn) + '</span>'
+         + '</span>';
+  }
+
+  function renderBlockDiff(diff, tokenizeFn, padOverride) {
+    // `padOverride` (>= 0) wins over the module-level diffFoldPad — the
+    // headless tests pin specific pads without touching shared state; the
+    // production callers omit it and ride the user-configurable value.
+    var foldPad = (typeof padOverride === "number" && padOverride >= 0)
+      ? padOverride : diffFoldPad;
+    var lines = diff.lines;
+    var n = lines.length;
+    // Precompute the old/new line-number pair for every line, in document
+    // order, so the gutters read like a unified diff regardless of folding.
+    var nums = diffNumbers(lines);
+    var html = "";
+    var foldId = 0; // counter LOCAL to this call — parent-scoping makes it safe.
+    var i = 0;
+    while (i < n) {
+      if (lines[i].kind !== "context") {
+        html += renderOneDiffLine(lines[i], tokenizeFn, "", false, nums[i]);
+        i += 1;
+        continue;
+      }
+      // Maximal run of consecutive context lines: [runStart, j).
+      var runStart = i;
+      var j = i;
+      while (j < n && lines[j].kind === "context") j += 1;
+      var runLen = j - runStart;
+      var head = runStart > 0 ? foldPad : 0;      // change before -> keep PAD on top
+      var tail = j < n ? foldPad : 0;             // change after  -> keep PAD on bottom
+      var hidden = runLen - head - tail;
+      if (hidden < FOLD_MIN_HIDDEN) {
+        // Short run (or short hidden middle): render the whole run normally.
+        for (var k = runStart; k < j; k++) {
+          html += renderOneDiffLine(lines[k], tokenizeFn, "", false, nums[k]);
+        }
+      } else {
+        var id = foldId++;
+        // Reveal direction (#199): a LEADING fold (no change above it, hunk
+        // below) expands UP — lines appear adjacent to the hunk and grow
+        // outward. Otherwise expand DOWN from the top (adjacent to the
+        // preceding hunk).
+        var foldDir = (head === 0 && tail > 0) ? "up" : "down";
+        // head context lines (adjacent to the preceding change).
+        for (var h = runStart; h < runStart + head; h++) {
+          html += renderOneDiffLine(lines[h], tokenizeFn, "", false, nums[h]);
+        }
+        // The fold control. It stays in the DOM across every state, so
+        // `data-fold-count` + a dedicated label span let updateFoldControl/
+        // setAllFolds rebuild the text without re-rendering. `aria-expanded`
+        // tracks fully-revealed vs (partially) collapsed. The gutter carries
+        // the +/− steppers (expand/contract by settings.expandStep); the
+        // far-right "collapse all" appears once anything is revealed.
+        html += '<span class="diff-line diff-fold" data-fold="' + id + '"'
+              + ' data-fold-count="' + hidden + '" data-fold-dir="' + foldDir + '"'
+              + ' role="button" tabindex="0" aria-expanded="false">'
+              + '<span class="diff-gutter diff-gutter-hunk"><span class="fold-steppers"><button type="button" class="fold-expand" aria-label="Show more">+</button><button type="button" class="fold-contract" aria-label="Show fewer">−</button></span></span>'
+              + '<span class="diff-sigil"></span>'
+              + '<span class="diff-fold-label">'
+              + 'Show ' + hidden + ' unchanged line' + (hidden === 1 ? "" : "s")
+              + '</span>'
+              + '<button type="button" class="fold-collapse-all" hidden aria-label="Collapse this whole section">collapse all</button>'
+              + '</span>';
+        // The folded (hidden) middle lines.
+        for (var m2 = runStart + head; m2 < j - tail; m2++) {
+          html += renderOneDiffLine(lines[m2], tokenizeFn, "diff-folded fold-" + id, true, nums[m2]);
+        }
+        // tail context lines (adjacent to the following change).
+        for (var tIdx = j - tail; tIdx < j; tIdx++) {
+          html += renderOneDiffLine(lines[tIdx], tokenizeFn, "", false, nums[tIdx]);
+        }
+      }
+      i = j;
+    }
+    return html;
+  }
+  // Expose for headless JS unit tests (parallels __cuteHighlightYaml). The
+  // #96 name is kept as a thin alias so existing headless assertions and the
+  // YAML call site stay stable; SQL callers pass `tokenizeSql`.
+  function renderYamlDiff(diff) { return renderBlockDiff(diff, tokenizeYaml); }
+  window.__cuteRenderBlockDiff = renderBlockDiff;
+  window.__cuteRenderYamlDiff = renderYamlDiff;
+
+  // cute-dbt#269 — the project panel's hook rows. The Rust side attaches a
+  // BlockDiff of the hook bodies (new side = the manifest's operation.*
+  // nodes when they match the working tree) to each hooks ProjectChange;
+  // this fills the server-rendered row's slot with the SAME #111 renderer
+  // the Model SQL diff uses (renderBlockDiff + tokenizeSql), so hook SQL
+  // gets the full diff treatment: row tints, gutters, intra-line emphasis,
+  // syntax palette. Server-rendered chrome stays intact without JS — the
+  // slot is simply empty. Runs at boot BEFORE the zero-models early return
+  // (a PR can touch only dbt_project.yml).
+  function renderProjectHookDiffs() {
+    var panel = DATA.project_change_panel;
+    if (!panel || panel.kind !== "categorized" || !panel.changes) return;
+    panel.changes.forEach(function (c) {
+      if (c.category !== "hooks" || !c.hook || !c.hook.sql_diff) return;
+      var d = c.hook.sql_diff;
+      if (!d.lines || !d.lines.length) return;
+      var slot = document.querySelector(
+        '.project-def-hook-slot[data-hook-slot="' + c.label + '"]'
+      );
+      if (!slot) return;
+      $(slot).empty().append(
+        $("<pre>").addClass("sql-block project-hook-sql sql-diff-view").append(
+          $("<code>").addClass("project-hook-code").html(renderBlockDiff(d, tokenizeSql))
+        )
+      );
+    });
+  }
+
+  // cute-dbt#350 — the "Data tables" section: each in-scope seed's CURRENT
+  // table (capped, with an honest "showing N of M rows" note) + its old→new
+  // cell-diff (pr-diff arm), reusing the unit-test fixture machinery
+  // wholesale (buildFixtureView → the #98/#127 NULL-aware grids + the
+  // Current↔Diff toggle + the same vendored DataTables). JS-built (no static
+  // markup), so the default HTML carries zero seed bytes — the section is
+  // created only when the gated `DATA.seed_cards` payload is present. A seed
+  // whose data could not be read (`table` null) renders a LABELED
+  // "data unavailable" state, never a silent empty grid (the cute-dbt#126
+  // lesson). The section is inserted once, ABOVE the per-model surfaces, so a
+  // seeds-only PR (zero in-scope models) still shows its seed data.
+  function renderSeedDataTables() {
+    var cards = DATA.seed_cards;
+    if (!cards || !cards.length) return;
+    var $section = $("<section>")
+      .addClass("data-tables-section")
+      .attr("aria-label", "Data tables")
+      .attr("data-testid", "data-tables-section");
+    $section.append($("<h2>").text("Data tables"));
+    $section.append(
+      $("<p>").addClass("data-tables-subtitle").append(
+        document.createTextNode(
+          "Seed CSVs the engine loads as data. "
+        )
+      ).append(
+        $("<span>").addClass("data-tables-experimental")
+          .attr("data-testid", "data-tables-experimental").text("experimental")
+      )
+    );
+    cards.forEach(function (card) {
+      $section.append(buildSeedCard(card));
+    });
+    // Insert above the always-present .error-card landmark (which sits
+    // before the per-model surfaces) so the section lands near the top.
+    var $anchor = $(".error-card").first();
+    if ($anchor.length) {
+      $anchor.before($section);
+    } else {
+      $("body").append($section);
+    }
+    // The seed grids are visible immediately (no toggle gate), so initialize
+    // their DataTables now — initDataTablesIn skips already-hidden tables and
+    // is a no-op for the diff-view's lazy-init Current grid.
+    initDataTablesIn($section);
+  }
+
+  // One seed's card in the "Data tables" section: an identity + lineage
+  // header, the config-display chips, and the fixture-view grid (current
+  // table capped + the optional cell-diff toggle) OR the labeled
+  // data-unavailable state.
+  function buildSeedCard(card) {
+    var $card = $("<div>")
+      .addClass("data-table-card seed-data-card")
+      .attr("data-testid", "seed-data-card")
+      .attr("data-seed", card.name);
+    // Header: the seed name + project-relative path.
+    var $head = $("<div>").addClass("seed-data-head");
+    $head.append($("<code>").addClass("seed-data-name").text(card.name));
+    if (card.original_file_path) {
+      $head.append(
+        $("<code>").addClass("seed-data-path").text(card.original_file_path)
+      );
+    }
+    $card.append($head);
+    // Lineage: the direct downstream models the seed feeds (the immediate
+    // blast radius — never a percentage, just the names).
+    var feeds = card.feeds_models || [];
+    var $lineage = $("<p>")
+      .addClass("seed-data-lineage")
+      .attr("data-testid", "seed-data-lineage");
+    if (feeds.length) {
+      $lineage.append(
+        document.createTextNode(
+          "Feeds " + feeds.length + (feeds.length === 1 ? " model: " : " models: ")
+        )
+      );
+      feeds.forEach(function (name, i) {
+        if (i) $lineage.append(document.createTextNode(", "));
+        $lineage.append($("<code>").text(name));
+      });
+    } else {
+      $lineage.append(
+        document.createTextNode("Feeds no models (no model ref()s this seed).")
+      );
+    }
+    $card.append($lineage);
+    // Config-display chips (delimiter / quote_columns / column_types), each
+    // shown only when authored — the payload carries a display string or null.
+    var $config = buildSeedConfigChips(card);
+    if ($config) $card.append($config);
+    // The data grid OR the truthful degrade.
+    if (!card.table) {
+      $card.append(
+        $("<p>")
+          .addClass("seed-data-unavailable")
+          .attr("data-testid", "seed-data-unavailable")
+          .text(
+            "Data unavailable — the seed CSV could not be read "
+            + "(run with --project-root, and ensure the file exists)."
+          )
+      );
+      return $card;
+    }
+    // The honest "showing N of M rows" note — only when the cap truncated.
+    if (card.capped) {
+      $card.append(
+        $("<p>")
+          .addClass("seed-data-rowcap")
+          .attr("data-testid", "seed-data-rowcap")
+          .attr("data-shown", card.shown_rows)
+          .attr("data-total", card.total_rows)
+          .text(
+            "Showing " + card.shown_rows + " of " + card.total_rows
+            + " rows (raise [seeds] row_cap to show more)."
+          )
+      );
+    } else {
+      $card.append(
+        $("<p>")
+          .addClass("seed-data-rowcount")
+          .attr("data-testid", "seed-data-rowcount")
+          .attr("data-total", card.total_rows)
+          .text(
+            card.total_rows + (card.total_rows === 1 ? " row" : " rows")
+          )
+      );
+    }
+    // The grid: reuse the unit-test fixture machinery wholesale — the
+    // Current table (given-table class so the shared DataTables init + the
+    // reflow path pick it up) plus, on the pr-diff arm, the cell-diff toggle.
+    // `card.name` is the column-tooltip owner; no per-column metadata map for
+    // seeds (the #240 truthful fallback names the seed).
+    $card.append(buildFixtureView(card.table, "given-table", card.diff, null, card.name));
+    return $card;
+  }
+
+  // The seed's config-display chips, or null when the seed authored none.
+  // Each value is a Rust-composed display string (or null); the JS only
+  // renders, every interpolation through jQuery .text() (never parsed as
+  // HTML — a hostile config value draws as a glyph).
+  function buildSeedConfigChips(card) {
+    var rows = [
+      ["delimiter", card.delimiter],
+      ["quote_columns", card.quote_columns],
+      ["column_types", card.column_types]
+    ].filter(function (r) { return r[1] != null; });
+    if (!rows.length) return null;
+    var $wrap = $("<p>")
+      .addClass("seed-data-config")
+      .attr("data-testid", "seed-data-config");
+    rows.forEach(function (r) {
+      $wrap.append(
+        $("<span>").addClass("seed-config-chip")
+          .attr("data-key", r[0])
+          .append($("<code>").addClass("seed-config-key").text(r[0]))
+          .append(document.createTextNode(": "))
+          .append($("<span>").addClass("seed-config-val").text(String(r[1])))
+      );
+    });
+    return $wrap;
+  }
+
+  // cute-dbt#178 / cute-dbt#199 — SPLIT (side-by-side) diff. Old lines on
+  // the left, new on the right, paired index-wise within each change block;
+  // context appears on both sides. Long context runs fold with the SAME
+  // hunk model + fold control as the unified renderer (fold row with the
+  // gutter steppers + a colspan label cell), so setAllFolds and the per-diff
+  // fold toggle drive both layouts from one control set. Rendered as a
+  // fixed-layout table (the <colgroup> pins 3.8em number columns + auto code
+  // columns for a true 50/50) so the two halves stay aligned; each code cell
+  // soft-wraps. The CSS picks unified vs split per html[data-difflayout]
+  // (+ a responsive fallback), so BOTH are emitted and exactly one is shown.
+  // Consumes the same BlockDiff lines verbatim — the Rust diff engine is
+  // unchanged.
+  // cute-dbt#251 — the split-view line-number cells carry aria-hidden in
+  // PARITY with the unified .diff-gutter (decorative positional metadata;
+  // screen readers read the code, not the numbering — the #178 idiom).
+  function splitCell(side) {
+    if (!side) {
+      return '<td class="ds-num ds-empty" aria-hidden="true"></td><td class="ds-code ds-empty"></td>';
+    }
+    return '<td class="ds-num ds-' + side.cls + '" aria-hidden="true">' + (side.num == null ? "" : side.num) + '</td>'
+         + '<td class="ds-code ds-' + side.cls + '"><div class="ds-codewrap">'
+         + '<span class="diff-sigil">' + side.sigil + '</span>'
+         + '<span class="diff-code">' + side.body + '</span></div></td>';
+  }
+  function renderSplitDiff(diff, tokenizeFn, padOverride) {
+    var foldPad = (typeof padOverride === "number" && padOverride >= 0) ? padOverride : diffFoldPad;
+    var lines = diff.lines, n = lines.length, nums = diffNumbers(lines);
+    var rows = "", i = 0, foldId = 0;
+    function ctxRow(idx, extraClass, hidden) {
+      var body = diffBody(lines[idx], tokenizeFn);
+      return '<tr class="ds-row' + (extraClass ? " " + extraClass : "") + '"' + (hidden ? " hidden" : "") + '>'
+        + splitCell({ cls: "context", num: nums[idx].o, sigil: " ", body: body })
+        + splitCell({ cls: "context", num: nums[idx].n, sigil: " ", body: body })
+        + '</tr>';
+    }
+    while (i < n) {
+      if (lines[i].kind === "context") {
+        // Fold long context runs just like the unified renderer (same run
+        // arithmetic, same control anatomy, same parent-scoped fold ids —
+        // here the <tbody> is the scope).
+        var runStart = i, j = i;
+        while (j < n && lines[j].kind === "context") j += 1;
+        var runLen = j - runStart;
+        var head = runStart > 0 ? foldPad : 0;
+        var tail = j < n ? foldPad : 0;
+        var hiddenN = runLen - head - tail;
+        if (hiddenN < FOLD_MIN_HIDDEN) {
+          for (var k = runStart; k < j; k++) rows += ctxRow(k, "", false);
+        } else {
+          var id = foldId++;
+          var foldDir = (head === 0 && tail > 0) ? "up" : "down";
+          for (var h = runStart; h < runStart + head; h++) rows += ctxRow(h, "", false);
+          rows += '<tr class="ds-row diff-fold" data-fold="' + id + '" data-fold-count="' + hiddenN + '" data-fold-dir="' + foldDir + '"'
+                + ' role="button" tabindex="0" aria-expanded="false">'
+                + '<td class="ds-fold-gutter"><span class="fold-steppers"><button type="button" class="fold-expand" aria-label="Show more">+</button><button type="button" class="fold-contract" aria-label="Show fewer">−</button></span></td>'
+                + '<td class="ds-fold-cell" colspan="3">'
+                + '<div class="ds-fold-inner">'
+                + '<span class="diff-fold-label">Show ' + hiddenN + ' unchanged line' + (hiddenN === 1 ? "" : "s") + '</span>'
+                + '<button type="button" class="fold-collapse-all" hidden aria-label="Collapse this whole section">collapse all</button>'
+                + '</div>'
+                + '</td></tr>';
+          for (var m2 = runStart + head; m2 < j - tail; m2++) rows += ctxRow(m2, "diff-folded fold-" + id, true);
+          for (var tIdx = j - tail; tIdx < j; tIdx++) rows += ctxRow(tIdx, "", false);
+        }
+        i = j;
+        continue;
+      }
+      // Maximal change block; pair removed (left) with added (right).
+      var rem = [], add = [];
+      while (i < n && lines[i].kind !== "context") {
+        if (lines[i].kind === "removed") rem.push(i); else add.push(i);
+        i += 1;
+      }
+      var maxL = Math.max(rem.length, add.length);
+      for (var x = 0; x < maxL; x++) {
+        var L = x < rem.length
+          ? { cls: "removed", num: nums[rem[x]].o, sigil: "-", body: diffBody(lines[rem[x]], tokenizeFn) } : null;
+        var R = x < add.length
+          ? { cls: "added", num: nums[add[x]].n, sigil: "+", body: diffBody(lines[add[x]], tokenizeFn) } : null;
+        rows += '<tr class="ds-row">' + splitCell(L) + splitCell(R) + '</tr>';
+      }
+    }
+    return '<table class="diff-split">'
+      + '<colgroup><col class="ds-c-num"><col class="ds-c-code"><col class="ds-c-num"><col class="ds-c-code"></colgroup>'
+      + '<tbody>' + rows + '</tbody></table>';
+  }
+
+  // Emit BOTH layouts into a diff <pre>: the unified <code> + the split
+  // table. CSS shows exactly one (html[data-difflayout] + the responsive
+  // fallback). `codeClass` lets the Model-SQL caller keep its hook class.
+  function diffViewsHtml(diff, tokenizeFn, codeClass) {
+    return '<code class="diff-unified' + (codeClass ? " " + codeClass : "") + '">'
+         + renderBlockDiff(diff, tokenizeFn) + '</code>'
+         + renderSplitDiff(diff, tokenizeFn);
+  }
+  window.__cuteRenderSplitDiff = renderSplitDiff;
+
+  // cute-dbt#132 / cute-dbt#199 — global fold ops. setAllFolds is a
+  // SYMMETRIC DOM MIRROR over the already-rendered diff: expand reveals every
+  // folded middle line; collapse re-hides them — exactly reproducing the
+  // renderers' default-folded output WITHOUT a re-render, so the SQL
+  // File<->Diff toggle, mermaid, and scroll position are never disturbed.
+  // The per-hunk controls stay VISIBLE in every state (the #136 invariant)
+  // and updateFoldControl recomputes their full anatomy (label, aria-expanded,
+  // stepper disabled-states, collapse-all visibility, parked position), so an
+  // individual hunk can still be stepped after a global op. `root` scopes the
+  // op (the per-diff fold toggle passes its diff <pre>; the headless tests
+  // pass a mounted node). Drives BOTH layouts: unified `.fold-<id>` spans in
+  // a <code>, split fold rows in a <tbody>.
+  function setAllFolds(expanded, root) {
+    var scope = root || document;
+    $(scope).find(".diff-fold").each(function () {
+      var $ctrl = $(this);
+      var id = $ctrl.attr("data-fold");
+      var $allFolded = $ctrl.closest("code, tbody").find(".fold-" + id);
+      $allFolded.prop("hidden", !expanded);
+      updateFoldControl($ctrl, $allFolded);
+    });
+  }
+  // Recompute a fold control's label / collapse affordance / stepper states /
+  // parked position from the current hidden-state of its lines. Shared by
+  // expand, contract, collapse, and the global expand/collapse-all mirror so
+  // they never drift.
+  function updateFoldControl($ctrl, $allFolded) {
+    var total = $allFolded.length;
+    var stillHidden = $allFolded.filter(function () { return this.hidden; }).length;
+    var revealed = total - stillHidden;
+    var fully = stillHidden <= 0;
+    $ctrl.attr("aria-expanded", fully ? "true" : "false");
+    $ctrl.toggleClass("is-fully-expanded", fully);
+    var $label = $ctrl.find(".diff-fold-label");
+    $label.text(fully ? "All " + total + " lines shown" : "Show " + stillHidden + " unchanged line" + (stillHidden === 1 ? "" : "s"));
+    // gutter steppers: + expands (disabled when nothing left to show), −
+    // contracts (disabled when nothing revealed yet).
+    $ctrl.find(".fold-expand").prop("disabled", fully);
+    $ctrl.find(".fold-contract").prop("disabled", revealed <= 0);
+    // far-right "collapse all" appears once anything in this hunk is revealed.
+    $ctrl.find(".fold-collapse-all").prop("hidden", revealed <= 0);
+    // park the control adjacent to what it acts on: just above the remaining
+    // hidden run while partial, or below the whole run once fully revealed.
+    if (fully) $ctrl.insertAfter($allFolded.last());
+    else $ctrl.insertBefore($allFolded.filter(function () { return this.hidden; }).first());
+    // Every fold mutation funnels through here — keep the per-diff fold
+    // toggles truthful too (Gemini, PR #213).
+    syncFoldToggles();
+  }
+  // Relabel every mounted per-diff fold toggle from ITS OWN diff's DOM
+  // truth: pressed ("Collapse all") iff the diff has folds and none remain
+  // hidden. Each button reads the root accessor buildFoldToggleBtn stored on
+  // it, so per-hunk steppers, the per-hunk collapse-all, AND the global
+  // __cuteExpandAllFolds/__cuteCollapseAllFolds hooks all keep the button
+  // honest — no cached state anywhere. (Per-root truth is deliberately
+  // stronger than relabeling "toggles in scope" to one shared state: a
+  // partial-scope op never lies about an untouched sibling diff.)
+  function syncFoldToggles() {
+    $(".diff-fold-toggle").each(function () {
+      var getRoot = this.__cuteFoldRoot;
+      if (!getRoot) return;
+      var $folded = $(getRoot()).find(".diff-folded");
+      if (!$folded.length) return; // fold-less diff: leave the rest label.
+      var anyHidden = $folded.filter(function () { return this.hidden; }).length > 0;
+      $(this).text(anyHidden ? "Expand all" : "Collapse all")
+        .attr("aria-pressed", anyHidden ? "false" : "true");
+    });
+  }
+  // Expand by one step. settings.expandStep is read live on every activation
+  // (0 = reveal all), so a settings change needs NO re-render. "up" folds
+  // (hunk below) reveal from the BOTTOM of the hidden run so the newly-shown
+  // lines sit next to the hunk and the gap grows outward.
+  function expandFold($ctrl) {
+    var id = $ctrl.attr("data-fold");
+    var $allFolded = $ctrl.closest("code, tbody").find(".fold-" + id);
+    var $hidden = $allFolded.filter(function () { return this.hidden; });
+    var remaining = $hidden.length;
+    if (remaining === 0) return;
+    var step = settings.expandStep;                 // 0 => all
+    var reveal = (step <= 0 || step >= remaining) ? remaining : step;
+    if ($ctrl.attr("data-fold-dir") === "up") {
+      $hidden.slice(remaining - reveal).prop("hidden", false);
+    } else {
+      $hidden.slice(0, reveal).prop("hidden", false);
+    }
+    updateFoldControl($ctrl, $allFolded);
+  }
+  // Contract by one step — re-hide the most-recently-revealed lines (those
+  // farthest from the hunk), mirroring expandFold's direction.
+  function contractFold($ctrl) {
+    var id = $ctrl.attr("data-fold");
+    var $allFolded = $ctrl.closest("code, tbody").find(".fold-" + id);
+    var $visible = $allFolded.filter(function () { return !this.hidden; });
+    var shown = $visible.length;
+    if (shown === 0) return;
+    var step = settings.expandStep;                 // 0 => collapse all
+    var hide = (step <= 0 || step >= shown) ? shown : step;
+    if ($ctrl.attr("data-fold-dir") === "up") {
+      // revealed grew upward from the bottom => hide from the TOP of visible.
+      $visible.slice(0, hide).prop("hidden", true);
+    } else {
+      // revealed grew downward from the top => hide from the BOTTOM of visible.
+      $visible.slice(shown - hide).prop("hidden", true);
+    }
+    updateFoldControl($ctrl, $allFolded);
+  }
+  // Restore this hunk's fully-folded state (the far-right "collapse all").
+  function collapseFold($ctrl) {
+    var id = $ctrl.attr("data-fold");
+    var $allFolded = $ctrl.closest("code, tbody").find(".fold-" + id);
+    $allFolded.prop("hidden", true);
+    updateFoldControl($ctrl, $allFolded);
+  }
+  window.__cuteExpandAllFolds = function (root) { setAllFolds(true, root); };
+  window.__cuteCollapseAllFolds = function (root) { setAllFolds(false, root); };
+
+  // cute-dbt#139 — wire the settings cog + panel. PR-diff mode only; returns
+  // early in baseline mode where the cog is not emitted. The cog toggles a
+  // non-blocking panel (Escape + outside-click close, focus returns to the
+  // cog). The settings drive PURE presentation:
+  //   contextLines  -> diffFoldPad, then renderForSelectedModel() re-folds the
+  //                    live diffs (the same re-render path #132's input used).
+  //   expandStep    -> lines revealed per gutter-fold activation (0 = all);
+  //                    read live by expandFold/contractFold, so NO re-render.
+  //   normalize     -> flips the cell lens (cellChanged); a re-render re-tints
+  //                    the cell-diff grids under the new lens.
+  // All persist via saveSettings (localStorage where available; in-memory
+  // under file://). The controls hydrate to the loaded settings at bind time.
+  function bindSettingsMenu() {
+    var $cog = $(".settings-cog");
+    if (!$cog.length) return;
+    var $panel = $("#settings-panel");
+    var $ctx = $("#settings-context-input");
+    var $expand = $("#settings-expand-step");
+    var $norm = $("#settings-normalize-input");
+
+    // Hydrate the controls from the (possibly persisted) settings.
+    $ctx.val(settings.contextLines);
+    $expand.val(settings.expandStep);
+    $norm.prop("checked", settings.normalizeEquality);
+
+    function setOpen(open) {
+      $panel.prop("hidden", !open);
+      $cog.attr("aria-expanded", open ? "true" : "false");
+    }
+    $cog.on("click", function () {
+      setOpen($cog.attr("aria-expanded") !== "true");
+    });
+    // Escape closes (from anywhere) and returns focus to the cog. Outside-click
+    // closes too; clicks inside the panel/cog are ignored.
+    $(document).on("keydown", function (e) {
+      if ((e.key === "Escape" || e.key === "Esc") && $cog.attr("aria-expanded") === "true") {
+        setOpen(false);
+        $cog.trigger("focus");
+      }
+    });
+    $(document).on("click", function (e) {
+      if ($cog.attr("aria-expanded") !== "true") return;
+      if ($(e.target).closest(".settings-cog-wrap").length) return;
+      setOpen(false);
+    });
+
+    // Context-lines: clamp to [0,20], re-fold the live diffs, persist.
+    $ctx.on("change", function () {
+      var v = parseInt($(this).val(), 10);
+      if (isNaN(v)) { $(this).val(settings.contextLines); return; }
+      settings.contextLines = Math.max(0, Math.min(20, v));
+      diffFoldPad = settings.contextLines;
+      $(this).val(settings.contextLines);
+      saveSettings();
+      renderForSelectedModel();
+    });
+    // Expand-step: lines revealed per gutter-fold activation (0 = all). NO
+    // re-render — expandFold/contractFold read settings.expandStep live on
+    // each activation. NaN input restores the current value.
+    $expand.on("change", function () {
+      var v = parseInt($(this).val(), 10);
+      if (isNaN(v)) { $(this).val(settings.expandStep); return; }
+      settings.expandStep = Math.max(0, Math.min(500, v));
+      $(this).val(settings.expandStep);
+      saveSettings();
+    });
+    // Normalize-equality: flip the cell lens, re-render to re-tint, persist.
+    $norm.on("change", function () {
+      settings.normalizeEquality = $(this).prop("checked");
+      saveSettings();
+      renderForSelectedModel();
+    });
+  }
+
+  function copySql(text, $btn) {
+    function done(ok) {
+      $btn.addClass(ok ? "copied" : "copy-failed").text(ok ? "Copied" : "Failed");
+      setTimeout(function () {
+        $btn.removeClass("copied copy-failed").text("Copy");
+      }, 1500);
+    }
+    if (navigator.clipboard && navigator.clipboard.writeText) {
+      navigator.clipboard.writeText(text).then(function () { done(true); }, function () {
+        fallbackCopy(text, done);
+      });
+    } else {
+      fallbackCopy(text, done);
+    }
+  }
+  function fallbackCopy(text, done) {
+    var ta = document.createElement("textarea");
+    ta.value = text;
+    ta.setAttribute("readonly", "");
+    ta.style.position = "fixed";
+    ta.style.opacity = "0";
+    document.body.appendChild(ta);
+    ta.select();
+    var ok = false;
+    try { ok = document.execCommand("copy"); } catch (e) { ok = false; }
+    document.body.removeChild(ta);
+    done(ok);
+  }
+
+  // The Current-view grid, rendered PURELY from the Rust-computed
+  // `FixtureTable` POD (cute-dbt#138). Each cell is a `{display, key}` Cell:
+  // the text is the authored `display` (so a csv `1.00` shows `1.00`, not the
+  // canonicalized `1`), while the numeric / NULL styling + sort order come
+  // from the canonical `key` (a `Number` key sorts numerically even when its
+  // display is `1.00`). The JS no longer parses csv/dict — the domain's
+  // `table_from_manifest_rows` owns that, and ships this POD on the wire.
+  // cute-dbt#165 / cute-dbt#178 — column-header tooltips, the handoff spec:
+  // the WHOLE header cell is the hover/focus trigger (no per-column icon).
+  // decorateColHeader marks a metadata-bearing th with `.has-col-meta` +
+  // data-attrs; showColTip fills + positions a single body-appended
+  // `#col-tooltip` bubble (position:fixed, so the .table-fit overflow
+  // scroller never clips it; horizontally clamped; flips above on bottom
+  // overflow). Data source: OUR shipped per-table `column_meta` payload
+  // ({description, tests:[{name, values, detail}]}) — Rust computes the
+  // README §2.2 display mapping, the JS only renders. Every interpolation
+  // below passes through escapeHtml, so payload strings never parse as HTML.
+  //
+  // A11y (the #146/#166 contract carried forward): a bare th is not
+  // focusable, so decorated headers gain tabindex="0" (keyboard users reach
+  // the tip via focusin); the th carries an aria-label summarizing the same
+  // content (the bubble is aria-hidden so it is never announced twice);
+  // hover AND focus both reveal. The native `title` is REMOVED from
+  // decorated headers — it would double-show over the bubble.
+  function colTestSummary(t) {
+    var extra = (t.values && t.values.length) ? " " + t.values.join(", ")
+              : t.detail ? " " + t.detail : "";
+    return t.name + extra;
+  }
+  // One test row in the shared tooltip anatomy: accent `.ct-key` name +
+  // one `.ct-val` chip per argument. cute-dbt#202 — a multi-arg detail
+  // splits into one chip per argument (the ct-vals/ct-val dress the
+  // values branch uses; the ct-detail run is retired); the shipped
+  // single-value detail forms — relationships `m.f`, accepted_range
+  // `0–100` — carry no comma, so they stay one chip. cute-dbt#235 —
+  // shared by the column tooltip AND the model-ref tip's model-test
+  // rows, so the two bubbles cannot drift apart visually. Every
+  // interpolation passes through escapeHtml (payload strings never
+  // parse as HTML).
+  function ctTestHtml(t) {
+    var inner = '<span class="ct-key">' + escapeHtml(t.name) + "</span>";
+    if (t.values && t.values.length) {
+      inner += '<span class="ct-vals">'
+        + t.values.map(function (v) { return '<span class="ct-val">' + escapeHtml(String(v)) + "</span>"; }).join("")
+        + "</span>";
+    } else if (t.detail) {
+      inner += '<span class="ct-vals">'
+        + String(t.detail).split(/,\s*/).map(function (a) {
+            return '<span class="ct-val">' + escapeHtml(a) + "</span>";
+          }).join("")
+        + "</span>";
+    }
+    return '<div class="ct-test">' + inner + "</div>";
+  }
+  // cute-dbt#240 (defect B, third report) — EVERY fixture column header is
+  // a tooltip trigger now. The #165/#178 "no metadata ⇒ no affordance"
+  // posture made undeclared columns silently hover-dead; on real projects
+  // (staging models declare a couple of columns, target models declare
+  // all) that meant MOST given headers showed nothing while every expected
+  // header answered — read three times by the founder as "given tooltips
+  // are broken". A metadata-less header now reveals a truthful fallback
+  // ("no description or data tests declared …") naming the owning node, so
+  // hover always answers and the honesty invariant strengthens: never an
+  // empty bubble, and no dead headers either. `owner` is the human name of
+  // the node that owns the columns (input model / seed / source for a
+  // given, the target model for `this` and the expected table).
+  function decorateColHeader($th, name, meta, owner) {
+    var m = meta || {};
+    var tests = m.tests || [];
+    // PR #244 review — trim ONCE here (the single writer of
+    // data-col-desc): a whitespace-only authored description must not
+    // count as metadata, or it would suppress the truthful fallback and
+    // open an effectively-empty bubble on a description-only column —
+    // exactly the never-empty-bubble contract this fix exists to keep.
+    var desc = (m.description || "").trim();
+    var hasMeta = Boolean(desc || tests.length);
+    var parts = ["Column " + name];
+    if (desc) parts.push(desc);
+    if (tests.length) parts.push("column tests: " + tests.map(colTestSummary).join("; "));
+    if (!hasMeta) {
+      parts.push("no description or data tests declared"
+        + (owner ? " on " + owner : "") + " in the project YAML");
+    }
+    $th.removeAttr("title")
+      .addClass("has-col-meta")
+      .toggleClass("col-meta-empty", !hasMeta)
+      .attr("tabindex", "0")
+      .attr("aria-label", parts.join(" — "))
+      .attr("data-col-name", name)
+      .attr("data-col-desc", desc)
+      .attr("data-col-owner", owner || "")
+      .attr("data-col-tests", JSON.stringify(tests));
+  }
+  // The singleton bubble, lazily appended to <body> (one element serves
+  // every header). aria-hidden: the th aria-label is the AT surface.
+  function ensureColTooltip() {
+    var el = document.getElementById("col-tooltip");
+    if (!el) {
+      el = document.createElement("div");
+      el.id = "col-tooltip"; el.className = "col-tooltip"; el.hidden = true;
+      el.setAttribute("aria-hidden", "true");
+      document.body.appendChild(el);
+    }
+    return el;
+  }
+  function showColTip(trigger) {
+    var el = ensureColTooltip();
+    var name = trigger.getAttribute("data-col-name");
+    var desc = trigger.getAttribute("data-col-desc");
+    var owner = trigger.getAttribute("data-col-owner");
+    var tests = [];
+    try { tests = JSON.parse(trigger.getAttribute("data-col-tests") || "[]"); } catch (e) { tests = []; }
+    var h = '<div class="ct-desc">' + escapeHtml(desc || name) + "</div>";
+    if (tests.length) {
+      h += '<div class="ct-tests-label">Data tests</div><div class="ct-tests">'
+         + tests.map(ctTestHtml).join("")
+         + "</div>";
+    }
+    // cute-dbt#240 (defect B) — the truthful no-metadata fallback: name the
+    // column and say plainly that the project YAML declares nothing for it
+    // on the owning node. Never an empty bubble; never a dead header.
+    if (!desc && !tests.length) {
+      h += '<div class="ct-empty">No description or data tests declared'
+         + (owner ? " on " + escapeHtml(owner) : "") + " in the project YAML.</div>";
+    }
+    el.innerHTML = h;
+    el.hidden = false;
+    positionTipNear(el, trigger);
+  }
+  // Shared fixed-position clamp for the singleton bubble: below the
+  // trigger, horizontally clamped, flipping above on bottom overflow.
+  function positionTipNear(el, trigger) {
+    // cute-dbt#232 (audit D18) — reset the previous show's left BEFORE
+    // measuring: a stale left near the viewport edge squeezes the layout,
+    // so offsetWidth under-reports and the clamp below overshoots.
+    el.style.left = "0px";
+    // cute-dbt#240 (defect D) — clamp against documentElement.clientWidth,
+    // not window.innerWidth: innerWidth includes a classic scrollbar's
+    // gutter, so an innerWidth-based clamp can park the bubble's right edge
+    // under the scrollbar — visually clipped at the screen edge. The CSS
+    // side caps every singleton's max-width at calc(100vw - 16px) so the
+    // measured box below can never exceed the clamping range.
+    var vw = document.documentElement.clientWidth || window.innerWidth;
+    var vh = document.documentElement.clientHeight || window.innerHeight;
+    var r = trigger.getBoundingClientRect();
+    var tw = el.offsetWidth, th = el.offsetHeight;
+    var left = r.left;
+    if (left + tw > vw - 8) left = vw - 8 - tw;
+    if (left < 8) left = 8;
+    var top = r.bottom + 6;
+    if (top + th > vh - 8) top = r.top - 6 - th; // flip above
+    // PR #244 review / cute-dbt#246 — post-flip top clamp: at pathological
+    // viewport heights the flipped position (trigger top − bubble height)
+    // goes negative, pushing the bubble above the screen. Clamp to the
+    // 8px gutter (mirrors the horizontal clamp; the bubble may then
+    // overlap the trigger — contained beats clipped).
+    if (top < 8) top = 8;
+    el.style.left = Math.round(left) + "px";
+    el.style.top = Math.round(top) + "px";
+  }
+  // cute-dbt#170 — plain-text variant of the singleton bubble for the
+  // findings-panel tier chips (`.has-finding-tip` + data-tip). Same
+  // element, same positioning, same hover-AND-focus reveal contract.
+  function showPlainTip(trigger) {
+    var el = ensureColTooltip();
+    el.innerHTML = '<div class="ct-desc">' + escapeHtml(trigger.getAttribute("data-tip") || "") + "</div>";
+    el.hidden = false;
+    positionTipNear(el, trigger);
+  }
+  function hideColTip() { var el = document.getElementById("col-tooltip"); if (el) el.hidden = true; }
+  function bindColTooltips() {
+    $(document)
+      .on("mouseenter focusin", ".has-col-meta", function () { showColTip(this); })
+      .on("mouseleave focusout", ".has-col-meta", function () { hideColTip(); })
+      .on("mouseenter focusin", ".has-finding-tip", function () { showPlainTip(this); })
+      .on("mouseleave focusout", ".has-finding-tip", function () { hideColTip(); });
+  }
+
+  // `colMeta` (cute-dbt#165) is the per-table column-metadata map keyed by
+  // column name (absent key/entry ⇒ the cute-dbt#240 truthful fallback
+  // bubble naming `owner`, never a dead header).
+  function buildTable(table, cls, colMeta, owner) {
+    var columns = (table && table.columns) || [];
+    var rows = (table && table.rows) || [];
+    var $wrap = $("<div>").addClass("table-fit");
+    var $tbl = $("<table>").addClass(cls);
+    var $thead = $("<thead>").appendTo($tbl);
+    var $hr = $("<tr>").appendTo($thead);
+    columns.forEach(function (c) {
+      var html = escapeHtml(c).replace(/_/g, "_<wbr>");
+      var $th = $("<th>").attr("title", c).html(html);
+      // cute-dbt#178 — decorate (and de-title) the th; the header cell
+      // itself is the tooltip trigger (cute-dbt#240: every th, fallback
+      // content when the manifest carries no metadata for the column).
+      decorateColHeader($th, c, colMeta && colMeta[c], owner);
+      $hr.append($th);
+    });
+    var $tbody = $("<tbody>").appendTo($tbl);
+    rows.forEach(function (r) {
+      var $tr = $("<tr>").appendTo($tbody);
+      (r.cells || []).forEach(function (cell) {
+        $tr.append(decorateCurrentCell($("<td>"), cell));
+      });
+    });
+    $wrap.append($tbl);
+    return $wrap;
+  }
+
+  // Render one Current-view Cell (`{display, key}`) into a <td>: the text is
+  // the authored `display`; the styling/sort affordance is read from `key.t`
+  // (the canonical type), never from the display token (cute-dbt#138). Shares
+  // the null/number styling with `decorateValueCell` so Current and Diff read
+  // identically.
+  function decorateCurrentCell($td, cell) {
+    var key = cell && cell.key;
+    if (!key || key.t === "null") {
+      return $td.addClass("cell-null").text("NULL");
+    }
+    if (key.t === "absent") {
+      return $td.addClass("cell-absent").text("");
+    }
+    if (key.t === "number") {
+      // Sort numerically on the canonical value; show the authored display.
+      return $td.addClass("cell-num").attr("data-order", key.v).text(cell.display);
+    }
+    return $td.text(cell.display);
+  }
+
+  // cute-dbt#98 ----------------------------------------------------------
+  //
+  // The structured cell-diff renderer + the per-table Current↔Diff toggle.
+  // Mirrors the #96 Authored↔Diff drawer idiom (a `.cell-diff-toggle` of two
+  // `.yaml-view-btn`s, `data-view`, `.active`, and `prop("hidden", …)` show/
+  // hide reusing the #121 `[hidden]{display:none!important}` rule). When the
+  // test carries no cell diff for this table the view is the plain Current
+  // grid — byte-identical to the historic `buildTable` output, so baseline-
+  // mode reports never drift.
+
+  // True when the fixture's data lives in an external file: dbt's `fixture:`
+  // key is set AND `rows` is null (the manifest does NOT inline the data).
+  function isExternalFixture(obj) {
+    return Boolean(obj && obj.fixture && (obj.rows === null || obj.rows === undefined));
+  }
+
+  // cute-dbt#126 — the fixture data lives in an external file AND was LOADED
+  // (the reader inlined it, so `rows` is the file body). The complement of
+  // isExternalFixture over a `fixture`-bearing obj: loaded → render the grid /
+  // sql code block + a provenance chip; unloaded (unreadable) → the affordance.
+  function isLoadedExternalFixture(obj) {
+    return Boolean(obj && obj.fixture && obj.rows !== null && obj.rows !== undefined);
+  }
+
+  // A small "from <path>" chip noting the fixture's external source, shown
+  // alongside the loaded grid / sql code block. Inline fixtures omit `fixture`
+  // so this never renders for them (byte-identity of existing reports holds).
+  function buildFixtureProvenanceChip(name) {
+    return $("<span>").addClass("fixture-provenance")
+      .append(document.createTextNode("from "))
+      .append($("<code>").text(String(name)));
+  }
+
+  // The external-fixture affordance: never a silently-empty grid. Names the
+  // file and points the reader at the Unit test YAML drawer (the #96 view),
+  // which surfaces the authored block including the `fixture:` line.
+  // (Pointer copy follows the cute-dbt#247 truthful drawer rename.)
+  function buildExternalFixtureAffordance(name) {
+    var $box = $("<div>").addClass("given-empty external-fixture-note");
+    $box.append(document.createTextNode("data in external fixture file: "));
+    $box.append($("<code>").addClass("td-defined-in").text(String(name)));
+    $box.append(document.createTextNode(" — see the Unit test YAML drawer above for this test's block."));
+    return $box;
+  }
+
+  // A FixtureTable's cell view: build the Current grid, and — when a cell diff
+  // is present — also build the Diff grid and a Current↔Diff toggle that flips
+  // exactly one view visible at a time (the inactive view carries `hidden`).
+  // Default-Diff when a diff exists, else Current with no toggle.
+  function buildFixtureView(table, cls, diff, colMeta, owner) {
+    if (!diff) {
+      // No cell diff for this table → the plain Current grid from the POD.
+      return buildTable(table, cls, colMeta, owner);
+    }
+    var $wrap = $("<div>").addClass("fixture-view");
+    var $diffView = buildDiffTable(diff, cls, colMeta, owner);
+    var $currentView = buildTable(table, cls, colMeta, owner).prop("hidden", true);
+
+    var $toggle = $("<div>").addClass("cell-diff-toggle");
+    var $diffBtn = $("<button>").attr("type", "button")
+      .addClass("yaml-view-btn active").attr("data-view", "diff").text("Diff");
+    // cute-dbt#178 — labelled "File" for consistency with the other
+    // Diff/File toggles (the data-view stays "current": it is the test's
+    // current fixture data as authored in the file).
+    var $curBtn = $("<button>").attr("type", "button")
+      .addClass("yaml-view-btn").attr("data-view", "current").text("File");
+    $toggle.append($diffBtn).append($curBtn);
+    // Bind to the LOCAL $toggle (not a global `.yaml-view-btn` delegate) so it
+    // can never catch the #96/#111 drawer toggles elsewhere in the document.
+    $toggle.on("click", ".yaml-view-btn", function () {
+      var view = $(this).attr("data-view");
+      $toggle.find(".yaml-view-btn").removeClass("active");
+      $(this).addClass("active");
+      $diffView.prop("hidden", view !== "diff");
+      $currentView.prop("hidden", view !== "current");
+      // Lazy-init + reflow the table just revealed: a DataTable initialized
+      // while `display:none` measures column widths against a 0-width box, so
+      // the Current grid is initialized only on first reveal and any already-
+      // initialized table gets its widths recomputed once it is on-screen.
+      var $shown = view === "diff" ? $diffView : $currentView;
+      initDataTablesIn($shown);
+      adjustDataTablesIn($shown);
+    });
+
+    // cute-dbt#178 — the toggle rides a .fixture-view-bar strip so
+    // renderExpectedPanel can relocate the header meta (model pill, mode
+    // badge, row count — cute-dbt#202 order) onto the same row,
+    // GitHub-toolbar style.
+    $wrap.append($("<div>").addClass("fixture-view-bar").append($toggle))
+         .append($diffView).append($currentView);
+    return $wrap;
+  }
+
+  // cute-dbt#139 — the per-cell change lens. The #138 wire ships BOTH axes per
+  // cell (`old`/`new` each a `{display, key}` Cell) plus the Rust-precomputed
+  // `changed = old.key != new.key` (the NORMALIZED verdict). This re-derives
+  // the verdict client-side from the active lens WITHOUT a Rust round-trip:
+  //   normalized (default): the canonical `key` axis — Rust's `cell.changed`.
+  //   strict:               the authored `display` axis — a reformat-but-equal
+  //                         cell (`1.00` -> `1`, both keying Number("1")) flags.
+  // Row alignment is NEVER re-paired here (it stays Rust-canonical, #138): only
+  // a Modified row's cells carry both displays, so the strict lens reveals
+  // format-only changes exclusively WITHIN already-Modified rows. Added/Removed
+  // cells never participate — their `changed` is intrinsic to the row kind.
+  // DOCUMENTED ASYMMETRY (the literal #139 spec, not a bug): the strict lens
+  // HIDES a same-display/different-key real change (e.g. Number `1` vs Str
+  // `1`), since the displays match. A pure-reformat row stays canonically
+  // Unchanged (its OLD display is discarded by the Rust `unchanged_row`), so
+  // strict cannot reveal it — closing that gap needs a Rust change and is out
+  // of #139's scope.
+  function cellChanged(cell) {
+    if (!cell) return false;
+    if (settings.normalizeEquality) {
+      return !!cell.changed;
+    }
+    return cellText(cell.old) !== cellText(cell.new);
+  }
+
+  // The row-modified rollup under the active lens: a Modified row counts as
+  // modified iff >=1 of its cells flags under cellChanged. The normalize toggle
+  // never re-pairs rows; it only re-rolls this presentation flag (the row stays
+  // a Rust-`Modified` row even when the lens flags no cell, e.g. a row whose
+  // only delta is format-only and the lens is normalized).
+  function rowFlagged(rc) {
+    return rc.kind === "modified" && rc.cells.some(cellChanged);
+  }
+
+  // Build the Diff grid from a FixtureTableDiff: a unified column axis (with
+  // added/removed column badges from DiffColumn.status) and rows tinted by
+  // RowChangeKind. A `Modified` cell flagged by the active lens (cellChanged)
+  // renders inline `old → new`; otherwise the cell shows its current value.
+  function buildDiffTable(diff, cls, colMeta, owner) {
+    var $wrap = $("<div>").addClass("table-fit");
+    var $tbl = $("<table>").addClass(cls).addClass("cell-diff-table");
+    var $thead = $("<thead>").appendTo($tbl);
+    var $hr = $("<tr>").appendTo($thead);
+    diff.columns.forEach(function (c) {
+      var html = escapeHtml(c.name).replace(/_/g, "_<wbr>");
+      var $th = $("<th>").attr("title", c.name).html(html);
+      if (c.status === "added") {
+        $th.addClass("col-added").append($("<span>").addClass("col-status").text("added"));
+      } else if (c.status === "removed") {
+        $th.addClass("col-removed").append($("<span>").addClass("col-status").text("removed"));
+      }
+      // cute-dbt#165 — the Diff grid's unified column axis shares the same
+      // metadata map (a removed column simply has no entry — the map is
+      // filtered to the CURRENT table's columns in Rust). cute-dbt#178: the
+      // th itself is the tooltip trigger (cute-dbt#240: every th, with the
+      // truthful fallback when the manifest carries no column metadata).
+      decorateColHeader($th, c.name, colMeta && colMeta[c.name], owner);
+      $hr.append($th);
+    });
+    var $tbody = $("<tbody>").appendTo($tbl);
+    diff.rows.forEach(function (rc) {
+      var $tr = $("<tr>").appendTo($tbody);
+      if (rc.kind === "added")   $tr.addClass("row-added");
+      if (rc.kind === "removed") $tr.addClass("row-removed");
+      // cute-dbt#139 — the row-modified tint is the lens rollup, not the raw
+      // Rust kind: a Modified row whose only delta is format-only sheds its
+      // tint under the normalized lens (no cell flags), and regains it under
+      // strict. Added/Removed tints are intrinsic and never lens-driven.
+      if (rowFlagged(rc)) $tr.addClass("row-modified");
+      rc.cells.forEach(function (cell) {
+        $tr.append(buildDiffCell(cell, rc.kind));
+      });
+    });
+    $wrap.append($tbl);
+    return $wrap;
+  }
+
+  // One diff cell. For a Removed row we show the old value; for an Added row
+  // the new value; for an Unchanged row the (shared) value; for a Modified
+  // row, a changed cell renders `old → new` inline, an unchanged cell its
+  // value. `data-order` carries a sort key so DataTables orders the column
+  // sensibly even on the composite old→new rendering.
+  function buildDiffCell(cell, rowKind) {
+    var $td = $("<td>");
+    if (rowKind === "removed") {
+      return decorateValueCell($td, cell.old);
+    }
+    if (rowKind === "added" || rowKind === "unchanged" || !cellChanged(cell)) {
+      // Not flagged (added/unchanged, or — under the active lens — a cell the
+      // normalize toggle treats as unchanged): show the NEW authored value, no
+      // red/green (cute-dbt#138/#139).
+      return decorateValueCell($td, cell.new);
+    }
+    // Modified + changed → inline old → new, each side showing its authored
+    // display. Each side is type-aware via diffToken: a real null/absent keeps
+    // its own neutral styling and never borrows the value-change red/green
+    // (cute-dbt#132). The sort key is the NEW cell's authored display.
+    $td.addClass("cell-changed").attr("data-order", cellText(cell.new));
+    $td.append(diffToken(cell.old, "cell-old"));
+    $td.append($("<span>").addClass("cell-arrow").html("&rarr;"));
+    $td.append(diffToken(cell.new, "cell-new"));
+    return $td;
+  }
+
+  // One side of an inline `old → new` changed cell. `cell` is a `{display,
+  // key}` Cell (cute-dbt#138): the text is the authored `display`; the
+  // neutral-vs-value styling is read from `key.t` so a real null or absent
+  // cell reads in its own neutral styling rather than the brilliant red/green
+  // reserved for a real value change: null → italic muted-gray "NULL"; absent
+  // → blank; any concrete value (including the string 'null') → the side's
+  // color via `sideClass` (.cell-old = red, .cell-new = green). This keeps a
+  // real NULL visually distinct from a string 'null' even mid-diff (#132).
+  function diffToken(cell, sideClass) {
+    var key = cell && cell.key;
+    if (!key || key.t === "null") {
+      return $("<span>").addClass("cell-null").text("NULL");
+    }
+    if (key.t === "absent") {
+      return $("<span>").addClass("cell-absent").text("");
+    }
+    return $("<span>").addClass(sideClass).text(cell.display);
+  }
+
+  // Render a single `{display, key}` Cell into a plain value cell, matching
+  // `buildTable`'s null/number styling so the Current and Diff views read
+  // identically. Text is the authored `display`; styling/sort from `key`.
+  function decorateValueCell($td, cell) {
+    return decorateCurrentCell($td, cell);
+  }
+
+  // The display text of a `{display, key}` Cell (cute-dbt#138): a `null` key
+  // renders the word NULL, an `absent` key renders empty, otherwise the
+  // authored `display` token verbatim. Used for the changed-cell sort key.
+  function cellText(cell) {
+    var key = cell && cell.key;
+    if (!key) return "";
+    if (key.t === "null") return "NULL";
+    if (key.t === "absent") return "";
+    return cell.display == null ? "" : String(cell.display);
+  }
+
+  // Re-measure DataTables column widths for any (now-visible) table under
+  // `$scope`. A no-op for tables not yet wrapped by DataTables. cute-dbt#98 —
+  // a table initialized while `display:none` must be re-adjusted on reveal.
+  function adjustDataTablesIn($scope) {
+    if (!($.fn && $.fn.DataTable)) return;
+    $scope.find("table.given-table, table.expected-table").each(function () {
+      if ($.fn.DataTable.isDataTable(this)) {
+        $(this).DataTable().columns.adjust();
+      }
+    });
+  }
+  // Exposed for headless JS unit tests (cute-dbt#98 — parallels the
+  // `__cuteRenderYamlDiff` seam). Takes the Rust-computed `FixtureTable` POD
+  // (cute-dbt#138) + an optional cell diff and returns the outer wrapper
+  // element so a test can assert the authored-display rendering, the toggle,
+  // and the tinted markup.
+  if (typeof window !== "undefined") {
+    window.__cuteBuildFixtureView = function (table, cls, diff, colMeta, owner) {
+      return buildFixtureView(table, cls, diff, colMeta, owner)[0];
+    };
+    window.__cuteCellText = cellText;
+    // cute-dbt#139 — the cell-change lens + the settings object, exposed so the
+    // headless tests can pin the normalized/strict verdict on a `{old,new,
+    // changed}` cell without driving the full panel, and flip the lens directly.
+    window.__cuteCellChanged = cellChanged;
+    window.__cuteSettings = settings;
+  }
+
+  function initDataTablesIn($scope) {
+    $scope.find("table.given-table, table.expected-table").each(function () {
+      // cute-dbt#98 — never initialize a DataTable while its host is hidden:
+      // column widths measured against a 0-width box render wrong on reveal.
+      // The Current↔Diff toggle handler lazy-inits the hidden view when first
+      // shown (see buildFixtureView). `offsetParent === null` ⇒ not rendered.
+      if (this.offsetParent === null) return;
+      if ($.fn && $.fn.DataTable) {
+        if ($.fn.DataTable.isDataTable(this)) return;
+        $(this).DataTable({
+          paging: false,
+          info: false,
+          searching: false,
+          scrollX: false,
+          ordering: true,
+          order: []
+        });
+      }
+    });
+    reflowPanelsForOverflow();
+  }
+
+  var reflowScheduled = false;
+  function reflowPanelsForOverflow() {
+    if (reflowScheduled) return;
+    reflowScheduled = true;
+    requestAnimationFrame(function () {
+      reflowScheduled = false;
+      var $row = $(".panel-row");
+      if (!$row.length) return;
+      $row.removeClass("is-stacked");
+      void $row[0].offsetWidth;
+      var needsStack = false;
+      $row.find(".table-fit").each(function () {
+        if (this.scrollWidth > this.clientWidth + 1) { needsStack = true; }
+      });
+      if (needsStack) $row.addClass("is-stacked");
+    });
+  }
+
+  $(window).on("resize", reflowPanelsForOverflow);
+
+})();
+/* end of cute-dbt report interaction engine v1 (cute-dbt#178) */
+
+</script>
+<script>
+
+/* cute-dbt cytoscape DAG engine v1 (cute-dbt#180)
+   ----------------------------------------------------------------------------
+   The OPT-IN interactive CTE-DAG engine behind the settings-panel
+   Mermaid <-> Cytoscape picker (Mermaid stays the static default; theme.js
+   persists the choice under cute-dbt.appearance.v1). Visual parity with the
+   Mermaid DAG: same nodes, roles, edge-type colors, selected-pink — every
+   color arrives through the window.cuteDagPalette() hook interaction.js
+   exposes, so this file carries NO palette table of its own (the
+   edge-vocab-completeness CI gate greps interaction.js's
+   JOIN_COLORS_LIGHT/_DARK as the single source; a local copy would dodge it).
+   The hook returns the dark-bg lightness variant when html.dark is on — the
+   handoff's documented dark-theme bug (near-black `from` edges vanishing on
+   dark) cannot recur here because both engines read the same dark palette.
+
+   Init hygiene (the cute-dbt#180 / ADR-4 amendment contract):
+   - node labels are CANVAS TEXT (Cytoscape's default canvas renderer) —
+     XSS-safe by construction: hostile manifest-derived names are drawn as
+     glyphs, never parsed as HTML. No cytoscape-node-html-label, ever.
+   - explicit non-webfont system fontFamily (zero-egress: no font fetch).
+   - NO layout plugin (no cytoscape-dagre, never the EPL-licensed
+     cytoscape-elk) and NO workers: positions come from the first-party
+     longest-path column layout below, fed to the built-in `preset` layout.
+   - handlers are bound from THIS file to rendered elements — never click
+     directives executing payload data.
+   - per-click interaction mutates classes IN PLACE and must NOT re-call a
+     render entry point (the spike's no-renderDag-per-click rule): a rebuild
+     would reset pan/zoom and wipe the lineage highlight. Full rebuild is
+     reserved for model-switch, engine-switch and theme re-tint.
+   - the hover card + node-detail panel are DOM (canvas safety covers labels
+     only): every dynamic string lands via textContent, never innerHTML.
+
+   First-party, NOT vendored: lives at templates/cyto-dag.js, embedded at
+   compile time via asset_embed::CYTO_DAG_JS (include_str!) and interpolated
+   inline by the askama renderer. Banner-pin + end-of-file-sentinel tests in
+   src/adapters/asset_embed.rs guard the include. */
+(function () {
+  "use strict";
+
+  var cy = null;
+  var card = null;
+
+  // ---- longest-path column layout (parity with Mermaid `graph LR`) -------
+  // Kahn topological order relaxing longest path; each node's depth is its
+  // column. The CTE engine guarantees an acyclic graph, but any node a cycle
+  // would leave unseen simply keeps depth 0 (defensive, never throws).
+  // Every id-keyed map is prototype-less (Object.create(null)) so a CTE
+  // legitimately named `toString`/`constructor` can never collide with a
+  // built-in prototype property (gemini review, PR #192).
+  function computeDepths(nodes, edges) {
+    var indeg = Object.create(null), adj = Object.create(null);
+    nodes.forEach(function (n) { indeg[n.id] = 0; adj[n.id] = []; });
+    edges.forEach(function (e) {
+      if (adj[e.from] !== undefined && indeg[e.to] !== undefined) {
+        adj[e.from].push(e.to);
+        indeg[e.to] += 1;
+      }
+    });
+    var depth = Object.create(null);
+    nodes.forEach(function (n) { depth[n.id] = 0; });
+    var queue = nodes.filter(function (n) { return indeg[n.id] === 0; })
+      .map(function (n) { return n.id; });
+    var deg = Object.create(null);
+    nodes.forEach(function (n) { deg[n.id] = indeg[n.id]; });
+    while (queue.length) {
+      var u = queue.shift();
+      adj[u].forEach(function (v) {
+        if (depth[v] < depth[u] + 1) depth[v] = depth[u] + 1;
+        deg[v] -= 1;
+        if (deg[v] === 0) queue.push(v);
+      });
+    }
+    return depth;
+  }
+
+  function layoutPositions(nodes, edges) {
+    var depth = computeDepths(nodes, edges);
+    var cols = Object.create(null);
+    nodes.forEach(function (n) {
+      var d = depth[n.id] || 0;
+      (cols[d] = cols[d] || []).push(n.id);
+    });
+    var colGap = 210, rowGap = 86, pos = Object.create(null);
+    var maxRows = 1;
+    Object.keys(cols).forEach(function (k) {
+      if (cols[k].length > maxRows) maxRows = cols[k].length;
+    });
+    Object.keys(cols).forEach(function (d) {
+      var ids = cols[d];
+      var colH = (ids.length - 1) * rowGap;
+      var offset = ((maxRows - 1) * rowGap - colH) / 2;
+      ids.forEach(function (id, i) {
+        pos[id] = { x: (+d) * colGap, y: offset + i * rowGap };
+      });
+    });
+    return pos;
+  }
+
+  // ---- elements off the SAME DagPayload buildMermaidSource consumes ------
+  function buildElements(m) {
+    var dag = m && m.dag;
+    if (!dag || !dag.nodes || !dag.nodes.length) return null;
+    var pos = layoutPositions(dag.nodes, dag.edges || []);
+    var els = [];
+    dag.nodes.forEach(function (n) {
+      els.push({
+        group: "nodes",
+        // The element id is the stable node id (cute-dbt#155). It is only
+        // ever resolved via cy.getElementById(...) — never string-concatenated
+        // into a selector — so a selector-hostile id is inert by construction.
+        data: { id: n.id, label: String(n.label || n.id), role: n.role || "transform" },
+        position: pos[n.id] || { x: 0, y: 0 }
+      });
+    });
+    (dag.edges || []).forEach(function (e, i) {
+      els.push({
+        group: "edges",
+        data: { id: "e" + i, source: e.from, target: e.to, etype: e.edge_type || "from" }
+      });
+    });
+    return els;
+  }
+
+  function styleSheet() {
+    // The palette hook is REQUIRED (interaction.js defines it before this
+    // file parses). It returns the active light/dark edge palette + the
+    // role fills/strokes/shapes + nodeText + selected — keeping both DAG
+    // engines on the one gated color source.
+    var pal = window.cuteDagPalette();
+    var EDGES = pal.edges, ROLES = pal.roles;
+    var sheet = [
+      { selector: "node", style: {
+        "label": "data(label)",
+        "text-valign": "center",
+        "text-halign": "center",
+        // Explicit non-webfont system stack — zero-egress, ADR-4 contract.
+        "font-family": 'ui-monospace, SFMono-Regular, Menlo, Consolas, monospace',
+        "font-size": 13,
+        "color": pal.nodeText,
+        "text-wrap": "wrap",
+        "text-max-width": 150,
+        "width": "label",
+        "height": "label",
+        "padding": "10px",
+        "border-width": 1.6,
+        "background-color": ROLES.transform.fill,
+        "border-color": ROLES.transform.stroke,
+        "shape": "rectangle",
+        "transition-property": "opacity",
+        "transition-duration": 120
+      }},
+      { selector: 'node[role = "import"]', style: {
+        "background-color": ROLES.import.fill,
+        "border-color": ROLES.import.stroke,
+        "shape": "round-rectangle"
+      }},
+      { selector: 'node[role = "transform"]', style: {
+        "background-color": ROLES.transform.fill,
+        "border-color": ROLES.transform.stroke,
+        "shape": "rectangle"
+      }},
+      { selector: 'node[role = "final"]', style: {
+        "background-color": ROLES.final.fill,
+        "border-color": ROLES.final.stroke,
+        "shape": "hexagon",
+        "border-width": 2,
+        "padding": "14px"
+      }},
+      { selector: "edge", style: {
+        "curve-style": "bezier",
+        "width": 1.8,
+        "target-arrow-shape": "triangle",
+        "line-color": pal.fallbackEdge,
+        "target-arrow-color": pal.fallbackEdge,
+        "arrow-scale": 1.1,
+        "transition-property": "opacity",
+        "transition-duration": 120
+      }}
+    ];
+    Object.keys(EDGES).forEach(function (k) {
+      var dashed = (k === "union_all" || k === "union_distinct");
+      var style = { "line-color": EDGES[k], "target-arrow-color": EDGES[k] };
+      if (dashed) {
+        style["line-style"] = "dashed";
+        style["line-dash-pattern"] = [6, 3];
+      }
+      sheet.push({ selector: 'edge[etype = "' + k + '"]', style: style });
+    });
+    sheet.push({ selector: ".dim", style: { "opacity": 0.18 } });
+    sheet.push({ selector: "node.sel", style: {
+      "border-color": pal.selected, "border-width": 4, "opacity": 1
+    }});
+    sheet.push({ selector: "edge.trace", style: { "opacity": 1, "width": 2.6 } });
+    sheet.push({ selector: "node.trace", style: { "opacity": 1 } });
+    return sheet;
+  }
+
+  // ---- in-place interaction (never a re-render) ---------------------------
+  function clearHighlight() {
+    if (!cy) return;
+    cy.elements().removeClass("dim sel trace");
+  }
+
+  function traceLineage(node) {
+    clearHighlight();
+    var lineage = node.union(node.predecessors()).union(node.successors());
+    cy.elements().addClass("dim");
+    lineage.removeClass("dim").addClass("trace");
+    node.removeClass("dim").addClass("sel");
+  }
+
+  // Drive the report's shared node selection (the Inspect pane) through the
+  // hook interaction.js exposes. The hook updates the panel ONLY — it must
+  // never re-render the DAG (the no-renderDag-per-click rule).
+  function selectReportNode(id) {
+    if (typeof window.__cuteSelectNode === "function") window.__cuteSelectNode(id);
+  }
+
+  // One hover-card row: <div class="nc-row"><b>k</b> <span class="nc-v">v</span></div>.
+  // Built with createElement + textContent ONLY — no innerHTML anywhere on
+  // this DOM surface (the B-assertion: DOM stays escape-disciplined
+  // regardless of the canvas-label safety).
+  function cardRow(key, cls, value) {
+    var row = document.createElement("div");
+    row.className = "nc-row";
+    var b = document.createElement("b");
+    b.textContent = key;
+    var span = document.createElement("span");
+    span.className = cls;
+    span.textContent = value;
+    row.appendChild(b);
+    row.appendChild(document.createTextNode(" "));
+    row.appendChild(span);
+    return row;
+  }
+
+  function showCard(node) {
+    if (!card) return;
+    var d = node.data();
+    var pal = window.cuteDagPalette();
+    while (card.firstChild) card.removeChild(card.firstChild);
+    var id = document.createElement("span");
+    id.className = "nc-id";
+    id.textContent = d.id;
+    card.appendChild(id);
+    card.appendChild(cardRow("role", "nc-role", pal.roleLabels[d.role] || d.role));
+    card.appendChild(cardRow("name", "nc-name", d.label));
+    // Reveal BEFORE measuring (gemini review, PR #192): the chassis hides
+    // the card via opacity (so offsetWidth stays real today), but ordering
+    // the class first keeps the clamp correct even if the hide ever
+    // becomes display-based.
+    card.classList.add("is-visible");
+    var rp = node.renderedPosition();
+    var host = cy.container();
+    var x = Math.min(rp.x + 14, host.clientWidth - card.offsetWidth - 8);
+    var y = Math.min(rp.y + 12, host.clientHeight - card.offsetHeight - 8);
+    card.style.left = Math.max(8, x) + "px";
+    card.style.top = Math.max(8, y) + "px";
+  }
+  function hideCard() {
+    if (card) card.classList.remove("is-visible");
+  }
+
+  // ---- render / destroy (model-switch, engine-switch, theme re-tint) -----
+  function destroy() {
+    if (cy) { cy.destroy(); cy = null; }
+    hideCard();
+  }
+
+  function render(m, selectedNodeId) {
+    var container = document.querySelector(".cte-dag-cyto .cyto-canvas");
+    card = document.querySelector(".cte-dag-cyto .cyto-node-card");
+    if (!container) return;
+    destroy();
+    if (typeof window.cytoscape !== "function") {
+      container.textContent = "Cytoscape engine unavailable.";
+      return;
+    }
+    var els = buildElements(m);
+    if (!els) {
+      container.textContent = "(no DAG available)";
+      return;
+    }
+    container.textContent = "";
+
+    cy = window.cytoscape({
+      container: container,
+      elements: els,
+      style: styleSheet(),
+      layout: { name: "preset", fit: true, padding: 28 },
+      minZoom: 0.35,
+      maxZoom: 2.2,
+      boxSelectionEnabled: false,
+      autoungrabify: false
+    });
+
+    // Handlers bound ONCE per build, from our JS, to rendered elements.
+    // Tap = select + lineage highlight, all in-place class mutation.
+    cy.on("tap", "node", function (evt) {
+      traceLineage(evt.target);
+      selectReportNode(evt.target.id());
+    });
+    cy.on("tap", function (evt) { if (evt.target === cy) clearHighlight(); });
+    cy.on("mouseover", "node", function (evt) { showCard(evt.target); });
+    cy.on("mouseout", "node", hideCard);
+    cy.on("pan zoom drag", hideCard);
+
+    // Restore the report's current selection (parity with the Mermaid
+    // `selected` classDef) without waiting for a click.
+    if (selectedNodeId) {
+      var sel = cy.getElementById(String(selectedNodeId));
+      if (sel && sel.length) traceLineage(sel);
+    }
+
+    // Re-fit after first paint — the host may have been hidden (0-sized)
+    // when the engine flip revealed it this same frame. `fit(28)` is the
+    // documented number-as-padding form (gemini review, PR #192).
+    window.requestAnimationFrame(function () { if (cy) cy.fit(28); });
+  }
+
+  // Public seam: interaction.js's renderDag() dispatcher drives render/
+  // destroy; the cyInstance getter is the headless-test seam (mirrors the
+  // window.__cute* seam precedent). `layoutPositions` + `styleSheetFor` are
+  // EXPOSED (cute-dbt#429/#431) so the report's tab-scoped DAGs (the Models-tab
+  // mini-DAG + the Macros-tab macro DAG) reuse the SAME first-party preset
+  // longest-path column layout — the report-page no-layout-plugin contract
+  // (NEVER cytoscape-dagre; dagre is the explore page's layout only,
+  // AGENTS.md). The tab DAGs build their own independent cy instances (the
+  // CTE-DAG `cy` above stays a separate singleton), so they pass their own
+  // host container; they share only the pure layout math + the palette hook.
+  window.CuteCyto = {
+    render: render,
+    destroy: destroy,
+    cyInstance: function () { return cy; },
+    layoutPositions: layoutPositions
+  };
+})();
+/* end of cute-dbt cytoscape DAG engine v1 (cute-dbt#180) */
+
+</script>
+<script>
+
+/* cute-dbt shared appearance engine v1 (cute-dbt#242)
+   ----------------------------------------------------------------------------
+   The cross-page-family half of the appearance system, re-layered out of
+   templates/theme.js at cute-dbt#242: reads the persisted appearance
+   (localStorage key cute-dbt.appearance.v1, graceful in-memory fallback),
+   resolves the prefers-color-scheme default, and applies the html-level
+   hooks the shared token partial (templates/partials/tokens.css) keys on:
+   [data-theme] + the html.dark family class (the DataTables dark sync),
+   [data-style], [data-density], [data-difflayout], [data-coverage],
+   [data-project] and the accent custom-property overrides. Embedded into BOTH page families —
+   report.html (where templates/theme.js layers the settings UI on top via
+   window.CuteAppearance) and explore-dag.html / explore-tests.html (which
+   apply the saved appearance read-only; the explore-side settings
+   affordance is cute-dbt#219's lane). Plain vanilla ES5, no framework,
+   zero egress.
+
+   Application happens at PARSE TIME (the script sits at the end of <body>;
+   documentElement already exists), so the attributes are in place before
+   any DOMContentLoaded boot — including the report's interaction engine
+   and theme.js's control sync.
+
+   Attributes without matching rules on a page are deliberately applied
+   anyway ([data-difflayout] / [data-coverage] / [data-project] are inert
+   on the explore pages today): ONE attribute contract across page
+   families, so pages
+   adopt new surfaces without touching this engine. The `engine` field
+   (the report's Mermaid <-> Cytoscape DAG pick) is persisted state only
+   here — applying it is report-specific (theme.js applyEngine).
+
+   First-party, NOT vendored: this file lives at templates/appearance.js,
+   embedded at compile time via asset_embed::APPEARANCE_JS (include_str!)
+   and interpolated inline by the askama renderers. Banner-pin +
+   end-of-file-sentinel tests in src/adapters/asset_embed.rs guard the
+   include. */
+(function () {
+  "use strict";
+
+  // The eight [data-theme] packs the token partial ships
+  // (templates/partials/tokens.css), light family first (design pass-2,
+  // cute-dbt#198) — kept in lockstep with the report's static theme grid.
+  // `dark` drives the html.dark class DataTables' vendored dark rules key on.
+  var THEMES = [
+    { id: "light",     dark: false },
+    { id: "solarized", dark: false },
+    { id: "latte",     dark: false },
+    { id: "rosepine",  dark: false },
+    { id: "dark",      dark: true  },
+    { id: "tokyo",     dark: true  },
+    { id: "gruvbox",   dark: true  },
+    { id: "dracula",   dark: true  }
+  ];
+
+  // Accent palettes: value / hover / tint triples applied as inline custom
+  // properties on <html>. "theme" = remove the override (the active theme's
+  // own accent shows through).
+  var ACCENTS = [
+    { id: "theme" },
+    { id: "teal",   v: "#1d7484", h: "#155c69", t: "#e3f0f2" },
+    { id: "blue",   v: "#2a6fdb", h: "#1f57b0", t: "#e6eefb" },
+    { id: "violet", v: "#7c3aed", h: "#6027c4", t: "#efe8fd" },
+    { id: "green",  v: "#1f8a5b", h: "#176d47", t: "#e2f3ec" },
+    { id: "amber",  v: "#b45c0c", h: "#8f4708", t: "#fbeede" }
+  ];
+
+  // The public localStorage key for the appearance state — a stable consumer
+  // contract (cute-dbt#178 AC3), not a credential. Cross-page since #242:
+  // every cute-dbt page family reads + applies this same key.
+  var KEY = "cute-dbt.appearance.v1"; // gitleaks:allow — a public storage key name, no secret
+  // `engine` (cute-dbt#180) is the DAG-engine picker: "mermaid" (the static
+  // default) or "cytoscape" (the opt-in interactive engine).
+  // `coverage` (cute-dbt#219) is the viewer-side coverage-intelligence
+  // display toggle: "off" (the default since cute-dbt#292 — the settings
+  // panel's Experimental group; the field may be absent) or the explicit
+  // opt-in "on". The CROSS-PAGE contract: any cute-dbt page that renders
+  // check-engine-derived content reads this same field — the report keys
+  // one CSS rule on html[data-coverage=off]; the explorer pages adopt it
+  // as #103/#104 land.
+  // `project` (cute-dbt#292) is the viewer-side project-state display
+  // toggle: "on" (default; the field may be absent) or "off". Emission is
+  // already opt-in at render time (the cute-dbt#291 experimental gate), so
+  // the viewer default SHOWS what the producer chose to emit; the report
+  // keys CSS rules on html[data-project=off].
+  var pref = { theme: null, style: "soft", accent: "theme", density: "auto", difflayout: "auto", engine: "mermaid", coverage: "off", project: "on" };
+
+  // Read the persisted appearance, string-typed keys only. Any storage error
+  // (file:// SecurityError, disabled storage) leaves the defaults intact.
+  function load() {
+    var raw;
+    try { raw = window.localStorage && window.localStorage.getItem(KEY); } catch (e) { raw = null; }
+    if (!raw) return;
+    try {
+      var p = JSON.parse(raw);
+      if (p && typeof p === "object") {
+        ["theme", "style", "accent", "density", "difflayout", "engine", "coverage", "project"].forEach(function (k) {
+          if (typeof p[k] === "string") pref[k] = p[k];
+        });
+      }
+    } catch (e) { /* ignore — defaults hold */ }
+    // Coerce the engine into its closed vocabulary — an unknown persisted
+    // value must fall back to the static default, never reach the dispatcher.
+    if (pref.engine !== "cytoscape") pref.engine = "mermaid";
+    // Same closed-vocabulary coercion for the coverage toggle (cute-dbt#219;
+    // default flipped OFF at cute-dbt#292): anything but the explicit "on"
+    // reads as the default OFF.
+    if (pref.coverage !== "on") pref.coverage = "off";
+    // And for the project-state display toggle (cute-dbt#292): anything but
+    // the explicit "off" reads as the default ON — emission is already
+    // opt-in at render time (cute-dbt#291).
+    if (pref.project !== "off") pref.project = "on";
+  }
+  // Persist the current appearance. Swallows any storage error (zero-egress
+  // in-memory fallback, mirrors interaction.js saveSettings).
+  function save() {
+    try { if (window.localStorage) window.localStorage.setItem(KEY, JSON.stringify(pref)); } catch (e) { /* in-memory only */ }
+  }
+
+  var root = document.documentElement;
+
+  function themeById(id) {
+    for (var i = 0; i < THEMES.length; i++) if (THEMES[i].id === id) return THEMES[i];
+    return THEMES[0];
+  }
+
+  function applyTheme(id) {
+    var t = themeById(id);
+    root.setAttribute("data-theme", t.id);
+    root.classList.toggle("dark", t.dark); // DataTables dark rules follow html.dark
+    pref.theme = t.id;
+  }
+  function applyAccent(id) {
+    var a = null;
+    for (var i = 0; i < ACCENTS.length; i++) if (ACCENTS[i].id === id) a = ACCENTS[i];
+    if (!a || a.id === "theme") {
+      root.style.removeProperty("--accent");
+      root.style.removeProperty("--accent-hover");
+      root.style.removeProperty("--accent-tint");
+      pref.accent = "theme";
+    } else {
+      root.style.setProperty("--accent", a.v);
+      root.style.setProperty("--accent-hover", a.h);
+      root.style.setProperty("--accent-tint", a.t);
+      pref.accent = a.id;
+    }
+  }
+  function applyDensity(d) {
+    if (d === "auto") root.removeAttribute("data-density");
+    else root.setAttribute("data-density", d);
+    pref.density = d;
+  }
+  function applyDiffLayout(v) {
+    root.setAttribute("data-difflayout", v);
+    pref.difflayout = v;
+  }
+  function applyStyle(s) {
+    root.setAttribute("data-style", s);
+    pref.style = s;
+  }
+  // cute-dbt#219 — the coverage-intelligence display toggle. PURE display:
+  // OFF sets html[data-coverage=off] (one CSS rule hides every
+  // check-engine-derived surface); ON removes the attribute and the
+  // already-rendered content shows again — no re-render, payload untouched.
+  // Default OFF since cute-dbt#292 (the Experimental settings group), so an
+  // unknown value reads as OFF.
+  function applyCoverage(v) {
+    var coverage = v === "on" ? "on" : "off";
+    if (coverage === "off") root.setAttribute("data-coverage", "off");
+    else root.removeAttribute("data-coverage");
+    pref.coverage = coverage;
+  }
+  // cute-dbt#292 — the project-state display toggle, the applyCoverage
+  // twin with the OPPOSITE default polarity (emission is already opt-in at
+  // render time, so an unknown value reads as ON): OFF sets
+  // html[data-project=off] — CSS hides the project-definition panel and the
+  // per-model provenance/var chips; ON removes the attribute. PURE display:
+  // no re-render, payload untouched.
+  function applyProject(v) {
+    var project = v === "off" ? "off" : "on";
+    if (project === "off") root.setAttribute("data-project", "off");
+    else root.removeAttribute("data-project");
+    pref.project = project;
+  }
+
+  // ---- boot: load, resolve the default theme, apply everything ----------
+  load();
+  // Default theme: saved -> prefers-color-scheme -> light.
+  if (!pref.theme) {
+    pref.theme = (window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)").matches) ? "dark" : "light";
+  }
+  applyStyle(pref.style);
+  applyTheme(pref.theme);
+  applyAccent(pref.accent);
+  applyDensity(pref.density);
+  applyDiffLayout(pref.difflayout);
+  applyCoverage(pref.coverage);
+  applyProject(pref.project);
+
+  // The page-facing seam: templates/theme.js (the report's settings UI)
+  // drives the engine through this object — `pref` is the ONE live state
+  // (shared by reference; apply* mutate it, save() persists it).
+  window.CuteAppearance = {
+    key: KEY,
+    pref: pref,
+    save: save,
+    applyTheme: applyTheme,
+    applyStyle: applyStyle,
+    applyAccent: applyAccent,
+    applyDensity: applyDensity,
+    applyDiffLayout: applyDiffLayout,
+    applyCoverage: applyCoverage,
+    applyProject: applyProject
+  };
+})();
+/* end of cute-dbt shared appearance engine v1 (cute-dbt#242) */
+
+</script>
+<script>
+
+/* cute-dbt report appearance settings v1 (cute-dbt#178, re-layered cute-dbt#242)
+   ----------------------------------------------------------------------------
+   The REPORT-ONLY half of the appearance system: wires the theme / style /
+   accent / density / diff-layout / DAG-engine / coverage controls in the
+   report's settings panel (the markup is static in templates/report.html —
+   the askama DOM contract), syncs their visual state, reflows DataTables on
+   metric-affecting flips and dispatches the DAG-engine pick + re-tint.
+
+   The load/apply/persist core (localStorage key cute-dbt.appearance.v1,
+   prefers-color-scheme default, the html-level attribute application)
+   moved to the SHARED appearance engine at cute-dbt#242 —
+   templates/appearance.js, which parses immediately before this file and
+   exposes window.CuteAppearance. This file drives that engine; it never
+   touches the storage key or the html attributes directly. The #188
+   diff-cells colour/marks control was retired by design pass-2
+   (cute-dbt#198): cells always render in colour; a legacy persisted
+   `diffstyle` key is ignored gracefully by the shared engine's load().
+
+   First-party, NOT vendored: this file lives at templates/theme.js, embedded
+   at compile time via asset_embed::THEME_JS (include_str!) and interpolated
+   inline by the askama renderer. Banner-pin + end-of-file-sentinel tests in
+   src/adapters/asset_embed.rs guard the include. */
+(function () {
+  "use strict";
+
+  // The shared appearance engine (templates/appearance.js) parses before
+  // this file in the report's script order; its boot already loaded +
+  // applied the persisted appearance. `pref` is the ONE live state object,
+  // shared by reference — the engine's apply* mutate it, save() persists it.
+  var appearance = window.CuteAppearance;
+  var pref = appearance.pref;
+
+  // ES5-safe NodeList iteration (gemini review, PR #188): older engines ship
+  // querySelectorAll results without NodeList.prototype.forEach; iterate via
+  // an index loop so this file keeps its deliberate ES5 posture throughout.
+  function qsaForEach(selector, fn) {
+    var list = document.querySelectorAll(selector);
+    for (var i = 0; i < list.length; i++) fn(list[i]);
+  }
+
+  // cute-dbt#180 — push the picker's engine to the render dispatcher
+  // (interaction.js owns the DAG; this file just records the choice on the
+  // shared pref). REPORT-ONLY by design: the explore pages run no DAG
+  // engine picker, so this apply stays out of the shared engine. The
+  // caller follows up with rerenderDag() so the flip happens in place.
+  function applyEngine(e) {
+    var engine = e === "cytoscape" ? "cytoscape" : "mermaid";
+    pref.engine = engine;
+    if (typeof window.__cuteSetDagEngine === "function") window.__cuteSetDagEngine(engine);
+  }
+
+  // Re-tint the DAG after a theme flip (the light/dark edge + anchor
+  // variants live in interaction.js, which owns the legend + Mermaid).
+  function rerenderDag() {
+    if (typeof window.__cuteRerenderDag === "function") window.__cuteRerenderDag();
+  }
+
+  /* ---- sync visual state of the (static) controls to prefs ------------- */
+  function syncControls() {
+    qsaForEach(".style-opt", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-style-id") === pref.style ? "true" : "false");
+    });
+    qsaForEach(".theme-chip", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-theme-id") === pref.theme ? "true" : "false");
+    });
+    qsaForEach(".accent-swatch", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-accent") === pref.accent ? "true" : "false");
+    });
+    qsaForEach(".density-seg button", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-density") === pref.density ? "true" : "false");
+    });
+    qsaForEach(".difflayout-seg button", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-difflayout") === pref.difflayout ? "true" : "false");
+    });
+    qsaForEach(".engine-seg button", function (b) {
+      b.setAttribute("aria-pressed", b.getAttribute("data-engine") === pref.engine ? "true" : "false");
+    });
+    // cute-dbt#219 — the coverage switch mirrors the pref (a checkbox owns
+    // its checked state natively; no aria-pressed on an <input>). Default
+    // OFF since cute-dbt#292: only the explicit "on" checks it.
+    var cov = document.getElementById("settings-coverage-input");
+    if (cov) cov.checked = pref.coverage === "on";
+    // cute-dbt#292 — the project-state display switch mirrors the pref
+    // (the row exists only when the report emitted project-state content).
+    var proj = document.getElementById("settings-project-input");
+    if (proj) proj.checked = pref.project !== "off";
+  }
+
+  function wire() {
+    qsaForEach(".style-opt", function (b) {
+      b.addEventListener("click", function () { appearance.applyStyle(b.getAttribute("data-style-id")); appearance.save(); syncControls(); reflowTables(); rerenderDag(); });
+    });
+    qsaForEach(".theme-chip", function (b) {
+      b.addEventListener("click", function () { appearance.applyTheme(b.getAttribute("data-theme-id")); appearance.save(); syncControls(); reflowTables(); rerenderDag(); });
+    });
+    qsaForEach(".accent-swatch", function (b) {
+      b.addEventListener("click", function () { appearance.applyAccent(b.getAttribute("data-accent")); appearance.save(); syncControls(); });
+    });
+    qsaForEach(".density-seg button", function (b) {
+      b.addEventListener("click", function () { appearance.applyDensity(b.getAttribute("data-density")); appearance.save(); syncControls(); reflowTables(); });
+    });
+    qsaForEach(".difflayout-seg button", function (b) {
+      b.addEventListener("click", function () { appearance.applyDiffLayout(b.getAttribute("data-difflayout")); appearance.save(); syncControls(); });
+    });
+    // cute-dbt#180 — the DAG-engine picker. The swap is IN PLACE: push the
+    // engine to the dispatcher, persist, then rerenderDag() tears down the
+    // old engine and builds the new one. No reload.
+    qsaForEach(".engine-seg button", function (b) {
+      b.addEventListener("click", function () { applyEngine(b.getAttribute("data-engine")); appearance.save(); syncControls(); rerenderDag(); });
+    });
+    // cute-dbt#219 — the coverage-intelligence switch: flip the attribute,
+    // persist. Display-only — no re-render, no DAG/table reflow needed.
+    var cov = document.getElementById("settings-coverage-input");
+    if (cov) {
+      cov.addEventListener("change", function () { appearance.applyCoverage(cov.checked ? "on" : "off"); appearance.save(); });
+    }
+    // cute-dbt#292 — the project-state display switch: the same
+    // display-only contract as the coverage switch.
+    var proj = document.getElementById("settings-project-input");
+    if (proj) {
+      proj.addEventListener("change", function () { appearance.applyProject(proj.checked ? "on" : "off"); appearance.save(); });
+    }
+  }
+
+  // DataTables column widths shift when the font-size changes (density /
+  // style / theme flips); re-adjust the visible ones. jQuery is bound
+  // locally (gemini review, PR #188) so the lookup is explicit and a bare
+  // global `jQuery` reference can never throw mid-iteration.
+  function reflowTables() {
+    var $ = window.jQuery;
+    if ($ && $.fn && $.fn.DataTable) {
+      $("table.given-table, table.expected-table").each(function () {
+        if ($.fn.DataTable.isDataTable(this) && this.offsetParent !== null) {
+          try { $(this).DataTable().columns.adjust(); } catch (e) { /* hidden */ }
+        }
+      });
+    }
+  }
+
+  function boot() {
+    // The shared engine already loaded + applied the persisted appearance
+    // at parse time; this boot owns the report-only side: control state,
+    // wiring, and the deferred DAG-engine apply.
+    syncControls();
+    wire();
+    // Re-tint the legend + the active DAG engine once the theme's .dark
+    // class is on: interaction.js drew them on DOM-ready, so the edge
+    // swatches would otherwise keep the light palette on a dark boot.
+    // The engine apply rides the same deferred tick (cute-dbt#180): by
+    // then interaction.js's boot has installed __cuteSetDagEngine, so a
+    // persisted "cytoscape" choice flips the DAG in place right after the
+    // default Mermaid render.
+    setTimeout(function () {
+      applyEngine(pref.engine);
+      rerenderDag();
+    }, 0);
+  }
+
+  if (document.readyState === "loading") document.addEventListener("DOMContentLoaded", boot);
+  else boot();
+})();
+/* end of cute-dbt report appearance settings v1 (cute-dbt#178) */
+
+</script>
+</body>
+</html>
\ No newline at end of file
diff --git a/examples/jaffle-shop-report.html b/examples/jaffle-shop-report.html
index ec8f295..00d9044 100644
--- a/examples/jaffle-shop-report.html
+++ b/examples/jaffle-shop-report.html
@@ -2580,7 +2580,7 @@ <h2>Expected</h2>
 <div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
 </div>
 
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index 2022c3c..d517ee2 100644
--- a/examples/playground-report.html
+++ b/examples/playground-report.html
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It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final select)\u001f*":{"start":{"line":91,"col":8,"byte":2540},"end":{"line":91,"col":9,"byte":2541}},"sequenced\u001f_loaded_at":{"start":{"line":51,"col":11,"byte":1159},"end":{"line":51,"col":42,"byte":1190}},"sequenced\u001faddress":{"start":{"line":17,"col":11,"byte":338},"end":{"line":17,"col":18,"byte":345}},"sequenced\u001famount_covered":{"start":{"line":24,"col":11,"byte":452},"end":{"line":24,"col":25,"byte":466}},"sequenced\u001famount_uncovered":{"start":{"line":25,"col":11,"byte":477},"end":{"line":25,"col":27,"byte":493}},"sequenced\u001faverage_quality_of_life_score":{"start":{"line":40,"col":11,"byte":856},"end":{"line":40,"col":40,"byte":885}},"sequenced\u001fcity":{"start":{"line":18,"col":11,"byte":356},"end":{"line":18,"col":15,"byte":360}},"sequenced\u001fcoverage_rate":{"start":{"line":44,"col":11,"byte":948},"end":{"line":48,"col":29,"byte":1127}},"sequenced\u001fcovered_encounters":{"start":{"line":29,"col":11,"byte":554},"end":{"line":29,"col":29,"byte":572}},"sequenced\u001fcovered_immunizations":{"start":{"line":35,"col":11,"byte":736},"end":{"line":35,"col":32,"byte":757}},"sequenced\u001fcovered_medications":{"start":{"line":31,"col":11,"byte":614},"end":{"line":31,"col":30,"byte":633}},"sequenced\u001fcovered_procedures":{"start":{"line":33,"col":11,"byte":676},"end":{"line":33,"col":29,"byte":694}},"sequenced\u001fmember_months":{"start":{"line":41,"col":11,"byte":896},"end":{"line":41,"col":24,"byte":909}},"sequenced\u001fpayer_id":{"start":{"line":13,"col":11,"byte":275},"end":{"line":13,"col":19,"byte":283}},"sequenced\u001fpayer_key":{"start":{"line":10,"col":9,"byte":190},"end":{"line":10,"col":59,"byte":240}},"sequenced\u001fpayer_name":{"start":{"line":16,"col":11,"byte":317},"end":{"line":16,"col":21,"byte":327}},"sequenced\u001fphone":{"start":{"line":21,"col":11,"byte":406},"end":{"line":21,"col":16,"byte":411}},"sequenced\u001frevenue":{"start":{"line":26,"col":11,"byte":504},"end":{"line":26,"col":18,"byte":511}},"sequenced\u001fstate":{"start":{"line":19,"col":11,"byte":371},"end":{"line":19,"col":16,"byte":376}},"sequenced\u001funcovered_encounters":{"start":{"line":30,"col":11,"byte":583},"end":{"line":30,"col":31,"byte":603}},"sequenced\u001funcovered_immunizations":{"start":{"line":36,"col":11,"byte":768},"end":{"line":36,"col":34,"byte":791}},"sequenced\u001funcovered_medications":{"start":{"line":32,"col":11,"byte":644},"end":{"line":32,"col":32,"byte":665}},"sequenced\u001funcovered_procedures":{"start":{"line":34,"col":11,"byte":705},"end":{"line":34,"col":31,"byte":725}},"sequenced\u001funique_customers":{"start":{"line":39,"col":11,"byte":829},"end":{"line":39,"col":27,"byte":845}},"sequenced\u001fzip_code":{"start":{"line":20,"col":11,"byte":387},"end":{"line":20,"col":19,"byte":395}},"source\u001f*":{"start":{"line":4,"col":12,"byte":30},"end":{"line":4,"col":13,"byte":31}},"unknown_member\u001f_loaded_at":{"start":{"line":82,"col":11,"byte":2408},"end":{"line":82,"col":42,"byte":2439}},"unknown_member\u001faddress":{"start":{"line":62,"col":16,"byte":1411},"end":{"line":62,"col":43,"byte":1438}},"unknown_member\u001famount_covered":{"start":{"line":67,"col":16,"byte":1620},"end":{"line":67,"col":49,"byte":1653}},"unknown_member\u001famount_uncovered":{"start":{"line":68,"col":16,"byte":1669},"end":{"line":68,"col":51,"byte":1704}},"unknown_member\u001faverage_quality_of_life_score":{"start":{"line":79,"col":16,"byte":2253},"end":{"line":79,"col":64,"byte":2301}},"unknown_member\u001fcity":{"start":{"line":63,"col":16,"byte":1454},"end":{"line":63,"col":40,"byte":1478}},"unknown_member\u001fcoverage_rate":{"start":{"line":81,"col":16,"byte":2365},"end":{"line":81,"col":48,"byte":2397}},"unknown_member\u001fcovered_encounters":{"start":{"line":70,"col":16,"byte":1762},"end":{"line":70,"col":53,"byte":1799}},"unknown_member\u001fcovered_immunizations":{"start":{"line":76,"col":16,"byte":2088},"end":{"line":76,"col":56,"byte":2128}},"unknown_member\u001fcovered_medications":{"start":{"line":72,"col":16,"byte":1870},"end":{"line":72,"col":54,"byte":1908}},"unknown_member\u001fcovered_procedures":{"start":{"line":74,"col":16,"byte":1980},"end":{"line":74,"col":53,"byte":2017}},"unknown_member\u001fmember_months":{"start":{"line":80,"col":16,"byte":2317},"end":{"line":80,"col":48,"byte":2349}},"unknown_member\u001fpayer_id":{"start":{"line":60,"col":11,"byte":1334},"end":{"line":60,"col":32,"byte":1355}},"unknown_member\u001fpayer_key":{"start":{"line":59,"col":10,"byte":1309},"end":{"line":59,"col":24,"byte":1323}},"unknown_member\u001fpayer_name":{"start":{"line":61,"col":11,"byte":1366},"end":{"line":61,"col":40,"byte":1395}},"unknown_member\u001fphone":{"start":{"line":66,"col":16,"byte":1579},"end":{"line":66,"col":41,"byte":1604}},"unknown_member\u001frevenue":{"start":{"line":69,"col":16,"byte":1720},"end":{"line":69,"col":42,"byte":1746}},"unknown_member\u001fstate":{"start":{"line":64,"col":16,"byte":1494},"end":{"line":64,"col":41,"byte":1519}},"unknown_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}},"raw_node_spans":{"final":{"start":{"line":85,"col":1,"byte":2515},"end":{"line":89,"col":2,"byte":2602}},"sequenced":{"start":{"line":7,"col":1,"byte":158},"end":{"line":54,"col":2,"byte":1280}},"source":{"start":{"line":3,"col":6,"byte":91},"end":{"line":5,"col":2,"byte":155}},"unknown_member":{"start":{"line":57,"col":1,"byte":1340},"end":{"line":83,"col":2,"byte":2512}}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue collected","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"revenue","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"revenue is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_revenue__0__revenue_is_not_null.0bff623461"}],"documented":true},"state":{"description":"State","documented":true},"uncovered_encounters":{"description":"Number of encounters not covered","documented":true},"uncovered_immunizations":{"description":"Number of immunizations not covered","documented":true},"uncovered_medications":{"description":"Number of medications not covered","documented":true},"uncovered_procedures":{"description":"Number of procedures not covered","documented":true},"unique_customers":{"description":"Number of unique customers","documented":true},"zip_code":{"description":"ZIP code","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__payers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_id"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"payer_name"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"payer_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"address"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"address"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"zip_code"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"zip_code"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"phone"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"phone"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_covered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_covered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"amount_uncovered"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"amount_uncovered"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"revenue"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"revenue"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_encounters"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_encounters"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_medications"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_medications"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_procedures"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_procedures"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"covered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"covered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"uncovered_immunizations"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"uncovered_immunizations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"unique_customers"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"unique_customers"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"average_quality_of_life_score"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"average_quality_of_life_score"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"member_months"},"to_col":{"scope":{"intra":{"node_id":"sequenced"}},"column":"member_months"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"final"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":13,"col":1,"byte":233},"end":{"line":22,"col":1,"byte":450}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":11,"col":2,"byte":231}}},"raw_zones":[{"kind":"for_loop","start":{"line":16,"col":9,"byte":304},"end":{"line":18,"col":21,"byte":428},"presence":"structural","node_id":"source"},{"kind":"incremental_guard","start":{"line":29,"col":1,"byte":594},"end":{"line":31,"col":12,"byte":691},"presence":"structural","node_id":"(final select)"}],"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":14,"col":5,"byte":244},"end":{"line":14,"col":17,"byte":256}},"(final select)\u001fencounter_type":{"start":{"line":16,"col":7,"byte":280},"end":{"line":16,"col":21,"byte":294}},"(final select)\u001fpatient_id":{"start":{"line":15,"col":7,"byte":263},"end":{"line":15,"col":17,"byte":273}},"(final select)\u001fstart_time":{"start":{"line":17,"col":7,"byte":301},"end":{"line":17,"col":17,"byte":311}},"(final select)\u001ftotal_cost_rounded":{"start":{"line":18,"col":7,"byte":318},"end":{"line":18,"col":25,"byte":336}},"source\u001fencounter_id":{"start":{"line":5,"col":9,"byte":38},"end":{"line":5,"col":21,"byte":50}},"source\u001fencounter_type":{"start":{"line":7,"col":11,"byte":82},"end":{"line":7,"col":25,"byte":96}},"source\u001fpatient_id":{"start":{"line":6,"col":11,"byte":61},"end":{"line":6,"col":21,"byte":71}},"source\u001fstart_time":{"start":{"line":8,"col":11,"byte":107},"end":{"line":8,"col":21,"byte":117}},"source\u001ftotal_cost_rounded":{"start":{"line":9,"col":11,"byte":128},"end":{"line":9,"col":53,"byte":170}}},"raw_node_spans":{"source":{"start":{"line":3,"col":6,"byte":185},"end":{"line":13,"col":2,"byte":476}}},"raw_column_spans":{"(final select)\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}},"source\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost_rounded"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost_rounded"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        {% for cost_col in rounded_cost_columns %}\n        , round({{ cost_col }}, 2) as {{ cost_col }}_rounded\n        {% endfor %}\n    from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}\n","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"code_map":{"compiled":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":11,"col":40,"byte":193}}},"column_spans":{"(final select)\u001f*":{"start":{"line":7,"col":8,"byte":69},"end":{"line":7,"col":9,"byte":70}}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key for the encounter (from source system)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_encounter_id.b3a18316f7"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.unique_int_dq_quarantine__encounters_encounter_id.31321ef55a"}],"documented":true},"end_after_1900":{"description":"Flag: encounter_end_at >= timestamp '1900-01-01'","documented":true},"failed_dq_tests":{"description":"Array of failed validation test names","tests":[{"kind":"not_null","kwargs":{"column_name":"failed_dq_tests","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_failed_dq_tests.4bad065925"}],"documented":true},"is_dq_valid":{"description":"Data quality validity flag - should always be false in quarantine","tests":[{"kind":"accepted_values","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}","values":[false]},"node_id":"test.healthcare_analytics.accepted_values_int_dq_quarantine__encounters_is_dq_valid__False.be192883a9"},{"kind":"not_null","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_is_dq_valid.2c340f0519"}],"documented":true},"no_future_encounter_dates":{"description":"Flag: encounter_start_at \u003c= current_timestamp","documented":true},"start_after_1900":{"description":"Flag: encounter_start_at >= timestamp '1900-01-01'","documented":true},"valid_encounter_timestamps":{"description":"Flag: encounter_end_at >= encounter_start_at","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/intermediate/quarantine/_quarantine__models.yml","missing":"Schema file models/intermediate/quarantine/_quarantine__models.yml was not found under the project root."},"tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}},"raw_node_spans":{"grid":{"start":{"line":19,"col":1,"byte":781},"end":{"line":28,"col":2,"byte":1036}},"months":{"start":{"line":5,"col":6,"byte":536},"end":{"line":11,"col":2,"byte":664}},"states":{"start":{"line":13,"col":1,"byte":667},"end":{"line":17,"col":2,"byte":778}}},"raw_column_spans":{"grid\u001fmonth_key":{"start":{"line":21,"col":76,"byte":877},"end":{"line":21,"col":85,"byte":886}}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}},"raw_node_spans":{"combined_metrics":{"start":{"line":29,"col":1,"byte":1516},"end":{"line":53,"col":2,"byte":2430}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":103},"end":{"line":14,"col":2,"byte":797}},"medication_metrics":{"start":{"line":16,"col":1,"byte":800},"end":{"line":27,"col":2,"byte":1513}}},"raw_column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"code_map":{"compiled":"with source as (\n    select * from \"memory\".\"main\".\"patients\"\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1155},"end":{"line":55,"col":40,"byte":1215}},"final":{"start":{"line":46,"col":1,"byte":1061},"end":{"line":51,"col":2,"byte":1153}},"renamed":{"start":{"line":5,"col":1,"byte":66},"end":{"line":44,"col":2,"byte":1058}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":63}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1162},"end":{"line":53,"col":9,"byte":1163}},"final\u001f*":{"start":{"line":48,"col":9,"byte":1091},"end":{"line":48,"col":10,"byte":1092}},"final\u001f_loaded_at":{"start":{"line":49,"col":11,"byte":1103},"end":{"line":49,"col":42,"byte":1134}},"renamed\u001faddress":{"start":{"line":32,"col":11,"byte":740},"end":{"line":32,"col":29,"byte":758}},"renamed\u001fbirth_date":{"start":{"line":22,"col":11,"byte":464},"end":{"line":22,"col":34,"byte":487}},"renamed\u001fbirth_place":{"start":{"line":31,"col":11,"byte":704},"end":{"line":31,"col":36,"byte":729}},"renamed\u001fcity":{"start":{"line":33,"col":11,"byte":769},"end":{"line":33,"col":23,"byte":781}},"renamed\u001fcounty":{"start":{"line":35,"col":11,"byte":817},"end":{"line":35,"col":27,"byte":833}},"renamed\u001fdeath_date":{"start":{"line":23,"col":11,"byte":498},"end":{"line":23,"col":34,"byte":521}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":11,"byte":608},"end":{"line":27,"col":37,"byte":634}},"renamed\u001fethnicity":{"start":{"line":18,"col":11,"byte":377},"end":{"line":18,"col":33,"byte":399}},"renamed\u001ffirst_name":{"start":{"line":11,"col":11,"byte":173},"end":{"line":11,"col":30,"byte":192}},"renamed\u001fgender":{"start":{"line":16,"col":11,"byte":327},"end":{"line":16,"col":27,"byte":343}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":11,"byte":998},"end":{"line":42,"col":53,"byte":1040}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":11,"byte":945},"end":{"line":41,"col":53,"byte":987}},"renamed\u001flast_name":{"start":{"line":12,"col":11,"byte":203},"end":{"line":12,"col":28,"byte":220}},"renamed\u001flatitude":{"start":{"line":37,"col":11,"byte":870},"end":{"line":37,"col":26,"byte":885}},"renamed\u001flongitude":{"start":{"line":38,"col":11,"byte":896},"end":{"line":38,"col":27,"byte":912}},"renamed\u001fmaiden_name":{"start":{"line":15,"col":11,"byte":295},"end":{"line":15,"col":32,"byte":316}},"renamed\u001fmarital_status":{"start":{"line":19,"col":11,"byte":410},"end":{"line":19,"col":36,"byte":435}},"renamed\u001fname_prefix":{"start":{"line":13,"col":11,"byte":231},"end":{"line":13,"col":32,"byte":252}},"renamed\u001fname_suffix":{"start":{"line":14,"col":11,"byte":263},"end":{"line":14,"col":32,"byte":284}},"renamed\u001fpassport_number":{"start":{"line":28,"col":11,"byte":645},"end":{"line":28,"col":38,"byte":672}},"renamed\u001fpatient_id":{"start":{"line":8,"col":9,"byte":121},"end":{"line":8,"col":25,"byte":137}},"renamed\u001frace":{"start":{"line":17,"col":11,"byte":354},"end":{"line":17,"col":23,"byte":366}},"renamed\u001fssn_hash":{"start":{"line":26,"col":11,"byte":577},"end":{"line":26,"col":31,"byte":597}},"renamed\u001fstate":{"start":{"line":34,"col":11,"byte":792},"end":{"line":34,"col":25,"byte":806}},"renamed\u001fzip_code":{"start":{"line":36,"col":11,"byte":844},"end":{"line":36,"col":26,"byte":859}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}},"raw_node_spans":{"final":{"start":{"line":46,"col":1,"byte":1074},"end":{"line":51,"col":2,"byte":1166}},"renamed":{"start":{"line":5,"col":1,"byte":79},"end":{"line":44,"col":2,"byte":1071}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":76}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":49,"col":32,"byte":1137},"end":{"line":49,"col":42,"byte":1147}},"renamed\u001faddress":{"start":{"line":32,"col":22,"byte":764},"end":{"line":32,"col":29,"byte":771}},"renamed\u001fbirth_date":{"start":{"line":22,"col":24,"byte":490},"end":{"line":22,"col":34,"byte":500}},"renamed\u001fbirth_place":{"start":{"line":31,"col":25,"byte":731},"end":{"line":31,"col":36,"byte":742}},"renamed\u001fcity":{"start":{"line":33,"col":19,"byte":790},"end":{"line":33,"col":23,"byte":794}},"renamed\u001fcounty":{"start":{"line":35,"col":21,"byte":840},"end":{"line":35,"col":27,"byte":846}},"renamed\u001fdeath_date":{"start":{"line":23,"col":24,"byte":524},"end":{"line":23,"col":34,"byte":534}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":22,"byte":632},"end":{"line":27,"col":37,"byte":647}},"renamed\u001fethnicity":{"start":{"line":18,"col":24,"byte":403},"end":{"line":18,"col":33,"byte":412}},"renamed\u001ffirst_name":{"start":{"line":11,"col":20,"byte":195},"end":{"line":11,"col":30,"byte":205}},"renamed\u001fgender":{"start":{"line":16,"col":21,"byte":350},"end":{"line":16,"col":27,"byte":356}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":34,"byte":1034},"end":{"line":42,"col":53,"byte":1053}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":34,"byte":981},"end":{"line":41,"col":53,"byte":1000}},"renamed\u001flast_name":{"start":{"line":12,"col":19,"byte":224},"end":{"line":12,"col":28,"byte":233}},"renamed\u001flatitude":{"start":{"line":37,"col":18,"byte":890},"end":{"line":37,"col":26,"byte":898}},"renamed\u001flongitude":{"start":{"line":38,"col":18,"byte":916},"end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data_tests:\n          - not_null\n          - accepted_values:\n              values: ['M', 'F']\n          # String pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^[MF]$\"\n              config:\n                severity: warn\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          # Note: Uses year extraction to avoid date type casting issues with DuckDB\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: ssn_hash\n        description: MD5 hash of Social Security Number (masked for privacy)\n\n      - name: drivers_license\n        description: Driver's license number\n\n      - name: passport_number\n        description: Passport number\n\n      - name: birth_place\n        description: Place of birth\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name, e.g., Massachusetts)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n        data_tests:\n          # String pattern validation (v0.8 - dbt_expectations)\n          # ZIP code format: 5 digits or 5+4 format\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{5}(-\\\\d{4})?$\"\n              row_condition: \"zip_code is not null\"\n              config:\n                severity: warn\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded into staging"},"tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
+<script type="application/json" id="cute-dbt-data">{"baseline":"tests/fixtures/playground-baseline.json","models":[{"name":"dim_payers","path":"models/marts/core/dim_payers.sql","description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"sequenced","role":"transform"},{"id":"unknown_member","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"dim_payers.sql","role":"final"}],"edges":[{"from":"source","to":"sequenced","edge_type":"from"},{"from":"unknown_member","to":"final","edge_type":"union_all"},{"from":"sequenced","to":"final","edge_type":"union_all"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","final":"final as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)","sequenced":"sequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n)","unknown_member":"unknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select * from \"memory\".\"main_staging\".\"stg_synthea__payers\"\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","node_spans":{"(final select)":{"start":{"line":91,"col":1,"byte":2533},"end":{"line":101,"col":106,"byte":3101}},"final":{"start":{"line":85,"col":1,"byte":2444},"end":{"line":89,"col":2,"byte":2531}},"sequenced":{"start":{"line":7,"col":1,"byte":87},"end":{"line":54,"col":2,"byte":1209}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":5,"col":2,"byte":84}},"unknown_member":{"start":{"line":57,"col":1,"byte":1269},"end":{"line":83,"col":2,"byte":2441}}},"column_spans":{"(final 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_member\u001funcovered_encounters":{"start":{"line":71,"col":16,"byte":1815},"end":{"line":71,"col":55,"byte":1854}},"unknown_member\u001funcovered_immunizations":{"start":{"line":77,"col":16,"byte":2144},"end":{"line":77,"col":58,"byte":2186}},"unknown_member\u001funcovered_medications":{"start":{"line":73,"col":16,"byte":1924},"end":{"line":73,"col":56,"byte":1964}},"unknown_member\u001funcovered_procedures":{"start":{"line":75,"col":16,"byte":2033},"end":{"line":75,"col":55,"byte":2072}},"unknown_member\u001funique_customers":{"start":{"line":78,"col":16,"byte":2202},"end":{"line":78,"col":51,"byte":2237}},"unknown_member\u001fzip_code":{"start":{"line":65,"col":16,"byte":1535},"end":{"line":65,"col":44,"byte":1563}}},"raw_node_spans":{"final":{"start":{"line":85,"col":1,"byte":2515},"end":{"line":89,"col":2,"byte":2602}},"sequenced":{"start":{"line":7,"col":1,"byte":158},"end":{"line":54,"col":2,"byte":1280}},"source":{"start":{"line":3,"col":6,"byte":91},"end":{"line":5,"col":2,"byte":155}},"unknown_member":{"start":{"line":57,"col":1,"byte":1340},"end":{"line":83,"col":2,"byte":2512}}},"raw_dag":{"nodes":[{"id":"final","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"sequenced","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"source","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"unknown_member","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"final":"final","sequenced":"sequenced","source":"source","unknown_member":"unknown_member"}}},"column_lineage":{"context":{"_loaded_at":{"description":"Timestamp when record was loaded","documented":true},"address":{"description":"Street address","documented":true},"amount_covered":{"description":"Total amount covered by payer","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"amount_covered","min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"amount_covered is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_amount_covered__0__amount_covered_is_not_null.cd9b3952ed"}],"documented":true},"amount_uncovered":{"description":"Total amount not covered by payer","documented":true},"average_quality_of_life_score":{"description":"Average quality of life score for members","documented":true},"city":{"description":"City","documented":true},"coverage_rate":{"description":"Derived coverage rate (amount_covered / total amount)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"coverage_rate","max_value":1,"min_value":0,"model":"{{ get_where_subquery(ref('dim_payers')) }}","row_condition":"coverage_rate is not null"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_dim_payers_coverage_rate__1__0__coverage_rate_is_not_null.8999cddc49"}],"documented":true},"covered_encounters":{"description":"Number of encounters covered","documented":true},"covered_immunizations":{"description":"Number of immunizations covered","documented":true},"covered_medications":{"description":"Number of medications covered","documented":true},"covered_procedures":{"description":"Number of procedures covered","documented":true},"member_months":{"description":"Total member months","documented":true},"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_id","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_id.d533a298a8"}],"documented":true},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_key.1db9eefbf7"},{"kind":"unique","kwargs":{"column_name":"payer_key","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"}],"documented":true},"payer_name":{"description":"Name of the insurance payer","tests":[{"kind":"not_null","kwargs":{"column_name":"payer_name","model":"{{ get_where_subquery(ref('dim_payers')) }}"},"node_id":"test.healthcare_analytics.not_null_dim_payers_payer_name.a40249ff7e"}],"documented":true},"phone":{"description":"Phone number","documented":true},"revenue":{"description":"Total revenue 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select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        unique_key='payer_key'\n    )\n}}\n\nwith source as (\n    select * from {{ ref('stg_synthea__payers') }}\n),\n\nsequenced as (\n    select\n        -- surrogate key (starting from 1, reserving -1 for unknown)\n        row_number() over (order by payer_id) as payer_key\n\n        -- natural key\n        , payer_id\n\n        -- attributes\n        , payer_name\n        , address\n        , city\n        , state\n        , zip_code\n        , phone\n\n        -- financial metrics\n        , amount_covered\n        , amount_uncovered\n        , revenue\n\n        -- utilization metrics\n        , covered_encounters\n        , uncovered_encounters\n        , covered_medications\n        , uncovered_medications\n        , covered_procedures\n        , uncovered_procedures\n        , covered_immunizations\n        , uncovered_immunizations\n\n        -- member metrics\n        , unique_customers\n        , average_quality_of_life_score\n        , member_months\n\n        -- derived metrics\n        , case\n            when (amount_covered + amount_uncovered) > 0\n            then amount_covered / (amount_covered + amount_uncovered)\n            else 0\n        end as coverage_rate\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from source\n),\n\n-- unknown member for handling missing dimension lookups\nunknown_member as (\n    select\n        -1 as payer_key\n        , 'UNKNOWN' as payer_id\n        , 'Unknown Payer' as payer_name\n        , cast(null as varchar) as address\n        , cast(null as varchar) as city\n        , cast(null as varchar) as state\n        , cast(null as varchar) as zip_code\n        , cast(null as varchar) as phone\n        , cast(null as double) as amount_covered\n        , cast(null as double) as amount_uncovered\n        , cast(null as double) as revenue\n        , cast(null as bigint) as covered_encounters\n        , cast(null as bigint) as uncovered_encounters\n        , cast(null as bigint) as covered_medications\n        , cast(null as bigint) as uncovered_medications\n        , cast(null as bigint) as covered_procedures\n        , cast(null as bigint) as uncovered_procedures\n        , cast(null as bigint) as covered_immunizations\n        , cast(null as bigint) as uncovered_immunizations\n        , cast(null as bigint) as unique_customers\n        , cast(null as double) as average_quality_of_life_score\n        , cast(null as bigint) as member_months\n        , cast(null as double) as coverage_rate\n        , current_timestamp as _loaded_at\n),\n\nfinal as (\n    select * from unknown_member\n    union all\n    select * from sequenced\n)\n\nselect * from final\n\n-- ---------------------------------------------------------------------------\n-- cute-dbt dogfood (no-op): this trailing comment is the ONLY model change in\n-- the demo PR. It places dim_payers.sql in the PR diff so cute-dbt scopes the\n-- report to dim_payers + its unit test (test_dim_payers_injects_unknown_sentinel).\n-- ---------------------------------------------------------------------------\n\n-- cute-dbt fixture: synthetic body mod\n\n-- cute-dbt: ignore(union.arm-coverage, \"unknown_member is a constant sentinel arm, exercised by design\")","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: dim_payers\n    description: >\n      Payer dimension containing insurance payers.\n      Includes an unknown member row (payer_key = -1) for handling missing lookups.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 5\n          config:\n            severity: warn\n    columns:\n      - name: payer_key\n        description: Surrogate key (-1 for unknown member)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: payer_id\n        description: Natural payer identifier (UUID)\n        data_tests:\n          - not_null\n\n      - name: payer_name\n        description: Name of the insurance payer\n        data_tests:\n          - not_null\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City\n\n      - name: state\n        description: State\n\n      - name: zip_code\n        description: ZIP code\n\n      - name: phone\n        description: Phone number\n\n      - name: amount_covered\n        description: Total amount covered by payer\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"amount_covered is not null\"\n              config:\n                severity: warn\n\n      - name: amount_uncovered\n        description: Total amount not covered by payer\n\n      - name: revenue\n        description: Total revenue collected\n        data_tests:\n          # Value must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"revenue is not null\"\n              config:\n                severity: warn\n\n      - name: covered_encounters\n        description: Number of encounters covered\n\n      - name: uncovered_encounters\n        description: Number of encounters not covered\n\n      - name: covered_medications\n        description: Number of medications covered\n\n      - name: uncovered_medications\n        description: Number of medications not covered\n\n      - name: covered_procedures\n        description: Number of procedures covered\n\n      - name: uncovered_procedures\n        description: Number of procedures not covered\n\n      - name: covered_immunizations\n        description: Number of immunizations covered\n\n      - name: uncovered_immunizations\n        description: Number of immunizations not covered\n\n      - name: unique_customers\n        description: Number of unique customers\n\n      - name: average_quality_of_life_score\n        description: Average quality of life score for members\n\n      - name: member_months\n        description: Total member months\n\n      - name: coverage_rate\n        description: Derived coverage rate (amount_covered / total amount)\n        data_tests:\n          # Coverage rate must be 0-1 (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 1\n              row_condition: \"coverage_rate is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[{"id":"unit_test.healthcare_analytics.dim_payers.test_dim_payers_injects_unknown_sentinel","name":"test_dim_payers_injects_unknown_sentinel","target_model":"dim_payers","changed":false,"description":"dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\nwith the sequenced source rows. This test verifies the sentinel\nsurvives the union and the sequenced rows are surrogate-keyed\nstarting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\ndict format only compares listed columns, so non-deterministic\noutputs (`_loaded_at` via `current_timestamp`) are excluded from\nassertion automatically.\n","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt's authoring-YAML drawer (since #69) attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test to the test's authored slice. This block demonstrates the\n  # LEADING bracket — useful for stating the test's intent before the\n  # reader sees the assertion (cute-dbt#73).\n  - name: test_dim_payers_injects_unknown_sentinel\n    description: |\n      dim_payers UNION-ALLs an unknown-member sentinel row (payer_key = -1)\n      with the sequenced source rows. This test verifies the sentinel\n      survives the union and the sequenced rows are surrogate-keyed\n      starting at 1. Uses dbt's dict format for both GIVEN and EXPECT —\n      dict format only compares listed columns, so non-deterministic\n      outputs (`_loaded_at` via `current_timestamp`) are excluded from\n      assertion automatically.\n    model: dim_payers\n    # The INSIDE bracket — comments at deeper indent than `- name:` ride\n    # along with the body slice as raw text. Cute-dbt preserves them\n    # verbatim in the Authoring YAML drawer (cute-dbt#73).\n    given:\n      - input: ref('stg_synthea__payers')\n        format: dict\n        rows:\n          - payer_id: 'a00000aa-0000-0000-0000-000000000001'\n            payer_name: 'Acme Health'\n            address: '1 Acme Way'\n            city: 'Boston'\n            state: 'MA'\n            zip_code: '02101'\n            phone: '555-0100'\n            amount_covered: 1000.0\n            amount_uncovered: 250.0\n            revenue: 1250.0\n            covered_encounters: 10\n            uncovered_encounters: 2\n            covered_medications: 5\n            uncovered_medications: 1\n            covered_procedures: 3\n            uncovered_procedures: 0\n            covered_immunizations: 4\n            uncovered_immunizations: 0\n            unique_customers: 8\n            average_quality_of_life_score: 0.85\n            member_months: 96\n    expect:\n      format: dict\n      rows:\n        - payer_key: -1\n          payer_id: 'UNKNOWN'\n          payer_name: 'Unknown Payer'\n        - payer_key: 1\n          payer_id: 'a00000aa-0000-0000-0000-000000000001'\n          payer_name: 'Acme Health'\n  # The TRAILING bracket — contiguous `#`-comment lines at the SAME\n  # indent as `- name:`, immediately following the test body and\n  # terminated by EOF or a blank line, are attached to this test's\n  # slice. Useful for cross-references and follow-up notes (cute-dbt#73).","given":[{"input":"ref('stg_synthea__payers')","bound_to_node":"source","table":{"columns":["address","amount_covered","amount_uncovered","average_quality_of_life_score","city","covered_encounters","covered_immunizations","covered_medications","covered_procedures","member_months","payer_id","payer_name","phone","revenue","state","uncovered_encounters","uncovered_immunizations","uncovered_medications","uncovered_procedures","unique_customers","zip_code"],"rows":[{"cells":[{"display":"1 Acme Way","key":{"t":"str","v":"1 Acme Way"}},{"display":"1000.0","key":{"t":"number","v":"1000"}},{"display":"250.0","key":{"t":"number","v":"250"}},{"display":"0.85","key":{"t":"number","v":"0.85"}},{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"10","key":{"t":"number","v":"10"}},{"display":"4","key":{"t":"number","v":"4"}},{"display":"5","key":{"t":"number","v":"5"}},{"display":"3","key":{"t":"number","v":"3"}},{"display":"96","key":{"t":"number","v":"96"}},{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}},{"display":"555-0100","key":{"t":"str","v":"555-0100"}},{"display":"1250.0","key":{"t":"number","v":"1250"}},{"display":"MA","key":{"t":"str","v":"MA"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"8","key":{"t":"number","v":"8"}},{"display":"02101","key":{"t":"str","v":"02101"}}]}]},"rows":[{"address":"1 Acme Way","amount_covered":1000.0,"amount_uncovered":250.0,"average_quality_of_life_score":0.85,"city":"Boston","covered_encounters":10,"covered_immunizations":4,"covered_medications":5,"covered_procedures":3,"member_months":96,"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_name":"Acme Health","phone":"555-0100","revenue":1250.0,"state":"MA","uncovered_encounters":2,"uncovered_immunizations":0,"uncovered_medications":1,"uncovered_procedures":0,"unique_customers":8,"zip_code":"02101"}],"format":"dict","column_meta":{"address":{"description":"Street address"},"amount_covered":{"description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"amount_uncovered":{"description":"Total amount not covered by payer"},"average_quality_of_life_score":{"description":"Average quality of life score for members"},"city":{"description":"City"},"covered_encounters":{"description":"Number of encounters covered"},"covered_immunizations":{"description":"Number of immunizations covered"},"covered_medications":{"description":"Number of medications covered"},"covered_procedures":{"description":"Number of procedures covered"},"member_months":{"description":"Total member months"},"payer_id":{"description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]},"phone":{"description":"Phone number"},"revenue":{"description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"state":{"description":"State where headquartered"},"uncovered_encounters":{"description":"Number of encounters not covered"},"uncovered_immunizations":{"description":"Number of immunizations not covered"},"uncovered_medications":{"description":"Number of medications not covered"},"uncovered_procedures":{"description":"Number of procedures not covered"},"unique_customers":{"description":"Number of unique customers"},"zip_code":{"description":"ZIP code (stored as numeric, 5 digits)"}}}],"expected":{"table":{"columns":["payer_id","payer_key","payer_name"],"rows":[{"cells":[{"display":"UNKNOWN","key":{"t":"str","v":"UNKNOWN"}},{"display":"-1","key":{"t":"number","v":"-1"}},{"display":"Unknown Payer","key":{"t":"str","v":"Unknown Payer"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"Acme Health","key":{"t":"str","v":"Acme Health"}}]}]},"rows":[{"payer_id":"UNKNOWN","payer_key":-1,"payer_name":"Unknown Payer"},{"payer_id":"a00000aa-0000-0000-0000-000000000001","payer_key":1,"payer_name":"Acme Health"}],"format":"dict","column_meta":{"payer_id":{"description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},"payer_key":{"description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},"payer_name":{"description":"Name of the insurance payer","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.dim_payers","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2"]},"evidence":[{"label":"unique_key","value":"payer_key"}],"degraded":[{"by":"test.healthcare_analytics.unique_dim_payers_payer_key.953b1f5fd2","causes":["severity: warn — a failing run warns instead of failing, so this backing is advisory, not gating"]}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.dim_payers","construct":"union[final]","verdict":{"status":"unknown"},"evidence":[{"label":"union","value":"final (2 arms: sequenced, unknown_member)"},{"label":"unattributable arm","value":"unknown_member — no resolvable upstream relation"}],"suppressed":{"source":"pragma","reason":"unknown_member is a constant sentinel arm, exercised by design"},"pin_node":"final"}]},{"name":"fct_clinical_events","path":"models/marts/core/fct_clinical_events.sql","description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","dag":{"nodes":[{"id":"valid_encounters","role":"import"},{"id":"conditions","role":"import"},{"id":"medications","role":"import"},{"id":"procedures","role":"import"},{"id":"all_events","role":"transform"},{"id":"patients","role":"import"},{"id":"dim_date","role":"import"},{"id":"encounters","role":"import"},{"id":"final","role":"transform"},{"id":"(final select)","label":"fct_clinical_events.sql","role":"final"}],"edges":[{"from":"conditions","to":"all_events","edge_type":"union_all"},{"from":"medications","to":"all_events","edge_type":"union_all"},{"from":"procedures","to":"all_events","edge_type":"union_all"},{"from":"all_events","to":"final","edge_type":"from"},{"from":"patients","to":"final","edge_type":"left"},{"from":"encounters","to":"final","edge_type":"left"},{"from":"dim_date","to":"final","edge_type":"left"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","all_events":"all_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n)","conditions":"conditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n)","dim_date":"dim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","encounters":"encounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n)","final":"final as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)","medications":"medications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n)","patients":"patients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n)","procedures":"procedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n)","valid_encounters":"valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n)"},"code_map":{"compiled":"\n\nwith valid_encounters as (\n    select encounter_id\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n    \n    \n    where is_dq_valid = true\n    \n\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__conditions\" as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\" as med\n    \n    \n    where is_dq_valid = true\n    \n\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from \"memory\".\"main_staging\".\"stg_synthea__procedures\" as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from \"memory\".\"main_marts\".\"dim_patients\"\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from \"memory\".\"main_marts\".\"fct_encounters\"\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final 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unique_key='clinical_event_key'\n    )\n}}\n\nwith valid_encounters as (\n    select encounter_id\n    from {{ ref('stg_synthea__encounters') }}\n    {{ quarantine_filter() }}\n),\n\nconditions as (\n    select\n        condition_id as event_id\n        , 'CONDITION' as event_type\n        , patient_id\n        , cond.encounter_id\n        , condition_start_date as event_start_date\n        , condition_end_date as event_end_date\n        , condition_code as code\n        , 'SNOMED-CT' as code_system\n        , condition_description as description\n        , cast(null as varchar) as reason_code\n        , cast(null as varchar) as reason_description\n        , cast(null as decimal(18, 2)) as event_cost\n    from {{ ref('stg_synthea__conditions') }} as cond\n    where cond.encounter_id in (select encounter_id from valid_encounters)\n),\n\nmedications as (\n    select\n        medication_id as event_id\n        , 'MEDICATION' as event_type\n        , patient_id\n        , med.encounter_id\n        , cast(medication_start_at as date) as event_start_date\n        , cast(medication_end_at as date) as event_end_date\n        , medication_code as code\n        , 'RXNORM' as code_system\n        , medication_description as description\n        , reason_code\n        , reason_description\n        , total_cost as event_cost\n    from {{ ref('stg_synthea__medications') }} as med\n    {{ quarantine_filter() }}\n    and med.encounter_id in (select encounter_id from valid_encounters)\n),\n\nprocedures as (\n    select\n        procedure_id as event_id\n        , 'PROCEDURE' as event_type\n        , patient_id\n        , proc.encounter_id\n        , cast(procedure_at as date) as event_start_date\n        , cast(null as date) as event_end_date\n        , procedure_code as code\n        , 'SNOMED-CT' as code_system\n        , procedure_description as description\n        , reason_code\n        , reason_description\n        , base_cost as event_cost\n    from {{ ref('stg_synthea__procedures') }} as proc\n    where proc.encounter_id in (select encounter_id from valid_encounters)\n),\n\nall_events as (\n    select * from conditions\n    union all\n    select * from medications\n    union all\n    select * from procedures\n),\n\npatients as (\n    select\n        patient_key\n        , patient_id\n    from {{ ref('dim_patients') }}\n    where is_current = true\n),\n\ndim_date as (\n    select\n        date_key\n        , date_actual\n    from {{ ref('dim_date') }}\n),\n\nencounters as (\n    select\n        encounter_key\n        , encounter_id\n    from {{ ref('fct_encounters') }}\n),\n\nfinal as (\n    select\n        -- surrogate key\n        row_number() over (order by e.event_id) as clinical_event_key\n\n        -- event identifiers\n        , e.event_type\n        , e.event_id\n\n        -- dimension keys\n        , coalesce(p.patient_key, -1) as patient_key\n        , e.patient_id\n        , coalesce(enc.encounter_key, -1) as encounter_key\n        , e.encounter_id\n        , coalesce(d.date_key, -1) as event_date_key\n\n        -- dates\n        , e.event_start_date\n        , e.event_end_date\n\n        -- code information\n        , e.code\n        , e.code_system\n        , e.description\n\n        -- reason\n        , e.reason_code\n        , e.reason_description\n\n        -- cost\n        , e.event_cost\n\n        -- metadata\n        , current_timestamp as _loaded_at\n\n    from all_events as e\n    left join patients as p on e.patient_id = p.patient_id\n    left join encounters as enc on e.encounter_id = enc.encounter_id\n    left join dim_date as d on e.event_start_date = d.date_actual\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  - name: fct_clinical_events\n    description: >\n      Unified clinical events fact table combining conditions, medications, and procedures.\n      Each row represents a single clinical event with standardized structure.\n      Links to patient and encounter dimensions.\n    data_tests:\n      # Row count validation - should be sum of conditions + medications + procedures (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 60000\n          config:\n            severity: warn\n    columns:\n      - name: clinical_event_key\n        description: Surrogate key for the clinical event\n        data_tests:\n          - unique\n          - not_null\n\n      - name: event_type\n        description: Type of clinical event (CONDITION, MEDICATION, PROCEDURE)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['CONDITION', 'MEDICATION', 'PROCEDURE']\n\n      - name: event_id\n        description: Natural event identifier (condition_id, medication_id, or procedure_id)\n        data_tests:\n          - not_null\n\n      - name: patient_key\n        description: Foreign key to dim_patients (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_patients')\n              field: patient_key\n\n      - name: patient_id\n        description: Natural patient identifier\n        data_tests:\n          - not_null\n\n      - name: encounter_key\n        description: Foreign key to fct_encounters (-1 for unknown)\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('fct_encounters')\n              field: encounter_key\n\n      - name: encounter_id\n        description: Natural encounter identifier\n        data_tests:\n          - not_null\n\n      - name: event_date_key\n        description: Foreign key to dim_date for event start\n        data_tests:\n          - not_null\n          - relationships:\n              to: ref('dim_date')\n              field: date_key\n              config:\n                where: \"event_date_key != -1\"\n\n      - name: event_start_date\n        description: Start date of the clinical event\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: event_end_date\n        description: End date of the clinical event (null if ongoing or one-time)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"event_end_date is not null\"\n              config:\n                severity: warn\n\n      - name: code\n        description: Clinical code (SNOMED-CT or RxNorm)\n        data_tests:\n          - not_null\n\n      - name: code_system\n        description: Code system (SNOMED-CT or RXNORM)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['SNOMED-CT', 'RXNORM']\n\n      - name: description\n        description: Description of the clinical event\n        data_tests:\n          - not_null\n\n      - name: reason_code\n        description: Clinical code for the reason of the event\n\n      - name: reason_description\n        description: Description of the reason for the event\n\n      - name: event_cost\n        description: Cost of the clinical event\n        data_tests:\n          # Cost must be non-negative (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"event_cost is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded"},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_clinical_events_clinical_event_key.2af827606b"]},"evidence":[{"label":"unique_key","value":"clinical_event_key"}],"pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_clinical_events","construct":"union[all_events]","verdict":{"status":"uncovered"},"evidence":[{"label":"union","value":"all_events (3 arms: conditions, medications, procedures)"},{"label":"unexercised arm","value":"conditions — no given row reaches ref('stg_synthea__conditions')"},{"label":"unexercised arm","value":"medications — no given row reaches ref('stg_synthea__medications')"},{"label":"unexercised arm","value":"procedures — no given row reaches ref('stg_synthea__procedures')"}],"recommendation":"Add (or fill) a given row for each unexercised UNION arm's input so every arm contributes at least one row, then extend `expect` with the row(s) that arm should emit. This finding's evidence carries the per-arm input and a given-row sketch.","pin_node":"all_events","sketches":["- input: ref('stg_synthea__conditions')\n  rows:\n    - {_loaded_at: ..., condition_code: ..., condition_description: ..., condition_end_date: ..., condition_id: ..., condition_start_date: ..., encounter_id: ..., patient_id: ...}","- input: ref('stg_synthea__medications')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., dispenses: ..., encounter_id: ..., medication_code: ..., medication_description: ..., medication_end_at: ..., medication_id: ..., medication_start_at: ..., patient_id: ..., payer_coverage: ..., payer_id: ..., reason_code: ..., reason_description: ..., total_cost: ...}","- input: ref('stg_synthea__procedures')\n  rows:\n    - {_loaded_at: ..., base_cost: ..., encounter_id: ..., patient_id: ..., procedure_at: ..., procedure_code: ..., procedure_description: ..., procedure_id: ..., reason_code: ..., reason_description: ...}"]}]},{"name":"fct_encounters_incremental","path":"models/marts/core/fct_encounters_incremental.sql","description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","dag":{"nodes":[{"id":"source","role":"import"},{"id":"(final select)","label":"fct_encounters_incremental.sql","role":"final"}],"edges":[{"from":"source","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","source":"source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)"},"code_map":{"compiled":"\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        , round(total_cost, 2) as total_cost_rounded\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n\nwhere start_time > (select max(start_time) from \"memory\".\"main_marts\".\"fct_encounters_incremental\")\n","node_spans":{"(final select)":{"start":{"line":13,"col":1,"byte":233},"end":{"line":22,"col":1,"byte":450}},"source":{"start":{"line":3,"col":6,"byte":7},"end":{"line":11,"col":2,"byte":231}}},"raw_zones":[{"kind":"for_loop","start":{"line":16,"col":9,"byte":304},"end":{"line":18,"col":21,"byte":428},"presence":"structural","node_id":"source"},{"kind":"incremental_guard","start":{"line":29,"col":1,"byte":594},"end":{"line":31,"col":12,"byte":691},"presence":"structural","node_id":"(final select)"}],"column_spans":{"(final select)\u001fencounter_id":{"start":{"line":14,"col":5,"byte":244},"end":{"line":14,"col":17,"byte":256}},"(final select)\u001fencounter_type":{"start":{"line":16,"col":7,"byte":280},"end":{"line":16,"col":21,"byte":294}},"(final select)\u001fpatient_id":{"start":{"line":15,"col":7,"byte":263},"end":{"line":15,"col":17,"byte":273}},"(final select)\u001fstart_time":{"start":{"line":17,"col":7,"byte":301},"end":{"line":17,"col":17,"byte":311}},"(final select)\u001ftotal_cost_rounded":{"start":{"line":18,"col":7,"byte":318},"end":{"line":18,"col":25,"byte":336}},"source\u001fencounter_id":{"start":{"line":5,"col":9,"byte":38},"end":{"line":5,"col":21,"byte":50}},"source\u001fencounter_type":{"start":{"line":7,"col":11,"byte":82},"end":{"line":7,"col":25,"byte":96}},"source\u001fpatient_id":{"start":{"line":6,"col":11,"byte":61},"end":{"line":6,"col":21,"byte":71}},"source\u001fstart_time":{"start":{"line":8,"col":11,"byte":107},"end":{"line":8,"col":21,"byte":117}},"source\u001ftotal_cost_rounded":{"start":{"line":9,"col":11,"byte":128},"end":{"line":9,"col":53,"byte":170}}},"raw_node_spans":{"source":{"start":{"line":3,"col":6,"byte":185},"end":{"line":13,"col":2,"byte":476}}},"raw_column_spans":{"(final select)\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}},"source\u001ftotal_cost_rounded":{"start":{"line":20,"col":7,"byte":563},"end":{"line":20,"col":25,"byte":581}}},"raw_dag":{"nodes":[{"id":"source","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"zone:0","role":"zone","is_zone":true,"presence":"structural"},{"id":"zone:1","role":"zone","is_zone":true,"presence":"structural"}]},"node_map":{"compiled":{"source":"source"}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.not_null_fct_encounters_incremental_encounter_id.b165c01d02"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('fct_encounters_incremental')) }}"},"node_id":"test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"}],"documented":true},"encounter_type":{"description":"Visit class (wellness, emergency, ...)","documented":true},"patient_id":{"description":"Patient the encounter belongs to","documented":true},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark","documented":true},"total_cost":{"description":"Total claim cost for the encounter","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"encounter_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"encounter_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"start_time"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"start_time"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"total_cost_rounded"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_cost_rounded"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='incremental',\n        unique_key='encounter_id',\n        incremental_strategy='merge',\n        on_schema_change='append_new_columns'\n    )\n}}\n\nwith source as (\n    select\n        encounter_id\n        , patient_id\n        , encounter_type\n        , start_time\n        {% for cost_col in rounded_cost_columns %}\n        , round({{ cost_col }}, 2) as {{ cost_col }}_rounded\n        {% endfor %}\n    from {{ ref('stg_synthea__encounters') }}\n)\n\nselect\n    encounter_id\n    , patient_id\n    , encounter_type\n    , start_time\n    , total_cost_rounded\nfrom source\n{% if is_incremental() %}\nwhere start_time > (select max(start_time) from {{ this }})\n{% endif %}\n","model_yaml":{"path":"models/marts/core/_core__models.yml","raw":"  # cute-dbt#165 — column-header tooltip dogfood on the incremental model:\n  # authored descriptions + unique/not_null on the merge key, so the\n  # playground golden surfaces the th tooltips on this model's `this`\n  # given, ref given, and expect tables.\n  - name: fct_encounters_incremental\n    description: >\n      Incremental encounter accumulator. On incremental runs it only merges\n      encounters newer than the loaded start_time high-water mark.\n    columns:\n      - name: encounter_id\n        description: Natural key of the encounter (unique per visit)\n        data_tests:\n          - unique\n          - not_null\n      - name: patient_id\n        description: Patient the encounter belongs to\n      - name: encounter_type\n        description: Visit class (wellness, emergency, ...)\n      - name: start_time\n        description: Encounter start timestamp - the incremental high-water mark\n      - name: total_cost\n        description: Total claim cost for the encounter"},"tests":[{"id":"unit_test.healthcare_analytics.fct_encounters_incremental.test_fct_encounters_incremental_appends_new_encounters","name":"test_fct_encounters_incremental_appends_new_encounters","target_model":"fct_encounters_incremental","changed":true,"description":"fct_encounters_incremental appends encounters newer than the latest loaded start_time. With is_incremental() true only the new encounter is merged; Expected is that single inserted row (the materialization delta), not the resulting table.","tags":[],"meta":{},"defined_in":"models/marts/core/_core__models.yml","authoring_yaml":"  # cute-dbt#145 — an incremental-model unit test. `overrides.macros.\n  # is_incremental: true` drives the model's is_incremental() branch,\n  # `given: - input: this` supplies the model's prior state, and Expected is\n  # the rows MERGED/INSERTED by the run — not the final table after the merge.\n  - name: test_fct_encounters_incremental_appends_new_encounters\n    description: |\n      fct_encounters_incremental appends encounters newer than the latest\n      already-loaded start_time. With is_incremental() true, only the new\n      encounter (e2) is merged; the prior row (e1, supplied via `this`) is\n      untouched, so Expected is the single inserted row — the materialization\n      delta, not the resulting table.\n    model: fct_encounters_incremental\n    overrides:\n      macros:\n        is_incremental: true\n    given:\n      - input: this\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n      - input: ref('stg_synthea__encounters')\n        format: dict\n        rows:\n          - encounter_id: 'e1'\n            patient_id: 'p1'\n            encounter_type: 'wellness'\n            start_time: '2025-01-01 09:00:00'\n            total_cost: 100.0\n          - encounter_id: 'e2'\n            patient_id: 'p2'\n            encounter_type: 'emergency'\n            start_time: '2025-02-01 14:30:00'\n            total_cost: 500.0\n    expect:\n      format: dict\n      rows:\n        - encounter_id: 'e2'\n          patient_id: 'p2'\n          encounter_type: 'emergency'\n          start_time: '2025-02-01 14:30:00'\n          total_cost: 500.0","is_incremental_mode":true,"overrides":{"macros":{"is_incremental":true}},"given":[{"input":"this","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0}],"format":"dict","is_this":true,"column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}},{"input":"ref('stg_synthea__encounters')","bound_to_node":"source","table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e1","key":{"t":"str","v":"e1"}},{"display":"wellness","key":{"t":"str","v":"wellness"}},{"display":"p1","key":{"t":"str","v":"p1"}},{"display":"2025-01-01 09:00:00","key":{"t":"str","v":"2025-01-01 09:00:00"}},{"display":"100.0","key":{"t":"number","v":"100"}}]},{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e1","encounter_type":"wellness","patient_id":"p1","start_time":"2025-01-01 09:00:00","total_cost":100.0},{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"patient_id":{"description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]}}}],"expected":{"table":{"columns":["encounter_id","encounter_type","patient_id","start_time","total_cost"],"rows":[{"cells":[{"display":"e2","key":{"t":"str","v":"e2"}},{"display":"emergency","key":{"t":"str","v":"emergency"}},{"display":"p2","key":{"t":"str","v":"p2"}},{"display":"2025-02-01 14:30:00","key":{"t":"str","v":"2025-02-01 14:30:00"}},{"display":"500.0","key":{"t":"number","v":"500"}}]}]},"rows":[{"encounter_id":"e2","encounter_type":"emergency","patient_id":"p2","start_time":"2025-02-01 14:30:00","total_cost":500.0}],"format":"dict","column_meta":{"encounter_id":{"description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},"encounter_type":{"description":"Visit class (wellness, emergency, ...)"},"patient_id":{"description":"Patient the encounter belongs to"},"start_time":{"description":"Encounter start timestamp - the incremental high-water mark"},"total_cost":{"description":"Total claim cost for the encounter"}}}}],"is_recursive":false,"is_incremental":true,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.unique_key","verdict":{"status":"covered","by":["test.healthcare_analytics.unique_fct_encounters_incremental_encounter_id.a165c01d01"]},"evidence":[{"label":"unique_key","value":"encounter_id"}],"pin_node":"(final select)"},{"check":"incremental.branch-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.fct_encounters_incremental","construct":"config.materialized","verdict":{"status":"uncovered"},"evidence":[{"label":"materialized","value":"incremental (strategy: merge)"},{"label":"branch coverage","value":"true-only — 1 test(s) override is_incremental to true; none exercise the initial full-build (false) branch"},{"label":"missing branch","value":"the initial full-build branch (is_incremental() = false) runs in no unit test"}],"recommendation":"Add a unit test for each missing is_incremental() branch: one with `overrides: macros: is_incremental: true` (mock the prior model state with a `given: - input: this` entry) to exercise the incremental branch, and one without the override for the initial full build (dbt compiles is_incremental() as false by default). This finding's evidence carries a copy-pasteable unit-test sketch per missing branch.","pin_node":"(final select)","sketches":["- name: test_fct_encounters_incremental_initial_build\n  model: fct_encounters_incremental\n  # no overrides block — dbt compiles is_incremental() as false by default\n  given:\n    - input: ref('...')\n      rows:\n        - {...}\n  # expect: the full first-build output"]}]},{"name":"int_dq_quarantine__encounters","path":"models/intermediate/quarantine/int_dq_quarantine__encounters.sql","description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod"},"code_map":{"compiled":"\n\n\n\n-- Encounters quarantined due to data quality violations\n\nselect * from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\nwhere is_dq_valid = false\n\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":11,"col":40,"byte":193}}},"column_spans":{"(final select)\u001f*":{"start":{"line":7,"col":8,"byte":69},"end":{"line":7,"col":9,"byte":70}}},"raw_dag":{"nodes":[{"id":"(final select)","role":"terminal","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"(final select)":"(final select)"}}},"column_lineage":{"context":{"encounter_id":{"description":"Natural key for the encounter (from source system)","tests":[{"kind":"not_null","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_encounter_id.b3a18316f7"},{"kind":"unique","kwargs":{"column_name":"encounter_id","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.unique_int_dq_quarantine__encounters_encounter_id.31321ef55a"}],"documented":true},"end_after_1900":{"description":"Flag: encounter_end_at >= timestamp '1900-01-01'","documented":true},"failed_dq_tests":{"description":"Array of failed validation test names","tests":[{"kind":"not_null","kwargs":{"column_name":"failed_dq_tests","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_failed_dq_tests.4bad065925"}],"documented":true},"is_dq_valid":{"description":"Data quality validity flag - should always be false in quarantine","tests":[{"kind":"accepted_values","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}","values":[false]},"node_id":"test.healthcare_analytics.accepted_values_int_dq_quarantine__encounters_is_dq_valid__False.be192883a9"},{"kind":"not_null","kwargs":{"column_name":"is_dq_valid","model":"{{ get_where_subquery(ref('int_dq_quarantine__encounters')) }}"},"node_id":"test.healthcare_analytics.not_null_int_dq_quarantine__encounters_is_dq_valid.2c340f0519"}],"documented":true},"no_future_encounter_dates":{"description":"Flag: encounter_start_at \u003c= current_timestamp","documented":true},"start_after_1900":{"description":"Flag: encounter_start_at >= timestamp '1900-01-01'","documented":true},"valid_encounter_timestamps":{"description":"Flag: encounter_end_at >= encounter_start_at","documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__encounters"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"*"},"kind":"source","confidence":"opaque"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'quarantine', 'data_quality']\n    )\n}}\n\n{{ generate_quarantine_model(\n    source_model='stg_synthea__encounters',\n    description='Encounters quarantined due to data quality violations'\n) }}\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/intermediate/quarantine/_quarantine__models.yml","missing":"Schema file models/intermediate/quarantine/_quarantine__models.yml was not found under the project root."},"tests":[],"is_recursive":false},{"name":"int_patients__never_admitted","path":"models/intermediate/int_patients__never_admitted.sql","description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","dag":{"nodes":[],"edges":[]},"compiled_sql":{"(final select)":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n"},"code_map":{"compiled":"\n\n\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom \"memory\".\"main_staging\".\"stg_synthea__patients\" p\nwhere not exists (\n    select 1\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\" e\n    where e.patient_id = p.patient_id\n)\n","node_spans":{"(final select)":{"start":{"line":1,"col":1,"byte":0},"end":{"line":22,"col":1,"byte":523}}},"column_spans":{"(final select)\u001fcity":{"start":{"line":14,"col":5,"byte":315},"end":{"line":14,"col":11,"byte":321}},"(final select)\u001ffirst_name":{"start":{"line":12,"col":5,"byte":280},"end":{"line":12,"col":17,"byte":292}},"(final select)\u001flast_name":{"start":{"line":13,"col":5,"byte":298},"end":{"line":13,"col":16,"byte":309}},"(final select)\u001fpatient_id":{"start":{"line":11,"col":5,"byte":262},"end":{"line":11,"col":17,"byte":274}},"(final select)\u001fstate":{"start":{"line":15,"col":5,"byte":327},"end":{"line":15,"col":12,"byte":334}}},"raw_column_spans":{"(final select)\u001fcity":{"start":{"line":12,"col":7,"byte":411},"end":{"line":12,"col":11,"byte":415}},"(final select)\u001ffirst_name":{"start":{"line":10,"col":7,"byte":376},"end":{"line":10,"col":17,"byte":386}},"(final select)\u001flast_name":{"start":{"line":11,"col":7,"byte":394},"end":{"line":11,"col":16,"byte":403}},"(final select)\u001fstate":{"start":{"line":13,"col":7,"byte":423},"end":{"line":13,"col":12,"byte":428}}},"raw_dag":{"nodes":[{"id":"(final select)","role":"terminal","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"(final select)":"(final select)"}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"patient_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"patient_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"city"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"city"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['intermediate', 'anti_join']\n    )\n}}\n\n-- Patients with no encounter on record — the correlated NOT EXISTS\n-- anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no\n-- unit test: the report surfaces join.anti-join UNCOVERED with the\n-- inverted (matching-row) given sketch.\n\nselect\n    p.patient_id,\n    p.first_name,\n    p.last_name,\n    p.city,\n    p.state\nfrom {{ ref('stg_synthea__patients') }} p\nwhere not exists (\n    select 1\n    from {{ ref('stg_synthea__encounters') }} e\n    where e.patient_id = p.patient_id\n)\n","model_yaml":{"missing":"No schema file declares this model in the manifest — there is no authored models: entry to show."},"tests":[],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"config.unique_key","verdict":{"status":"unknown"},"evidence":[{"label":"unique_key","value":"patient_id"},{"label":"generic backing","value":"no enabled generic uniqueness data test (unique / unique_combination_of_columns) covers a column subset of the declared key"},{"label":"singular test","value":"test.healthcare_analytics.assert_never_admitted_patients_distinct — an enabled singular (SQL-file) test references this model via depends_on; whether its SQL asserts the declared grain is not statically decidable"}]},{"check":"join.anti-join","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.int_patients__never_admitted","construct":"not_exists[(final select):stg_synthea__encounters]","verdict":{"status":"uncovered"},"evidence":[{"label":"anti-join (NOT EXISTS)","value":"(final select) — WHERE NOT EXISTS (SELECT … FROM stg_synthea__encounters WHERE stg_synthea__encounters.patient_id = stg_synthea__patients.patient_id)"}],"recommendation":"Add a matching given pair: one left row whose join key IS present in the right-side given rows, then assert in `expect` that the matched row is excluded from the output. This finding's evidence carries a copy-pasteable given sketch.","sketches":["- input: ref('stg_synthea__patients')\n  rows:\n    - {patient_id: 1}   # matches the right row below\n- input: ref('stg_synthea__encounters')\n  rows:\n    - {patient_id: 1}\n# expect: rows WITHOUT the matched left row — the anti-join must exclude it"]}]},{"name":"mart_date_state_grid","path":"models/marts/analytics/mart_date_state_grid.sql","description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","dag":{"nodes":[{"id":"months","role":"import"},{"id":"states","role":"import"},{"id":"grid","role":"transform"},{"id":"(final select)","label":"mart_date_state_grid.sql","role":"final"}],"edges":[{"from":"months","to":"grid","edge_type":"from"},{"from":"states","to":"grid","edge_type":"cross"},{"from":"grid","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from grid\n\n-- cute-dbt fixture: synthetic body mod","grid":"grid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)","months":"months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n)","states":"states as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n)"},"code_map":{"compiled":"\n\n\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from \"memory\".\"main_marts\".\"dim_date\"\n),\n\nstates as (\n    select distinct state\n    from \"memory\".\"main_staging\".\"stg_synthea__patients\"\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":30,"col":1,"byte":535},"end":{"line":32,"col":40,"byte":594}},"grid":{"start":{"line":19,"col":1,"byte":278},"end":{"line":28,"col":2,"byte":533}},"months":{"start":{"line":5,"col":6,"byte":9},"end":{"line":11,"col":2,"byte":148}},"states":{"start":{"line":13,"col":1,"byte":151},"end":{"line":17,"col":2,"byte":275}}},"column_spans":{"(final select)\u001f*":{"start":{"line":30,"col":8,"byte":542},"end":{"line":30,"col":9,"byte":543}},"grid\u001fmonth_actual":{"start":{"line":23,"col":11,"byte":423},"end":{"line":23,"col":30,"byte":442}},"grid\u001fmonth_key":{"start":{"line":21,"col":14,"byte":312},"end":{"line":21,"col":85,"byte":383}},"grid\u001fmonth_name":{"start":{"line":24,"col":11,"byte":453},"end":{"line":24,"col":28,"byte":470}},"grid\u001fstate":{"start":{"line":25,"col":11,"byte":481},"end":{"line":25,"col":23,"byte":493}},"grid\u001fyear_actual":{"start":{"line":22,"col":11,"byte":394},"end":{"line":22,"col":29,"byte":412}},"months\u001fmonth_actual":{"start":{"line":8,"col":11,"byte":71},"end":{"line":8,"col":23,"byte":83}},"months\u001fmonth_name":{"start":{"line":9,"col":11,"byte":94},"end":{"line":9,"col":21,"byte":104}},"months\u001fyear_actual":{"start":{"line":7,"col":9,"byte":49},"end":{"line":7,"col":20,"byte":60}},"states\u001fstate":{"start":{"line":14,"col":21,"byte":183},"end":{"line":14,"col":26,"byte":188}}},"raw_node_spans":{"grid":{"start":{"line":19,"col":1,"byte":781},"end":{"line":28,"col":2,"byte":1036}},"months":{"start":{"line":5,"col":6,"byte":536},"end":{"line":11,"col":2,"byte":664}},"states":{"start":{"line":13,"col":1,"byte":667},"end":{"line":17,"col":2,"byte":778}}},"raw_column_spans":{"grid\u001fmonth_key":{"start":{"line":21,"col":76,"byte":877},"end":{"line":21,"col":85,"byte":886}}},"raw_dag":{"nodes":[{"id":"grid","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"months","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"states","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"grid":"grid","months":"months","states":"states"}}},"column_lineage":{"context":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"month_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_actual.c72a0ec470"}],"documented":true},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"kind":"not_null","kwargs":{"column_name":"month_key","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_month_key.46c6679e7f"}],"documented":true},"month_name":{"description":"Full month name (e.g., January)","documented":true},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"kind":"not_null","kwargs":{"column_name":"state","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_state.05af170bdb"}],"documented":true},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"kind":"not_null","kwargs":{"column_name":"year_actual","model":"{{ get_where_subquery(ref('mart_date_state_grid')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_date_state_grid_year_actual.3b341a64b2"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"dim_date"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"stg_synthea__patients"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"months"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"states"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_key"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_key"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"year_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"year_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_actual"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_actual"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"month_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"month_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"grid"}},"column":"state"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"state"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'reporting']\n    )\n}}\n\n{#\n    Dense month x state reporting grid.\n\n    Cross joins every calendar month from dim_date with every non-null\n    patient state observed in staging. Left-join sparse activity facts\n    against this grid so zero-activity month/state cells render as\n    explicit zeros instead of silently disappearing from dashboards.\n\n    The explicit CROSS JOIN inside a CTE doubles as the cute-dbt\n    cross-join CTE-edge demo (cute-dbt#64).\n#}\n\nwith months as (\n    select distinct\n        year_actual\n        , month_actual\n        , month_name\n    from {{ ref('dim_date') }}\n),\n\nstates as (\n    select distinct state\n    from {{ ref('stg_synthea__patients') }}\n    where state is not null\n),\n\ngrid as (\n    select\n        cast(months.year_actual * 100 + months.month_actual as integer) as month_key\n        , months.year_actual\n        , months.month_actual\n        , months.month_name\n        , states.state\n    from months\n    cross join states\n)\n\nselect * from grid\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_date_state_grid\n    description: |\n      Dense month x state reporting scaffold.\n      One row per (calendar month x patient state) combination — the full\n      cartesian product of dim_date months and the non-null states observed\n      in patient staging. Left-join sparse activity facts against this grid\n      so zero-activity cells render as explicit zeros.\n      Grain: month_key x state\n\n      Use cases:\n      - Dense time-series dashboards (no missing month/state cells)\n      - Zero-activity gap detection\n      - cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n\n    data_tests:\n      # New-style `arguments:` nesting (fusion-clean; dbt-core 1.10+).\n      # The legacy top-level-args form elsewhere in this file predates\n      # the planned dbt-autofix sweep.\n      - dbt_utils.unique_combination_of_columns:\n          arguments:\n            combination_of_columns:\n              - month_key\n              - state\n\n    config:\n      tags: ['analytics', 'reporting', 'marts']\n      meta:\n        grain: month_key x state\n        owner: healthcare_analytics\n        uses:\n          - \"Dense reporting grids\"\n          - \"Zero-activity gap detection\"\n\n    columns:\n      - name: month_key\n        description: \"Month surrogate key in YYYYMM integer form (e.g., 202401)\"\n        data_tests:\n          - not_null\n\n      - name: year_actual\n        description: \"Calendar year of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_actual\n        description: \"Calendar month number (1-12) of the grid cell\"\n        data_tests:\n          - not_null\n\n      - name: month_name\n        description: \"Full month name (e.g., January)\"\n\n      - name: state\n        description: \"Patient state, distinct and non-null from staging\"\n        data_tests:\n          - not_null"},"tests":[{"id":"unit_test.healthcare_analytics.mart_date_state_grid.test_mart_date_state_grid_cross_joins_months_and_states","name":"test_mart_date_state_grid_cross_joins_months_and_states","target_model":"mart_date_state_grid","changed":false,"description":"mart_date_state_grid CROSS JOINs the distinct calendar months from\ndim_date with the distinct non-null patient states from staging.\nThree daily dim_date rows collapse to two months; four patient\nrows collapse to two states (one NULL filtered out, one duplicate\ndeduplicated). The result is the full 2 x 2 cartesian product —\nfour grid rows. Mixes a dict given (dim_date) with a csv given\n(patients) in one test.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # Cross-join demo (cute-dbt#64): mart_date_state_grid's CROSS JOIN\n  # CTE gives cute-dbt's renderer a real cross-join edge to draw. This\n  # test also mixes given formats (dict + csv) in a single unit_test.\n  - name: test_mart_date_state_grid_cross_joins_months_and_states\n    description: |\n      mart_date_state_grid CROSS JOINs the distinct calendar months from\n      dim_date with the distinct non-null patient states from staging.\n      Three daily dim_date rows collapse to two months; four patient\n      rows collapse to two states (one NULL filtered out, one duplicate\n      deduplicated). The result is the full 2 x 2 cartesian product —\n      four grid rows. Mixes a dict given (dim_date) with a csv given\n      (patients) in one test.\n    model: mart_date_state_grid\n    given:\n      - input: ref('dim_date')\n        format: dict\n        rows:\n          - date_actual: 2024-01-15\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-01-16\n            year_actual: 2024\n            month_actual: 1\n            month_name: 'January'\n          - date_actual: 2024-02-01\n            year_actual: 2024\n            month_actual: 2\n            month_name: 'February'\n      - input: ref('stg_synthea__patients')\n        format: csv\n        fixture: mart_date_state_grid_patients\n    expect:\n      format: csv\n      rows: |\n        month_key,year_actual,month_actual,month_name,state\n        202401,2024,1,January,Massachusetts\n        202401,2024,1,January,Connecticut\n        202402,2024,2,February,Massachusetts\n        202402,2024,2,February,Connecticut","given":[{"input":"ref('dim_date')","bound_to_node":"months","table":{"columns":["date_actual","month_actual","month_name","year_actual"],"rows":[{"cells":[{"display":"2024-01-15","key":{"t":"str","v":"2024-01-15"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-01-16","key":{"t":"str","v":"2024-01-16"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2024-02-01","key":{"t":"str","v":"2024-02-01"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"date_actual":"2024-01-15","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-01-16","month_actual":1,"month_name":"January","year_actual":2024},{"date_actual":"2024-02-01","month_actual":2,"month_name":"February","year_actual":2024}],"format":"dict","column_meta":{"date_actual":{"description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},"month_actual":{"description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},"year_actual":{"description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}}},{"input":"ref('stg_synthea__patients')","bound_to_node":"states","table":{"columns":["patient_id","state"],"rows":[{"cells":[{"display":"a00000aa-0000-0000-0000-000000000001","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000001"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000002","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000002"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000003","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000003"}},{"display":"","key":{"t":"null"}}]},{"cells":[{"display":"a00000aa-0000-0000-0000-000000000004","key":{"t":"str","v":"a00000aa-0000-0000-0000-000000000004"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":"patient_id,state\na00000aa-0000-0000-0000-000000000001,Massachusetts\na00000aa-0000-0000-0000-000000000002,Connecticut\na00000aa-0000-0000-0000-000000000003,\na00000aa-0000-0000-0000-000000000004,Massachusetts\n","format":"csv","fixture":"tests/fixtures/mart_date_state_grid_patients.csv","column_meta":{"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}],"expected":{"table":{"columns":["month_actual","month_key","month_name","state","year_actual"],"rows":[{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"1","key":{"t":"number","v":"1"}},{"display":"202401","key":{"t":"number","v":"202401"}},{"display":"January","key":{"t":"str","v":"January"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}},{"display":"2024","key":{"t":"number","v":"2024"}}]},{"cells":[{"display":"2","key":{"t":"number","v":"2"}},{"display":"202402","key":{"t":"number","v":"202402"}},{"display":"February","key":{"t":"str","v":"February"}},{"display":"Connecticut","key":{"t":"str","v":"Connecticut"}},{"display":"2024","key":{"t":"number","v":"2024"}}]}]},"rows":[{"month_actual":"1","month_key":"202401","month_name":"January","state":"Massachusetts","year_actual":"2024"},{"month_actual":"1","month_key":"202401","month_name":"January","state":"Connecticut","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Massachusetts","year_actual":"2024"},{"month_actual":"2","month_key":"202402","month_name":"February","state":"Connecticut","year_actual":"2024"}],"format":"csv","column_meta":{"month_actual":{"description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},"month_key":{"description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},"month_name":{"description":"Full month name (e.g., January)"},"state":{"description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},"year_actual":{"description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}}}}],"is_recursive":false},{"name":"mart_dq_summary","path":"models/marts/analytics/mart_dq_summary.sql","description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","dag":{"nodes":[{"id":"encounter_metrics","role":"import"},{"id":"medication_metrics","role":"import"},{"id":"combined_metrics","role":"transform"},{"id":"(final select)","label":"mart_dq_summary.sql","role":"final"}],"edges":[{"from":"encounter_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"medication_metrics","to":"combined_metrics","edge_type":"union_all"},{"from":"combined_metrics","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","combined_metrics":"combined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)","encounter_metrics":"encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n)","medication_metrics":"medication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n)"},"code_map":{"compiled":"\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from \"memory\".\"main_staging\".\"stg_synthea__encounters\"\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from \"memory\".\"main_staging\".\"stg_synthea__medications\"\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > 5 as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":55,"col":1,"byte":2362},"end":{"line":69,"col":40,"byte":2794}},"combined_metrics":{"start":{"line":29,"col":1,"byte":1446},"end":{"line":53,"col":2,"byte":2360}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":7},"end":{"line":14,"col":2,"byte":714}},"medication_metrics":{"start":{"line":16,"col":1,"byte":717},"end":{"line":27,"col":2,"byte":1443}}},"column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":7,"byte":2676},"end":{"line":65,"col":41,"byte":2710}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":7,"byte":2459},"end":{"line":60,"col":63,"byte":2515}},"(final select)\u001fentity_type":{"start":{"line":56,"col":5,"byte":2373},"end":{"line":56,"col":16,"byte":2384}},"(final select)\u001ffailed_future_date_validations":{"start":{"line":62,"col":7,"byte":2557},"end":{"line":62,"col":37,"byte":2587}},"(final select)\u001ffailed_historical_end_validations":{"start":{"line":63,"col":7,"byte":2594},"end":{"line":63,"col":40,"byte":2627}},"(final select)\u001ffailed_historical_start_validations":{"start":{"line":64,"col":7,"byte":2634},"end":{"line":64,"col":42,"byte":2669}},"(final select)\u001ffailed_timestamp_validations":{"start":{"line":61,"col":7,"byte":2522},"end":{"line":61,"col":35,"byte":2550}},"(final select)\u001fquarantine_rate_pct":{"start":{"line":59,"col":7,"byte":2433},"end":{"line":59,"col":26,"byte":2452}},"(final select)\u001fquarantined_count":{"start":{"line":57,"col":7,"byte":2391},"end":{"line":57,"col":24,"byte":2408}},"(final select)\u001ftotal_count":{"start":{"line":58,"col":7,"byte":2415},"end":{"line":58,"col":18,"byte":2426}},"encounter_metrics\u001fentity_type":{"start":{"line":5,"col":9,"byte":49},"end":{"line":5,"col":36,"byte":76}},"encounter_metrics\u001ffailed_end_after_1900":{"start":{"line":11,"col":11,"byte":504},"end":{"line":11,"col":78,"byte":571}},"encounter_metrics\u001ffailed_no_future_encounter_dates":{"start":{"line":10,"col":11,"byte":404},"end":{"line":10,"col":100,"byte":493}},"encounter_metrics\u001ffailed_start_after_1900":{"start":{"line":12,"col":11,"byte":582},"end":{"line":12,"col":82,"byte":653}},"encounter_metrics\u001ffailed_valid_encounter_timestamps":{"start":{"line":9,"col":11,"byte":302},"end":{"line":9,"col":102,"byte":393}},"encounter_metrics\u001fquarantine_rate_pct":{"start":{"line":8,"col":11,"byte":196},"end":{"line":8,"col":106,"byte":291}},"encounter_metrics\u001fquarantined_count":{"start":{"line":6,"col":11,"byte":87},"end":{"line":6,"col":75,"byte":151}},"encounter_metrics\u001ftotal_count":{"start":{"line":7,"col":11,"byte":162},"end":{"line":7,"col":34,"byte":185}},"medication_metrics\u001fentity_type":{"start":{"line":18,"col":9,"byte":760},"end":{"line":18,"col":37,"byte":788}},"medication_metrics\u001ffailed_end_after_1900_if_present":{"start":{"line":25,"col":11,"byte":1292},"end":{"line":25,"col":100,"byte":1381}},"medication_metrics\u001ffailed_no_future_medication_dates":{"start":{"line":23,"col":11,"byte":1108},"end":{"line":23,"col":102,"byte":1199}},"medication_metrics\u001ffailed_start_after_1900":{"start":{"line":24,"col":11,"byte":1210},"end":{"line":24,"col":82,"byte":1281}},"medication_metrics\u001ffailed_valid_medication_dates":{"start":{"line":22,"col":11,"byte":1014},"end":{"line":22,"col":94,"byte":1097}},"medication_metrics\u001fquarantine_rate_pct":{"start":{"line":21,"col":11,"byte":908},"end":{"line":21,"col":106,"byte":1003}},"medication_metrics\u001fquarantined_count":{"start":{"line":19,"col":11,"byte":799},"end":{"line":19,"col":75,"byte":863}},"medication_metrics\u001ftotal_count":{"start":{"line":20,"col":11,"byte":874},"end":{"line":20,"col":34,"byte":897}}},"raw_node_spans":{"combined_metrics":{"start":{"line":29,"col":1,"byte":1516},"end":{"line":53,"col":2,"byte":2430}},"encounter_metrics":{"start":{"line":3,"col":6,"byte":103},"end":{"line":14,"col":2,"byte":797}},"medication_metrics":{"start":{"line":16,"col":1,"byte":800},"end":{"line":27,"col":2,"byte":1513}}},"raw_column_spans":{"(final select)\u001f_generated_at":{"start":{"line":65,"col":28,"byte":2802},"end":{"line":65,"col":41,"byte":2815}},"(final select)\u001fbreaches_quarantine_threshold":{"start":{"line":60,"col":69,"byte":2591},"end":{"line":60,"col":98,"byte":2620}}},"raw_dag":{"nodes":[{"id":"combined_metrics","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"encounter_metrics","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"medication_metrics","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"combined_metrics":"combined_metrics","encounter_metrics":"encounter_metrics","medication_metrics":"medication_metrics"}}},"column_lineage":{"context":{"_generated_at":{"description":"Timestamp when summary was generated","documented":true},"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"kind":"accepted_values","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}","values":["encounters","medications"]},"node_id":"test.healthcare_analytics.accepted_values_mart_dq_summary_entity_type__encounters__medications.7abb3b69d8"},{"kind":"not_null","kwargs":{"column_name":"entity_type","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_entity_type.9bd388f4fa"}],"documented":true},"failed_future_date_validations":{"description":"Count of records with future dates","documented":true},"failed_historical_end_validations":{"description":"Count of records with end dates before 1900","documented":true},"failed_historical_start_validations":{"description":"Count of records with start dates before 1900","documented":true},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations","documented":true},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"kind":"expect_column_values_to_be_between","kwargs":{"column_name":"quarantine_rate_pct","max_value":100,"min_value":0,"model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.dbt_expectations_expect_column_values_to_be_between_mart_dq_summary_quarantine_rate_pct__100__0.b37f8744b5"}],"documented":true},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"kind":"not_null","kwargs":{"column_name":"quarantined_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_quarantined_count.b0991e963b"}],"documented":true},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"kind":"not_null","kwargs":{"column_name":"total_count","model":"{{ get_where_subquery(ref('mart_dq_summary')) }}"},"node_id":"test.healthcare_analytics.not_null_mart_dq_summary_total_count.3eaac48d01"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"entity_type"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"entity_type"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantined_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantined_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"total_count"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"total_count"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"quarantine_rate_pct"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"quarantine_rate_pct"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_timestamp_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_timestamp_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_future_date_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_future_date_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_end_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_end_validations"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"combined_metrics"}},"column":"failed_historical_start_validations"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"failed_historical_start_validations"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"{{\n    config(\n        materialized='table',\n        tags=['analytics', 'data_quality']\n    )\n}}\n\nwith encounter_metrics as (\n    select\n        'encounters' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_encounter_timestamps) as failed_valid_encounter_timestamps\n        , count(*) filter (where not no_future_encounter_dates) as failed_no_future_encounter_dates\n        , count(*) filter (where not end_after_1900) as failed_end_after_1900\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n    from {{ ref('stg_synthea__encounters') }}\n),\n\nmedication_metrics as (\n    select\n        'medications' as entity_type\n        , count(*) filter (where is_dq_valid = false) as quarantined_count\n        , count(*) as total_count\n        , round(100.0 * count(*) filter (where is_dq_valid = false) / count(*), 2) as quarantine_rate_pct\n        , count(*) filter (where not valid_medication_dates) as failed_valid_medication_dates\n        , count(*) filter (where not no_future_medication_dates) as failed_no_future_medication_dates\n        , count(*) filter (where not start_after_1900) as failed_start_after_1900\n        , count(*) filter (where not end_after_1900_if_present) as failed_end_after_1900_if_present\n    from {{ ref('stg_synthea__medications') }}\n),\n\ncombined_metrics as (\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_encounter_timestamps as failed_timestamp_validations\n        , failed_no_future_encounter_dates as failed_future_date_validations\n        , failed_end_after_1900 as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from encounter_metrics\n\n    union all\n\n    select\n        entity_type\n        , quarantined_count\n        , total_count\n        , quarantine_rate_pct\n        , failed_valid_medication_dates as failed_timestamp_validations\n        , failed_no_future_medication_dates as failed_future_date_validations\n        , failed_end_after_1900_if_present as failed_historical_end_validations\n        , failed_start_after_1900 as failed_historical_start_validations\n    from medication_metrics\n)\n\nselect\n    entity_type\n    , quarantined_count\n    , total_count\n    , quarantine_rate_pct\n    , quarantine_rate_pct > {{ var('dq_quarantine_threshold') }} as breaches_quarantine_threshold\n    , failed_timestamp_validations\n    , failed_future_date_validations\n    , failed_historical_end_validations\n    , failed_historical_start_validations\n    , current_timestamp as _generated_at\nfrom combined_metrics\norder by entity_type\n\n-- cute-dbt fixture: synthetic body mod","model_yaml":{"path":"models/marts/analytics/_analytics__models.yml","raw":"  - name: mart_dq_summary\n    description: |\n      Data quality summary metrics for monitoring quarantine rates and validation failures.\n      One row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\n      Grain: entity_type\n\n      Use cases:\n      - Data quality monitoring dashboards\n      - Quarantine rate trending\n      - Validation failure root cause analysis\n      - Data quality SLA tracking\n\n    config:\n      tags: ['analytics', 'data_quality', 'marts']\n    meta:\n      grain: entity_type\n      owner: healthcare_analytics\n      uses:\n        - \"Data quality dashboards\"\n        - \"Quarantine monitoring\"\n        - \"Data quality alerting\"\n\n    columns:\n      - name: entity_type\n        description: \"Type of entity being measured (encounters, medications)\"\n        tests:\n          - not_null\n          - accepted_values:\n              values: ['encounters', 'medications']\n\n      - name: quarantined_count\n        description: \"Number of records failing data quality validations\"\n        tests:\n          - not_null\n\n      - name: total_count\n        description: \"Total number of records (valid + quarantined)\"\n        tests:\n          - not_null\n\n      - name: quarantine_rate_pct\n        description: \"Percentage of records quarantined (quarantined_count / total_count * 100)\"\n        tests:\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              max_value: 100\n\n      - name: failed_timestamp_validations\n        description: \"Count of records failing timestamp/date sequence validations\"\n\n      - name: failed_future_date_validations\n        description: \"Count of records with future dates\"\n\n      - name: failed_historical_end_validations\n        description: \"Count of records with end dates before 1900\"\n\n      - name: failed_historical_start_validations\n        description: \"Count of records with start dates before 1900\"\n\n      - name: _generated_at\n        description: \"Timestamp when summary was generated\""},"tests":[{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","name":"test_mart_dq_summary_combines_encounter_and_medication_metrics","target_model":"mart_dq_summary","changed":false,"description":"mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\nand stg_synthea__medications. This test mocks two encounter rows\n(one invalid) and two medication rows (both invalid), then asserts\nthe per-entity counts + quarantine rate. Uses csv format for both\nGIVEN and EXPECT — compact tabular form across many boolean\ndq-flag columns.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 1 — the slicer attaches contiguous\n  # `#`-comment lines at the SAME indent as `- name:` immediately above\n  # the test (no blank-line gap below) to this test's authored slice\n  # (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_combines_encounter_and_medication_metrics\n    description: |\n      mart_dq_summary UNION-ALLs metrics from stg_synthea__encounters\n      and stg_synthea__medications. This test mocks two encounter rows\n      (one invalid) and two medication rows (both invalid), then asserts\n      the per-entity counts + quarantine rate. Uses csv format for both\n      GIVEN and EXPECT — compact tabular form across many boolean\n      dq-flag columns.\n    model: mart_dq_summary\n    # INSIDE bracket for test 1 — comments at deeper indent than\n    # `- name:` ride along with the body slice as raw text (cute-dbt#69\n    # / #73).\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: csv\n        rows: |\n          is_dq_valid,valid_encounter_timestamps,no_future_encounter_dates,end_after_1900,start_after_1900\n          false,false,true,true,true\n          true,true,true,true,true\n      - input: ref(\"stg_synthea__medications\")\n        format: csv\n        rows: |\n          is_dq_valid,valid_medication_dates,no_future_medication_dates,start_after_1900,end_after_1900_if_present\n          false,false,true,true,true\n          false,true,false,true,true\n    expect:\n      format: csv\n      rows: |\n        entity_type,quarantined_count,total_count,quarantine_rate_pct,failed_timestamp_validations\n        encounters,1,2,50.00,1\n        medications,2,2,100.00,1\n  # TRAILING bracket for test 1 — the comment block butts against the\n  # body above (no blank-line gap) and is terminated below by the blank\n  # line separating the two tests (cute-dbt#69 / #73).","given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["end_after_1900","is_dq_valid","no_future_encounter_dates","start_after_1900","valid_encounter_timestamps"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900":"true","is_dq_valid":"false","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"false"},{"end_after_1900":"true","is_dq_valid":"true","no_future_encounter_dates":"true","start_after_1900":"true","valid_encounter_timestamps":"true"}],"format":"csv"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["end_after_1900_if_present","is_dq_valid","no_future_medication_dates","start_after_1900","valid_medication_dates"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}}]},{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"false","key":{"t":"bool","v":false}},{"display":"false","key":{"t":"bool","v":false}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":[{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"true","start_after_1900":"true","valid_medication_dates":"false"},{"end_after_1900_if_present":"true","is_dq_valid":"false","no_future_medication_dates":"false","start_after_1900":"true","valid_medication_dates":"true"}],"format":"csv"}],"expected":{"table":{"columns":["entity_type","failed_timestamp_validations","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"50.00","key":{"t":"number","v":"50"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"2","key":{"t":"number","v":"2"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"1","key":{"t":"number","v":"1"}},{"display":"100.00","key":{"t":"number","v":"100"}},{"display":"2","key":{"t":"number","v":"2"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"entity_type":"encounters","failed_timestamp_validations":"1","quarantine_rate_pct":"50.00","quarantined_count":"1","total_count":"2"},{"entity_type":"medications","failed_timestamp_validations":"1","quarantine_rate_pct":"100.00","quarantined_count":"2","total_count":"2"}],"format":"csv","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"failed_timestamp_validations":{"description":"Count of records failing timestamp/date sequence validations"},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}},{"id":"unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid","name":"test_mart_dq_summary_zero_quarantined_when_all_valid","target_model":"mart_dq_summary","changed":true,"description":"mart_dq_summary should report zero quarantined_count and 0.00\nquarantine_rate_pct when every upstream row passes data-quality\nvalidations. Uses sql format for the GIVEN mocks and dict format\nfor the EXPECT — dict expects only compare listed columns, so\nnon-deterministic outputs (`_generated_at` from\n`current_timestamp`) are excluded from comparison.\n","tags":[],"meta":{},"defined_in":"models/marts/analytics/_analytics__models.yml","authoring_yaml":"  # LEADING bracket for test 2 — sits between the blank-line\n  # terminator above and `- name:` below with no further gap, so the\n  # leading-wins tiebreaker is moot here (cute-dbt#69 / #73).\n  - name: test_mart_dq_summary_zero_quarantined_when_all_valid\n    description: |\n      mart_dq_summary should report zero quarantined_count and 0.00\n      quarantine_rate_pct when every upstream row passes data-quality\n      validations. Uses sql format for the GIVEN mocks and dict format\n      for the EXPECT — dict expects only compare listed columns, so\n      non-deterministic outputs (`_generated_at` from\n      `current_timestamp`) are excluded from comparison.\n    model: mart_dq_summary\n    # INSIDE bracket for test 2 — same rule as test 1 (cute-dbt#69 /\n    # #73). Demonstrates that the slicer ride-along applies to every\n    # unit_test, not just the first in the file.\n    given:\n      - input: ref('stg_synthea__encounters')\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_encounter_timestamps\n            , true as no_future_encounter_dates\n            , true as end_after_1900\n            , true as start_after_1900\n      - input: ref(\"stg_synthea__medications\")\n        format: sql\n        rows: |\n          select\n            true as is_dq_valid\n            , true as valid_medication_dates\n            , true as no_future_medication_dates\n            , true as start_after_1900\n            , true as end_after_1900_if_present\n    expect:\n      format: dict\n      rows:\n        - entity_type: 'encounters'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n        - entity_type: 'medications'\n          quarantined_count: 0\n          total_count: 1\n          quarantine_rate_pct: 0.00\n    overrides:\n      macros:\n        dbt.current_timestamp: \"'2024-01-15 00:00:00'\"\n      vars:\n        dq_quarantine_threshold: 5\n      env_vars:\n        DBT_DQ_REGION: 'us-east-1'\n  # TRAILING bracket for test 2 — terminated by the blank line\n  # separating it from test 3 (cute-dbt#69 / #73).","overrides":{"env_vars":{"DBT_DQ_REGION":"us-east-1"},"macros":{"dbt.current_timestamp":"'2024-01-15 00:00:00'"},"vars":{"dq_quarantine_threshold":5}},"given":[{"input":"ref('stg_synthea__encounters')","bound_to_node":"encounter_metrics","table":{"columns":["is_dq_valid","valid_encounter_timestamps","no_future_encounter_dates","end_after_1900","start_after_1900"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_encounter_timestamps\n  , true as no_future_encounter_dates\n  , true as end_after_1900\n  , true as start_after_1900","format":"sql"},{"input":"ref(\"stg_synthea__medications\")","bound_to_node":"medication_metrics","table":{"columns":["is_dq_valid","valid_medication_dates","no_future_medication_dates","start_after_1900","end_after_1900_if_present"],"rows":[{"cells":[{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}},{"display":"true","key":{"t":"bool","v":true}}]}]},"rows":"select\n  true as is_dq_valid\n  , true as valid_medication_dates\n  , true as no_future_medication_dates\n  , true as start_after_1900\n  , true as end_after_1900_if_present","format":"sql"}],"expected":{"table":{"columns":["entity_type","quarantine_rate_pct","quarantined_count","total_count"],"rows":[{"cells":[{"display":"encounters","key":{"t":"str","v":"encounters"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"medications","key":{"t":"str","v":"medications"}},{"display":"0.0","key":{"t":"number","v":"0"}},{"display":"0","key":{"t":"number","v":"0"}},{"display":"1","key":{"t":"number","v":"1"}}]}]},"rows":[{"entity_type":"encounters","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1},{"entity_type":"medications","quarantine_rate_pct":0.0,"quarantined_count":0,"total_count":1}],"format":"dict","column_meta":{"entity_type":{"description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},"quarantine_rate_pct":{"description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},"quarantined_count":{"description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},"total_count":{"description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}}}}],"is_recursive":false,"findings":[{"check":"grain.unique-key-unbacked","tier":"total","instrument":"data-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"config.unique_key","verdict":{"status":"uncovered"},"evidence":[{"label":"unique_key","value":"entity_type"},{"label":"exists but disabled","value":"test.healthcare_analytics.unique_mart_dq_summary_entity_type.4f2a9c7d10 — a uniqueness test on (entity_type) is declared but disabled (config.enabled: false); it asserts nothing and never counts as coverage"}],"recommendation":"Add a uniqueness data test at the declared grain: `unique` on a single-column key, or `dbt_utils.unique_combination_of_columns` over the composite key columns.","pin_node":"(final select)"},{"check":"union.arm-coverage","tier":"high","instrument":"unit-test","model_id":"model.healthcare_analytics.mart_dq_summary","construct":"union[combined_metrics]","verdict":{"status":"covered","by":["unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_combines_encounter_and_medication_metrics","unit_test.healthcare_analytics.mart_dq_summary.test_mart_dq_summary_zero_quarantined_when_all_valid"]},"evidence":[{"label":"union","value":"combined_metrics (2 arms: encounter_metrics, medication_metrics)"}],"pin_node":"combined_metrics"}]},{"name":"stg_synthea__patients","path":"models/staging/synthea/stg_synthea__patients.sql","description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"final","role":"transform"},{"id":"(final select)","label":"stg_synthea__patients.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"final","edge_type":"from"},{"from":"final","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from final\n\n-- cute-dbt fixture: synthetic body mod","final":"final as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)","renamed":"renamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n)","source":"source as (\n    select * from \"memory\".\"main\".\"patients\"\n)"},"code_map":{"compiled":"with source as (\n    select * from \"memory\".\"main\".\"patients\"\n),\n\nrenamed as (\n    select\n        -- primary key\n        Id as patient_id\n\n        -- demographics\n        , FIRST as first_name\n        , LAST as last_name\n        , PREFIX as name_prefix\n        , SUFFIX as name_suffix\n        , MAIDEN as maiden_name\n        , GENDER as gender\n        , RACE as race\n        , ETHNICITY as ethnicity\n        , MARITAL as marital_status\n\n        -- dates\n        , BIRTHDATE as birth_date\n        , DEATHDATE as death_date\n\n        -- identifiers (hashed for privacy)\n        , md5(SSN) as ssn_hash\n        , DRIVERS as drivers_license\n        , PASSPORT as passport_number\n\n        -- location\n        , BIRTHPLACE as birth_place\n        , ADDRESS as address\n        , CITY as city\n        , STATE as state\n        , COUNTY as county\n        , ZIP as zip_code\n        , LAT as latitude\n        , LON as longitude\n\n        -- financial\n        , HEALTHCARE_EXPENSES as healthcare_expenses\n        , HEALTHCARE_COVERAGE as healthcare_coverage\n    from source\n),\n\nfinal as (\n    select\n        *\n        , current_timestamp as _loaded_at\n    from renamed\n)\n\nselect * from final\n\n-- cute-dbt fixture: synthetic body mod","node_spans":{"(final select)":{"start":{"line":53,"col":1,"byte":1155},"end":{"line":55,"col":40,"byte":1215}},"final":{"start":{"line":46,"col":1,"byte":1061},"end":{"line":51,"col":2,"byte":1153}},"renamed":{"start":{"line":5,"col":1,"byte":66},"end":{"line":44,"col":2,"byte":1058}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":63}}},"column_spans":{"(final select)\u001f*":{"start":{"line":53,"col":8,"byte":1162},"end":{"line":53,"col":9,"byte":1163}},"final\u001f*":{"start":{"line":48,"col":9,"byte":1091},"end":{"line":48,"col":10,"byte":1092}},"final\u001f_loaded_at":{"start":{"line":49,"col":11,"byte":1103},"end":{"line":49,"col":42,"byte":1134}},"renamed\u001faddress":{"start":{"line":32,"col":11,"byte":740},"end":{"line":32,"col":29,"byte":758}},"renamed\u001fbirth_date":{"start":{"line":22,"col":11,"byte":464},"end":{"line":22,"col":34,"byte":487}},"renamed\u001fbirth_place":{"start":{"line":31,"col":11,"byte":704},"end":{"line":31,"col":36,"byte":729}},"renamed\u001fcity":{"start":{"line":33,"col":11,"byte":769},"end":{"line":33,"col":23,"byte":781}},"renamed\u001fcounty":{"start":{"line":35,"col":11,"byte":817},"end":{"line":35,"col":27,"byte":833}},"renamed\u001fdeath_date":{"start":{"line":23,"col":11,"byte":498},"end":{"line":23,"col":34,"byte":521}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":11,"byte":608},"end":{"line":27,"col":37,"byte":634}},"renamed\u001fethnicity":{"start":{"line":18,"col":11,"byte":377},"end":{"line":18,"col":33,"byte":399}},"renamed\u001ffirst_name":{"start":{"line":11,"col":11,"byte":173},"end":{"line":11,"col":30,"byte":192}},"renamed\u001fgender":{"start":{"line":16,"col":11,"byte":327},"end":{"line":16,"col":27,"byte":343}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":11,"byte":998},"end":{"line":42,"col":53,"byte":1040}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":11,"byte":945},"end":{"line":41,"col":53,"byte":987}},"renamed\u001flast_name":{"start":{"line":12,"col":11,"byte":203},"end":{"line":12,"col":28,"byte":220}},"renamed\u001flatitude":{"start":{"line":37,"col":11,"byte":870},"end":{"line":37,"col":26,"byte":885}},"renamed\u001flongitude":{"start":{"line":38,"col":11,"byte":896},"end":{"line":38,"col":27,"byte":912}},"renamed\u001fmaiden_name":{"start":{"line":15,"col":11,"byte":295},"end":{"line":15,"col":32,"byte":316}},"renamed\u001fmarital_status":{"start":{"line":19,"col":11,"byte":410},"end":{"line":19,"col":36,"byte":435}},"renamed\u001fname_prefix":{"start":{"line":13,"col":11,"byte":231},"end":{"line":13,"col":32,"byte":252}},"renamed\u001fname_suffix":{"start":{"line":14,"col":11,"byte":263},"end":{"line":14,"col":32,"byte":284}},"renamed\u001fpassport_number":{"start":{"line":28,"col":11,"byte":645},"end":{"line":28,"col":38,"byte":672}},"renamed\u001fpatient_id":{"start":{"line":8,"col":9,"byte":121},"end":{"line":8,"col":25,"byte":137}},"renamed\u001frace":{"start":{"line":17,"col":11,"byte":354},"end":{"line":17,"col":23,"byte":366}},"renamed\u001fssn_hash":{"start":{"line":26,"col":11,"byte":577},"end":{"line":26,"col":31,"byte":597}},"renamed\u001fstate":{"start":{"line":34,"col":11,"byte":792},"end":{"line":34,"col":25,"byte":806}},"renamed\u001fzip_code":{"start":{"line":36,"col":11,"byte":844},"end":{"line":36,"col":26,"byte":859}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}},"raw_node_spans":{"final":{"start":{"line":46,"col":1,"byte":1074},"end":{"line":51,"col":2,"byte":1166}},"renamed":{"start":{"line":5,"col":1,"byte":79},"end":{"line":44,"col":2,"byte":1071}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":76}}},"raw_column_spans":{"final\u001f_loaded_at":{"start":{"line":49,"col":32,"byte":1137},"end":{"line":49,"col":42,"byte":1147}},"renamed\u001faddress":{"start":{"line":32,"col":22,"byte":764},"end":{"line":32,"col":29,"byte":771}},"renamed\u001fbirth_date":{"start":{"line":22,"col":24,"byte":490},"end":{"line":22,"col":34,"byte":500}},"renamed\u001fbirth_place":{"start":{"line":31,"col":25,"byte":731},"end":{"line":31,"col":36,"byte":742}},"renamed\u001fcity":{"start":{"line":33,"col":19,"byte":790},"end":{"line":33,"col":23,"byte":794}},"renamed\u001fcounty":{"start":{"line":35,"col":21,"byte":840},"end":{"line":35,"col":27,"byte":846}},"renamed\u001fdeath_date":{"start":{"line":23,"col":24,"byte":524},"end":{"line":23,"col":34,"byte":534}},"renamed\u001fdrivers_license":{"start":{"line":27,"col":22,"byte":632},"end":{"line":27,"col":37,"byte":647}},"renamed\u001fethnicity":{"start":{"line":18,"col":24,"byte":403},"end":{"line":18,"col":33,"byte":412}},"renamed\u001ffirst_name":{"start":{"line":11,"col":20,"byte":195},"end":{"line":11,"col":30,"byte":205}},"renamed\u001fgender":{"start":{"line":16,"col":21,"byte":350},"end":{"line":16,"col":27,"byte":356}},"renamed\u001fhealthcare_coverage":{"start":{"line":42,"col":34,"byte":1034},"end":{"line":42,"col":53,"byte":1053}},"renamed\u001fhealthcare_expenses":{"start":{"line":41,"col":34,"byte":981},"end":{"line":41,"col":53,"byte":1000}},"renamed\u001flast_name":{"start":{"line":12,"col":19,"byte":224},"end":{"line":12,"col":28,"byte":233}},"renamed\u001flatitude":{"start":{"line":37,"col":18,"byte":890},"end":{"line":37,"col":26,"byte":898}},"renamed\u001flongitude":{"start":{"line":38,"col":18,"byte":916},"end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 One row per patient with personal information, location, and financial data.\n    data_tests:\n      # Row count validation (v0.8 - dbt_expectations)\n      - dbt_expectations.expect_table_row_count_to_be_between:\n          min_value: 900\n          config:\n            severity: warn\n    columns:\n      - name: patient_id\n        description: Unique patient identifier (UUID)\n        data_tests:\n          - unique\n          - not_null\n\n      - name: first_name\n        description: Patient's first name\n        data_tests:\n          - not_null\n\n      - name: last_name\n        description: Patient's last name\n        data_tests:\n          - not_null\n\n      - name: name_prefix\n        description: Name prefix (Mr., Mrs., Ms., etc.)\n\n      - name: name_suffix\n        description: Name suffix (Jr., Sr., III, etc.)\n\n      - name: maiden_name\n        description: Maiden name (if applicable)\n\n      - name: gender\n        description: Patient gender (M or F)\n        data_tests:\n          - not_null\n          - accepted_values:\n              values: ['M', 'F']\n          # String pattern validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^[MF]$\"\n              config:\n                severity: warn\n\n      - name: race\n        description: Patient race\n\n      - name: ethnicity\n        description: Patient ethnicity (hispanic or nonhispanic)\n\n      - name: marital_status\n        description: Marital status (M=Married, S=Single, or blank)\n\n      - name: birth_date\n        description: Patient date of birth\n        data_tests:\n          - not_null\n          # Date range validation (v0.8 - dbt_expectations)\n          # Note: Uses year extraction to avoid date type casting issues with DuckDB\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              config:\n                severity: warn\n\n      - name: death_date\n        description: Patient date of death (null if alive)\n        data_tests:\n          # Date range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: \"'1900-01-01'::date\"\n              max_value: \"current_date\"\n              row_condition: \"death_date is not null\"\n              config:\n                severity: warn\n\n      - name: ssn_hash\n        description: MD5 hash of Social Security Number (masked for privacy)\n\n      - name: drivers_license\n        description: Driver's license number\n\n      - name: passport_number\n        description: Passport number\n\n      - name: birth_place\n        description: Place of birth\n\n      - name: address\n        description: Street address\n\n      - name: city\n        description: City of residence\n\n      - name: state\n        description: State of residence (full state name, e.g., Massachusetts)\n\n      - name: county\n        description: County of residence\n\n      - name: zip_code\n        description: ZIP code\n        data_tests:\n          # String pattern validation (v0.8 - dbt_expectations)\n          # ZIP code format: 5 digits or 5+4 format\n          - dbt_expectations.expect_column_values_to_match_regex:\n              regex: \"^\\\\d{5}(-\\\\d{4})?$\"\n              row_condition: \"zip_code is not null\"\n              config:\n                severity: warn\n\n      - name: latitude\n        description: Latitude coordinate of residence\n\n      - name: longitude\n        description: Longitude coordinate of residence\n\n      - name: healthcare_expenses\n        description: Total healthcare expenses incurred\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_expenses is not null\"\n              config:\n                severity: warn\n\n      - name: healthcare_coverage\n        description: Total healthcare coverage received\n        data_tests:\n          # Value range validation (v0.8 - dbt_expectations)\n          - dbt_expectations.expect_column_values_to_be_between:\n              min_value: 0\n              row_condition: \"healthcare_coverage is not null\"\n              config:\n                severity: warn\n\n      - name: _loaded_at\n        description: Timestamp when record was loaded into staging"},"tests":[{"id":"unit_test.healthcare_analytics.stg_synthea__patients.test_stg_synthea__patients_renames_and_hashes_source_columns","name":"test_stg_synthea__patients_renames_and_hashes_source_columns","target_model":"stg_synthea__patients","changed":false,"description":"stg_synthea__patients renames the raw Synthea UPPERCASE columns to\nsnake_case and hashes the SSN with md5. The given mocks the\nsynthea_raw.patients SOURCE directly (inline dict rows) — the only\nunit test in this project whose input is a source() rather than a\nref() (cute-dbt#57). Dict expect compares listed columns only, so\nthe non-deterministic _loaded_at (current_timestamp) is excluded\nfrom assertion automatically.\n","tags":[],"meta":{},"defined_in":"models/staging/synthea/_synthea__models.yml","authoring_yaml":"  - name: test_stg_synthea__patients_renames_and_hashes_source_columns\n    description: |\n      stg_synthea__patients renames the raw Synthea UPPERCASE columns to\n      snake_case and hashes the SSN with md5. The given mocks the\n      synthea_raw.patients SOURCE directly (inline dict rows) — the only\n      unit test in this project whose input is a source() rather than a\n      ref() (cute-dbt#57). Dict expect compares listed columns only, so\n      the non-deterministic _loaded_at (current_timestamp) is excluded\n      from assertion automatically.\n    model: stg_synthea__patients\n    given:\n      - input: source('synthea_raw', 'patients')\n        format: dict\n        rows:\n          - Id: 'c0000000-0000-0000-0000-000000000001'\n            FIRST: 'Ada'\n            LAST: 'Lovelace'\n            GENDER: 'F'\n            CITY: 'Boston'\n            STATE: 'Massachusetts'\n            SSN: '999-99-9999'\n    expect:\n      format: dict\n      rows:\n        - patient_id: 'c0000000-0000-0000-0000-000000000001'\n          first_name: 'Ada'\n          last_name: 'Lovelace'\n          gender: 'F'\n          city: 'Boston'\n          state: 'Massachusetts'\n          ssn_hash: '257cf696294931925796925d89aa0afb'","given":[{"input":"source('synthea_raw', 'patients')","bound_to_node":"source","table":{"columns":["CITY","FIRST","GENDER","Id","LAST","SSN","STATE"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"999-99-9999","key":{"t":"str","v":"999-99-9999"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"CITY":"Boston","FIRST":"Ada","GENDER":"F","Id":"c0000000-0000-0000-0000-000000000001","LAST":"Lovelace","SSN":"999-99-9999","STATE":"Massachusetts"}],"format":"dict","column_meta":{"Id":{"description":"Unique patient identifier (UUID)"}}}],"expected":{"table":{"columns":["city","first_name","gender","last_name","patient_id","ssn_hash","state"],"rows":[{"cells":[{"display":"Boston","key":{"t":"str","v":"Boston"}},{"display":"Ada","key":{"t":"str","v":"Ada"}},{"display":"F","key":{"t":"str","v":"F"}},{"display":"Lovelace","key":{"t":"str","v":"Lovelace"}},{"display":"c0000000-0000-0000-0000-000000000001","key":{"t":"str","v":"c0000000-0000-0000-0000-000000000001"}},{"display":"257cf696294931925796925d89aa0afb","key":{"t":"str","v":"257cf696294931925796925d89aa0afb"}},{"display":"Massachusetts","key":{"t":"str","v":"Massachusetts"}}]}]},"rows":[{"city":"Boston","first_name":"Ada","gender":"F","last_name":"Lovelace","patient_id":"c0000000-0000-0000-0000-000000000001","ssn_hash":"257cf696294931925796925d89aa0afb","state":"Massachusetts"}],"format":"dict","column_meta":{"city":{"description":"City of residence"},"first_name":{"description":"Patient's first name","tests":[{"name":"not null"}]},"gender":{"description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},"last_name":{"description":"Patient's last name","tests":[{"name":"not null"}]},"patient_id":{"description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},"ssn_hash":{"description":"MD5 hash of Social Security Number (masked for privacy)"},"state":{"description":"State of residence (full state name, e.g., Massachusetts)"}}}}],"is_recursive":false}],"manifest_nodes":{"dim_date":{"description":"Calendar dimension covering 1909-01-01 to 2025-12-31. Provides date attributes, day/week/month/quarter/year hierarchies, weekend flags, and US holiday identification.\n","materialized":"table","columns":[{"name":"date_actual","description":"The actual date value","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"date_key","description":"Surrogate key in YYYYMMDD integer format (e.g., 20240115)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"},{"name":"unique"}]},{"name":"day_name","description":"Full day name (e.g., Monday, Tuesday)","tests":[{"name":"not null"}]},{"name":"day_name_short","description":"Abbreviated day name (e.g., Mon, Tue)","tests":[{"name":"not null"}]},{"name":"day_of_month","description":"Day of the month (1-31)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_week","description":"Day of week number (US convention - Sunday=1, Saturday=7)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"day_of_year","description":"Day of the year (1-366)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"holiday_name","description":"Name of the US holiday (null if not a holiday)"},{"name":"is_us_holiday","description":"Boolean flag indicating if date is a recognized US holiday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"is_weekend","description":"Boolean flag indicating if date falls on Saturday or Sunday","tests":[{"name":"accepted values","values":["true","false"]},{"name":"not null"}]},{"name":"month_actual","description":"Month number (1-12)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January, February)","tests":[{"name":"not null"}]},{"name":"month_name_short","description":"Abbreviated month name (e.g., Jan, Feb)","tests":[{"name":"not null"}]},{"name":"quarter_actual","description":"Quarter number (1-4)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"quarter_name","description":"Quarter name (Q1, Q2, Q3, Q4)","tests":[{"name":"accepted values","values":["Q1","Q2","Q3","Q4"]},{"name":"not null"}]},{"name":"week_of_year","description":"Week of the year (1-53)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"year_actual","description":"Year number","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"dim_payers":{"description":"Payer dimension containing insurance payers. Includes an unknown member row (payer_key = -1) for handling missing lookups.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"coverage_rate","description":"Derived coverage rate (amount_covered / total amount)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Natural payer identifier (UUID)","tests":[{"name":"not null"}]},{"name":"payer_key","description":"Surrogate key (-1 for unknown member)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_clinical_events":{"description":"Unified clinical events fact table combining conditions, medications, and procedures. Each row represents a single clinical event with standardized structure. Links to patient and encounter dimensions.\n","materialized":"table","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded"},{"name":"clinical_event_key","description":"Surrogate key for the clinical event","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"code","description":"Clinical code (SNOMED-CT or RxNorm)","tests":[{"name":"not null"}]},{"name":"code_system","description":"Code system (SNOMED-CT or RXNORM)","tests":[{"name":"accepted values","values":["SNOMED-CT","RXNORM"]},{"name":"not null"}]},{"name":"description","description":"Description of the clinical event","tests":[{"name":"not null"}]},{"name":"encounter_id","description":"Natural encounter identifier","tests":[{"name":"not null"}]},{"name":"encounter_key","description":"Foreign key to fct_encounters (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"fct_encounters.encounter_key"}]},{"name":"event_cost","description":"Cost of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_date_key","description":"Foreign key to dim_date for event start","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_date.date_key"}]},{"name":"event_end_date","description":"End date of the clinical event (null if ongoing or one-time)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"event_id","description":"Natural event identifier (condition_id, medication_id, or procedure_id)","tests":[{"name":"not null"}]},{"name":"event_start_date","description":"Start date of the clinical event","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"event_type","description":"Type of clinical event (CONDITION, MEDICATION, PROCEDURE)","tests":[{"name":"accepted values","values":["CONDITION","MEDICATION","PROCEDURE"]},{"name":"not null"}]},{"name":"patient_id","description":"Natural patient identifier","tests":[{"name":"not null"}]},{"name":"patient_key","description":"Foreign key to dim_patients (-1 for unknown)","tests":[{"name":"not null"},{"name":"relationships","detail":"dim_patients.patient_key"}]},{"name":"reason_code","description":"Clinical code for the reason of the event"},{"name":"reason_description","description":"Description of the reason for the event"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"fct_encounters_incremental":{"description":"Incremental fact table of encounter events. Appends encounters newer than the latest already-loaded start_time via an is_incremental() filter.","materialized":"incremental","columns":[{"name":"encounter_id","description":"Natural key of the encounter (unique per visit)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_type","description":"Visit class (wellness, emergency, ...)"},{"name":"patient_id","description":"Patient the encounter belongs to"},{"name":"start_time","description":"Encounter start timestamp - the incremental high-water mark"},{"name":"total_cost","description":"Total claim cost for the encounter"}]},"int_dq_quarantine__encounters":{"description":"Encounters that failed data quality validations and are excluded from downstream marts.\n\nRecords are quarantined when any of the following validations fail:\n- valid_encounter_timestamps: encounter_end_at >= encounter_start_at\n- no_future_encounter_dates: encounter_start_at \u003c= current_timestamp\n- end_after_1900: encounter_end_at >= timestamp '1900-01-01'\n- start_after_1900: encounter_start_at >= timestamp '1900-01-01'\n\nThis table preserves data quality violations for investigation without polluting fact tables.\n","materialized":"table","tags":["intermediate","quarantine","data_quality"],"columns":[{"name":"encounter_id","description":"Natural key for the encounter (from source system)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"end_after_1900","description":"Flag: encounter_end_at >= timestamp '1900-01-01'"},{"name":"failed_dq_tests","description":"Array of failed validation test names","tests":[{"name":"not null"}]},{"name":"is_dq_valid","description":"Data quality validity flag - should always be false in quarantine","tests":[{"name":"accepted values","values":["false"]},{"name":"not null"}]},{"name":"no_future_encounter_dates","description":"Flag: encounter_start_at \u003c= current_timestamp"},{"name":"start_after_1900","description":"Flag: encounter_start_at >= timestamp '1900-01-01'"},{"name":"valid_encounter_timestamps","description":"Flag: encounter_end_at >= encounter_start_at"}]},"int_patients__never_admitted":{"description":"Patients with no encounter on record — the correlated NOT EXISTS anti-join idiom (cute-dbt#196 dogfood). Deliberately carries no unit test so the golden report shows the join.anti-join finding UNCOVERED with its inverted (matching-row) given sketch.","materialized":"table","tags":["intermediate","anti_join"]},"mart_date_state_grid":{"description":"Dense month x state reporting scaffold.\nOne row per (calendar month x patient state) combination — the full\ncartesian product of dim_date months and the non-null states observed\nin patient staging. Left-join sparse activity facts against this grid\nso zero-activity cells render as explicit zeros.\nGrain: month_key x state\n\nUse cases:\n- Dense time-series dashboards (no missing month/state cells)\n- Zero-activity gap detection\n- cute-dbt cross-join CTE-edge demo (cute-dbt#64)\n","materialized":"table","tags":["analytics","reporting","marts"],"columns":[{"name":"month_actual","description":"Calendar month number (1-12) of the grid cell","tests":[{"name":"not null"}]},{"name":"month_key","description":"Month surrogate key in YYYYMM integer form (e.g., 202401)","tests":[{"name":"not null"}]},{"name":"month_name","description":"Full month name (e.g., January)"},{"name":"state","description":"Patient state, distinct and non-null from staging","tests":[{"name":"not null"}]},{"name":"year_actual","description":"Calendar year of the grid cell","tests":[{"name":"not null"}]}],"model_tests":[{"name":"dbt_utils.unique_combination_of_columns"}]},"mart_dq_summary":{"description":"Data quality summary metrics for monitoring quarantine rates and validation failures.\nOne row per entity type (encounters, medications) with quarantine counts and failure breakdowns.\nGrain: entity_type\n\nUse cases:\n- Data quality monitoring dashboards\n- Quarantine rate trending\n- Validation failure root cause analysis\n- Data quality SLA tracking\n","materialized":"table","tags":["analytics","data_quality","marts"],"columns":[{"name":"_generated_at","description":"Timestamp when summary was generated"},{"name":"entity_type","description":"Type of entity being measured (encounters, medications)","tests":[{"name":"accepted values","values":["encounters","medications"]},{"name":"not null"}]},{"name":"failed_future_date_validations","description":"Count of records with future dates"},{"name":"failed_historical_end_validations","description":"Count of records with end dates before 1900"},{"name":"failed_historical_start_validations","description":"Count of records with start dates before 1900"},{"name":"failed_timestamp_validations","description":"Count of records failing timestamp/date sequence validations"},{"name":"quarantine_rate_pct","description":"Percentage of records quarantined (quarantined_count / total_count * 100)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"quarantined_count","description":"Number of records failing data quality validations","tests":[{"name":"not null"}]},{"name":"total_count","description":"Total number of records (valid + quarantined)","tests":[{"name":"not null"}]}]},"stg_synthea__encounters":{"description":"Staging model for healthcare encounters/visits. One row per encounter with patient, provider, organization, and payer relationships.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_encounter_cost","description":"Base cost of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_class","description":"Type of encounter (ambulatory, wellness, emergency, etc.)","tests":[{"name":"accepted values","values":["ambulatory","wellness","emergency","urgentcare","outpatient","inpatient"]},{"name":"not null"}]},{"name":"encounter_code","description":"SNOMED CT code for the encounter type"},{"name":"encounter_description","description":"Description of the encounter type"},{"name":"encounter_end_at","description":"End timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Unique encounter identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"encounter_start_at","description":"Start timestamp of the encounter","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"organization_id","description":"Foreign key to the organization","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__organizations.organization_id"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"provider_id","description":"Foreign key to the provider","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__providers.provider_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of visit"},{"name":"reason_description","description":"Description of the reason for visit"},{"name":"total_claim_cost","description":"Total claim cost","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__medications":{"description":"Staging model for prescribed medications. One row per medication prescription with surrogate key.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"base_cost","description":"Base cost per dispense","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"dispenses","description":"Number of times medication was dispensed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"encounter_id","description":"Foreign key to the encounter","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__encounters.encounter_id"}]},{"name":"medication_code","description":"RxNorm code for the medication","tests":[{"name":"not null"}]},{"name":"medication_description","description":"Description of the medication","tests":[{"name":"not null"}]},{"name":"medication_end_at","description":"Timestamp when medication ended (null if ongoing)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"medication_id","description":"Surrogate key for the medication record","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"medication_start_at","description":"Timestamp when medication was prescribed","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"patient_id","description":"Foreign key to the patient","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__patients.patient_id"}]},{"name":"payer_coverage","description":"Amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"payer_id","description":"Foreign key to the payer","tests":[{"name":"not null"},{"name":"relationships","detail":"stg_synthea__payers.payer_id"}]},{"name":"reason_code","description":"SNOMED CT code for the reason of medication"},{"name":"reason_description","description":"Description of the reason for medication"},{"name":"total_cost","description":"Total cost of all dispenses","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__patients":{"description":"Staging model for patient demographics and identifiers. One row per patient with personal information, location, and financial data.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"birth_date","description":"Patient date of birth","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"},{"name":"not null"}]},{"name":"birth_place","description":"Place of birth"},{"name":"city","description":"City of residence"},{"name":"county","description":"County of residence"},{"name":"death_date","description":"Patient date of death (null if alive)","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"drivers_license","description":"Driver's license number"},{"name":"ethnicity","description":"Patient ethnicity (hispanic or nonhispanic)"},{"name":"first_name","description":"Patient's first name","tests":[{"name":"not null"}]},{"name":"gender","description":"Patient gender (M or F)","tests":[{"name":"accepted values","values":["M","F"]},{"name":"dbt_expectations.expect_column_values_to_match_regex"},{"name":"not null"}]},{"name":"healthcare_coverage","description":"Total healthcare coverage received","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"healthcare_expenses","description":"Total healthcare expenses incurred","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"last_name","description":"Patient's last name","tests":[{"name":"not null"}]},{"name":"latitude","description":"Latitude coordinate of residence"},{"name":"longitude","description":"Longitude coordinate of residence"},{"name":"maiden_name","description":"Maiden name (if applicable)"},{"name":"marital_status","description":"Marital status (M=Married, S=Single, or blank)"},{"name":"name_prefix","description":"Name prefix (Mr., Mrs., Ms., etc.)"},{"name":"name_suffix","description":"Name suffix (Jr., Sr., III, etc.)"},{"name":"passport_number","description":"Passport number"},{"name":"patient_id","description":"Unique patient identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"race","description":"Patient race"},{"name":"ssn_hash","description":"MD5 hash of Social Security Number (masked for privacy)"},{"name":"state","description":"State of residence (full state name, e.g., Massachusetts)"},{"name":"zip_code","description":"ZIP code","tests":[{"name":"dbt_expectations.expect_column_values_to_match_regex"}]}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]},"stg_synthea__payers":{"description":"Staging model for insurance payers. One row per payer with contact information and aggregate metrics.\n","materialized":"view","columns":[{"name":"_loaded_at","description":"Timestamp when record was loaded into staging"},{"name":"address","description":"Street address"},{"name":"amount_covered","description":"Total amount covered by payer","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"amount_uncovered","description":"Total amount not covered by payer"},{"name":"average_quality_of_life_score","description":"Average quality of life score for members"},{"name":"city","description":"City"},{"name":"covered_encounters","description":"Number of encounters covered"},{"name":"covered_immunizations","description":"Number of immunizations covered"},{"name":"covered_medications","description":"Number of medications covered"},{"name":"covered_procedures","description":"Number of procedures covered"},{"name":"member_months","description":"Total member months"},{"name":"payer_id","description":"Unique payer identifier (UUID)","tests":[{"name":"not null"},{"name":"unique"}]},{"name":"payer_name","description":"Name of the insurance payer","tests":[{"name":"not null"}]},{"name":"phone","description":"Phone number"},{"name":"revenue","description":"Total revenue collected","tests":[{"name":"dbt_expectations.expect_column_values_to_be_between"}]},{"name":"state","description":"State where headquartered"},{"name":"uncovered_encounters","description":"Number of encounters not covered"},{"name":"uncovered_immunizations","description":"Number of immunizations not covered"},{"name":"uncovered_medications","description":"Number of medications not covered"},{"name":"uncovered_procedures","description":"Number of procedures not covered"},{"name":"unique_customers","description":"Number of unique customers"},{"name":"zip_code","description":"ZIP code (stored as numeric, 5 digits)"}],"model_tests":[{"name":"dbt_expectations.expect_table_row_count_to_be_between"}]}},"check_specs":{"grain.unique-key-unbacked":{"name":"Unique key without a uniqueness test","group":"grain","tier":"total","instrument":"data-test","conditions":["the model declares config.unique_key (a column name or a list of columns)","no enabled uniqueness data test (unique, or a composite unique_combination_of_columns) attached to the model has a column set that is a subset of the declared key","a covering test whose own config weakens the guarantee — severity: warn, a where row filter, or a limit cap — still attributes, marked as DEGRADED backing with every cause enumerated on the finding (in-row honesty: a cue beside the attribution, never a fourth verdict, never a percentage)","when no enabled generic uniqueness test covers the key but an enabled singular (SQL-file) test references the model through depends_on, the verdict degrades to UNKNOWN — a singular test may assert the declared grain, but its SQL is not statically classifiable (never a false Uncovered nag on singular-test shops)","a uniqueness test on the declared grain that exists but is disabled — config.enabled: false on a nodes-map test, or a generic-test entry in the manifest disabled map — never counts as coverage and surfaces as `exists but disabled` evidence, distinct from absent"],"exclusions":["a unique_key value that is not a literal column name / list of column names is reported UNKNOWN, never UNCOVERED (the declared grain is not statically recoverable)","a uniqueness test whose column set is WIDER than the key does not satisfy the check (uniqueness of a superset does not imply uniqueness at the declared grain)","a disabled SINGULAR test (and every non-generic-test disabled-map entry) carries no statically recoverable model linkage — both engines empty depends_on and omit attached_node on disabled nodes — so it is never attributed and never surfaced here"],"rationale":"Incremental merge / delete+insert semantics silently depend on the declared unique_key actually being unique — a duplicate key corrupts the merge with no test to catch it. Declaring a grain without a test at that grain is an unverified load-bearing assumption.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/grain.unique-key-unbacked.html"},"incremental.branch-coverage":{"name":"Unexercised is_incremental() branch","group":"incremental","tier":"high","instrument":"unit-test","conditions":["the model is materialized incremental (config.materialized = \"incremental\") — its body forks on is_incremental(), and a dbt unit test compiles exactly one side of that fork","a unit test with overrides.macros.is_incremental = true exercises the incremental branch; an explicit false override OR no override at all exercises the initial full-build branch (dbt compiles is_incremental() as false in unit tests by default)","branch coverage rolls up per model as none / false-only / true-only / both; only BOTH satisfies the construct, attributing every unit test on the model (each test compiles one side of the fork)","the HIGH-tier cue boundary (the union.arm-coverage precedent): a test on a branch proves that branch's compiled SQL runs under fixtures — whether the branch's filter/merge semantics are meaningfully asserted, or whether the body's Jinja even calls is_incremental(), is not statically decidable from the manifest, so the recommendation is a cue, never an assertion of a bug"],"exclusions":["models whose materialization is absent, non-string, or anything other than incremental (view, table, ephemeral, custom) emit no finding — when the fork provably cannot exist or cannot be statically known, the check is silent, never misclassifies","microbatch-strategy models (incremental_strategy = \"microbatch\", or any non-null event_time config) are OUT of rule #1: dbt replays them through event-time batch windows, not the is_incremental() fork, so true/false override coverage does not describe their semantics — never classified","a non-boolean is_incremental override collapses to the no-override default at ingestion (cute-dbt#145 tolerant truthiness) and counts toward the full-build branch — the conservative side of the cue"],"rationale":"An incremental model is two programs in one body: the initial full build, and the incremental run that filters on the high-water mark and merges by key. Each unit test compiles only one of them, so a suite living entirely on one branch ships the other untested — exactly where incremental models silently drop, duplicate, or re-process rows.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/incremental.branch-coverage.html"},"join.anti-join":{"name":"Anti-join exclusion untested","group":"join","tier":"high","instrument":"unit-test","conditions":["the model LEFT-JOINs a relation and filters `WHERE \u003cright>.\u003ckey> IS NULL` in a top-level AND conjunct, where `\u003ckey>` is one of the join's ON equi-key right columns — the anti-join idiom: the join deliberately keeps the UNMATCHED left rows","OR the model filters `WHERE NOT EXISTS (SELECT … FROM \u003cinner> WHERE …)` in a top-level AND conjunct, the inner being a single plain named relation whose WHERE carries a correlated reference to the outer query — the resolvable outer↔inner equi-conjuncts are the anti-join keys (cute-dbt#196)","OR the model filters `WHERE \u003ccol> NOT IN (SELECT \u003ccol> FROM \u003cinner>)` in a top-level AND conjunct, the inner being a single plain named relation projecting exactly one column — the membership pair (outer column ↔ inner projected column) is the anti-join key (cute-dbt#196). SQL honesty note: a NULL in the inner column makes NOT IN yield NO rows at all; detection still treats the construct as the anti-join idiom (that is how it is authored), and the matched-row fixture this check recommends is exactly what surfaces the NULL trap","the recommendation INVERTS join.left-null-propagation's: the anti-join's risk is the matched class leaking through, so the missing fixture is a left row that DOES match a right row, with `expect` proving it is excluded","satisfaction (all arms): some unit test's literal givens carry a left/outer row whose key matches an inner/right given row (both cells non-NULL, equal on the value-normalized key)","supersedes join.left-null-propagation on the same construct: NULL right-side columns are the anti-join's working mechanism, not an untested gap (the subquery constructs are never enumerated by left-null-propagation at all)","given binding follows the union.arm-coverage premise: only `given` entries carry statically-visible rows — an ungiven non-seed input is an empty mock; join sides bind directly by external leaf name or through a single-external simple-FROM closure"],"exclusions":["negated subqueries anywhere but a top-level AND conjunct of a SELECT's WHERE (OR branches, HAVING, JOIN ON, projections) are NOT detected — different semantics or position, silent, never misclassified","non-negated EXISTS / IN subqueries (semi-join and membership inclusion — future evidence consumers) and scalar subqueries are NOT detected","a NOT EXISTS / NOT IN whose inner is a derived table, joins or reads several relations, or carries its own WITH clause is NOT detected; an uncorrelated NOT EXISTS (zero outer references) is not a keyed anti-join — silent","expression (non-column) correlations and projections are not key material: a correlated NOT EXISTS with no resolvable equi pair, and a NOT IN whose outer column does not resolve to a single relation, degrade to UNKNOWN, never UNCOVERED","an IS NULL on a non-key right column is a data filter, not the anti-join idiom — join.left-null-propagation governs that construct","an IS NULL inside an OR branch has different semantics and is never treated as the anti-join filter","unrecoverable join keys, unresolvable side bindings, external `fixture:` files, non-literal `format: sql` givens, and ungiven seed inputs degrade to UNKNOWN, never UNCOVERED"],"rationale":"An anti-join's output is defined by what it excludes. Every existing given that only carries unmatched rows proves the keep path, never the exclusion: if the ON key drifts or the IS NULL column changes, matched rows leak into the output and no test catches it.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/join.anti-join.html"},"union.arm-coverage":{"name":"Unexercised UNION arm","group":"union","tier":"high","instrument":"unit-test","conditions":["the model's body (or a CTE within it) UNIONs arms the CTE engine resolved to union-typed edges — each checked arm is a join-free reference to an earlier CTE (`UnionAll` / `UnionDistinct`)","an arm counts as exercised when at least one unit-test given with one or more in-manifest rows binds — by `ref(...)` / `source(...)` leaf name, case-insensitive — to any external relation in the arm's upstream CTE closure","a given bound to a relation shared by several arms exercises every arm whose closure reads it: its rows provably enter each arm's scan, while per-arm filter survival is deliberately out of scope (no predicate evaluation) — the HIGH-tier cue boundary, never an assertion of output-level coverage","verdict order: any provably-unfed arm makes the construct UNCOVERED; otherwise any statically-unattributable arm makes it UNKNOWN; otherwise COVERED, attributing every test that feeds an arm"],"exclusions":["arms that are not a join-free reference to an earlier CTE (join chains, derived tables, arms reading external tables directly, EXCEPT/INTERSECT arms) emit no union edge and are invisible to this check — never counted, never reported","an arm whose upstream closure reads no resolvable external relation (constant SELECT, table functions) makes the construct UNKNOWN, never UNCOVERED","an arm fed only by external-fixture or non-literal-sql givens (row counts not statically recoverable) makes the construct UNKNOWN, never UNCOVERED","an arm whose only unbound feeding relation resolves to a seed is UNKNOWN, never UNCOVERED, when the model has unit tests (dbt lets seed inputs go ungiven and reads the real seed file)","`this` givens (incremental prior state) never feed a union arm"],"rationale":"A UNION arm with no fixture rows contributes nothing to any unit test: its projection, casts, and filters run on zero rows, so a column mix-up or a dropped row in that arm ships silently. One given row per arm makes every branch's contribution visible in the expected output.","book_href":"https://breezy-bays-labs.github.io/cute-dbt/checks/union.arm-coverage.html"}}}</script>
 
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diff --git a/examples/prdiff-minidag-report.html b/examples/prdiff-minidag-report.html
index 54ff373..88e878a 100644
--- a/examples/prdiff-minidag-report.html
+++ b/examples/prdiff-minidag-report.html
@@ -2634,7 +2634,7 @@ <h2>Expected</h2>
 <div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
 </div>
 
-<script type="application/json" id="cute-dbt-data">{"baseline":"","models":[{"name":"fct_orders","path":"models/marts/fct_orders.sql","dag":{"nodes":[{"id":"items","role":"import"},{"id":"(final select)","label":"fct_orders.sql","role":"final"}],"edges":[{"from":"items","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select customer_id, count(*) as order_count, sum(amount) as gross_revenue from items group by 1","items":"items as (\n  select customer_id, amount from int_order_items\n)"},"code_map":{"compiled":"with items as (\n  select customer_id, amount from int_order_items\n)\nselect customer_id, count(*) as order_count, sum(amount) as gross_revenue from items group by 1","node_spans":{"(final select)":{"start":{"line":4,"col":1,"byte":68},"end":{"line":4,"col":96,"byte":163}},"items":{"start":{"line":1,"col":6,"byte":5},"end":{"line":3,"col":2,"byte":67}}},"column_spans":{"(final select)\u001fcustomer_id":{"start":{"line":4,"col":8,"byte":75},"end":{"line":4,"col":19,"byte":86}},"(final select)\u001fgross_revenue":{"start":{"line":4,"col":46,"byte":113},"end":{"line":4,"col":74,"byte":141}},"(final select)\u001forder_count":{"start":{"line":4,"col":21,"byte":88},"end":{"line":4,"col":44,"byte":111}},"items\u001famount":{"start":{"line":2,"col":23,"byte":38},"end":{"line":2,"col":29,"byte":44}},"items\u001fcustomer_id":{"start":{"line":2,"col":10,"byte":25},"end":{"line":2,"col":21,"byte":36}}},"raw_column_spans":{"(final select)\u001fcustomer_id":{"start":{"line":2,"col":3,"byte":9},"end":{"line":2,"col":14,"byte":20}},"(final select)\u001fgross_revenue":{"start":{"line":4,"col":18,"byte":66},"end":{"line":4,"col":31,"byte":79}},"(final select)\u001forder_count":{"start":{"line":3,"col":15,"byte":36},"end":{"line":3,"col":26,"byte":47}},"items\u001famount":{"start":{"line":4,"col":7,"byte":55},"end":{"line":4,"col":13,"byte":61}},"items\u001fcustomer_id":{"start":{"line":2,"col":3,"byte":9},"end":{"line":2,"col":14,"byte":20}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"int_order_items"}},"column":"customer_id"},"to_col":{"scope":{"intra":{"node_id":"items"}},"column":"customer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"int_order_items"}},"column":"amount"},"to_col":{"scope":{"intra":{"node_id":"items"}},"column":"amount"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"items"}},"column":"customer_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"customer_id"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"select\n  customer_id,\n  count(*) as order_count,\n  sum(amount) as gross_revenue\nfrom {{ ref('int_order_items') }}\ngroup by 1","sql_diff":{"lines":[{"kind":"context","text":"select","emphasis":null},{"kind":"context","text":"  customer_id,","emphasis":null},{"kind":"context","text":"  count(*) as order_count,","emphasis":null},{"kind":"removed","text":"  sum(amount) as revenue","emphasis":null},{"kind":"added","text":"  sum(amount) as gross_revenue","emphasis":[17,23]},{"kind":"context","text":"from {{ ref('int_order_items') }}","emphasis":null},{"kind":"context","text":"group by 1","emphasis":null}]},"model_yaml":{"path":"models/marts/_fct_orders_tests.yml","raw":"  - name: fct_orders\n    config:\n      materialized: incremental","diff":{"lines":[{"kind":"added","text":"  - name: fct_orders","emphasis":null},{"kind":"added","text":"    config:","emphasis":null},{"kind":"added","text":"      materialized: incremental","emphasis":null}]}},"tests":[{"id":"unit_test.shop.fct_orders.test_revenue_rollup","name":"test_revenue_rollup","target_model":"fct_orders","changed":false,"defined_in":"models/marts/_fct_orders_tests.yml","authoring_yaml":"  - name: test_revenue_rollup\n    model: fct_orders\n    given:\n      - input: ref('int_order_items')\n        rows:\n          - { order_id: 1, customer_id: 7, amount: 12 }\n          - { order_id: 2, customer_id: 7, amount: 8 }\n    expect:\n      rows:\n        - { customer_id: 7, order_count: 2, gross_revenue: 20 }","given":[{"input":"ref('int_order_items')","bound_to_node":"items","table":{"columns":["amount","customer_id","order_id"],"rows":[{"cells":[{"display":"12","key":{"t":"number","v":"12"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"1","key":{"t":"number","v":"1"}}]},{"cells":[{"display":"8","key":{"t":"number","v":"8"}},{"display":"7","key":{"t":"number","v":"7"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"amount":12,"customer_id":7,"order_id":1},{"amount":8,"customer_id":7,"order_id":2}],"format":"dict"}],"expected":{"table":{"columns":["customer_id","gross_revenue","order_count"],"rows":[{"cells":[{"display":"7","key":{"t":"number","v":"7"}},{"display":"20","key":{"t":"number","v":"20"}},{"display":"2","key":{"t":"number","v":"2"}}]}]},"rows":[{"customer_id":7,"gross_revenue":20,"order_count":2}],"format":"dict"}}],"is_recursive":false,"axes":{"body":true,"config":true,"unit_test":true},"config_file":"models/marts/_fct_orders_tests.yml","state":"modified"},{"name":"stg_orders","path":"models/staging/stg_orders.sql","dag":{"nodes":[{"id":"src","role":"import"},{"id":"(final select)","label":"stg_orders.sql","role":"final"}],"edges":[{"from":"src","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from src","src":"src as (\n  select order_id, customer_id, status, amount\n  from raw_orders\n)"},"code_map":{"compiled":"with src as (\n  select order_id, customer_id, status, amount\n  from raw_orders\n)\nselect * from src","node_spans":{"(final select)":{"start":{"line":5,"col":1,"byte":81},"end":{"line":5,"col":18,"byte":98}},"src":{"start":{"line":1,"col":6,"byte":5},"end":{"line":4,"col":2,"byte":80}}},"column_spans":{"(final select)\u001f*":{"start":{"line":5,"col":8,"byte":88},"end":{"line":5,"col":9,"byte":89}},"src\u001famount":{"start":{"line":2,"col":41,"byte":54},"end":{"line":2,"col":47,"byte":60}},"src\u001fcustomer_id":{"start":{"line":2,"col":20,"byte":33},"end":{"line":2,"col":31,"byte":44}},"src\u001forder_id":{"start":{"line":2,"col":10,"byte":23},"end":{"line":2,"col":18,"byte":31}},"src\u001fstatus":{"start":{"line":2,"col":33,"byte":46},"end":{"line":2,"col":39,"byte":52}}},"raw_node_spans":{"src":{"start":{"line":1,"col":6,"byte":5},"end":{"line":4,"col":2,"byte":99}}},"raw_column_spans":{"(final select)\u001f*":{"start":{"line":5,"col":8,"byte":107},"end":{"line":5,"col":9,"byte":108}},"src\u001famount":{"start":{"line":2,"col":41,"byte":54},"end":{"line":2,"col":47,"byte":60}},"src\u001fcustomer_id":{"start":{"line":2,"col":20,"byte":33},"end":{"line":2,"col":31,"byte":44}},"src\u001forder_id":{"start":{"line":2,"col":10,"byte":23},"end":{"line":2,"col":18,"byte":31}},"src\u001fstatus":{"start":{"line":2,"col":33,"byte":46},"end":{"line":2,"col":39,"byte":52}}}},"column_lineage":{"edges":[{"from_col":{"scope":{"intra":{"node_id":"raw_orders"}},"column":"order_id"},"to_col":{"scope":{"intra":{"node_id":"src"}},"column":"order_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"raw_orders"}},"column":"customer_id"},"to_col":{"scope":{"intra":{"node_id":"src"}},"column":"customer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"raw_orders"}},"column":"status"},"to_col":{"scope":{"intra":{"node_id":"src"}},"column":"status"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"raw_orders"}},"column":"amount"},"to_col":{"scope":{"intra":{"node_id":"src"}},"column":"amount"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"src"}},"column":"order_id"},"to_col":{"scope":{"intra":{"node_id":"(final 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
 <script>
 /*! jQuery v3.7.1 | (c) OpenJS Foundation and other contributors | jquery.org/license */
diff --git a/src/adapters/render.rs b/src/adapters/render.rs
index b8d4c1e..dcb7230 100644
--- a/src/adapters/render.rs
+++ b/src/adapters/render.rs
@@ -72,7 +72,7 @@ use crate::domain::{
     EdgeType, Finding, FixtureTable, FixtureTableDiff, GovernanceFacts, HeuristicId,
     HookChangeFacts, HookManifestPresence, InScopeSet, Instrument, MacroIdentity, Manifest,
     ModelInScopeSet, ModelState, ModelYamlOutcome, Node, NodeId, NormalizedDiffIndex, PrDagGraph,
-    PrRef, ProjectChange, ProjectChangeCategory, ProjectChangePanel, ProjectDefinition,
+    PrRef, Presence, ProjectChange, ProjectChangeCategory, ProjectChangePanel, ProjectDefinition,
     ProjectFacts, ProjectFallbackReason, SeedCard, SourceMap, SourceMapEntry, SourceNode,
     SourcePos, SourceSpan, SpanRole, TestMetadata, Tier, UnitTest, UnitTestDataDiff, UnitTestGiven,
     UnitTestOverrides, UnitTestYamlBlock, VarAttribution, VarChangeFacts, VarReference,
@@ -329,6 +329,65 @@ pub struct RawZonePayload {
     pub node_id: Option<String>,
 }
 
+/// One raw structural DAG node (cute-dbt#470, S2) — a literal `raw_code` region
+/// that has STRUCTURE as written, before any Jinja expansion. SOUND by
+/// construction: every node is a real lexical region of `raw_code` (a verbatim
+/// `WITH` CTE, the WITH-less terminal select, or a `{% if %}`/`{% for %}` control
+/// zone), never a fabricated/predicted node. Serialize-only projection of the
+/// assembled [`SourceMap`] (the filled `raw` spans + the `Zone` entries).
+///
+/// `presence` is the 3-state honesty verdict (`compiled_in` / `compiled_out` /
+/// `structural`): a `{% if is_incremental() %}` zone pruned this build is
+/// `compiled_out` (NEVER `compiled`/`compiled_in`) — a first-class raw node the
+/// future Incremental⇄Full-refresh toggle shows/hides with no recompile.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize)]
+pub struct RawDagNodePayload {
+    /// Stable raw-node id. A CTE node's id is its alias (== the compiled DAG
+    /// node id, the N→1 identity origin); the WITH-less terminal is
+    /// [`TERMINAL_NODE_NAME`]; a zone is `zone:<index>` (stable appearance
+    /// order among the model's `Zone` entries).
+    pub id: String,
+    /// `"cte"` | `"terminal"` | `"zone"` — the structural role of the raw
+    /// region. Distinct from `is_zone` so a consumer can branch on either.
+    pub role: &'static str,
+    /// Whether this is a control-flow zone (`{% if %}`/`{% for %}`) — `true`
+    /// only for `role == "zone"`. Carried explicitly (not just derivable from
+    /// `role`) so the prototype can style zones without string-matching.
+    pub is_zone: bool,
+    /// The 3-state presence verdict DERIVED ONCE in Rust (`compiled_in` /
+    /// `compiled_out` / `structural`), emitted as the wire string the prototype
+    /// renders verbatim. A verbatim CTE / WITH-less terminal is always
+    /// `compiled_in` (it is, by definition, present in the compiled output it
+    /// was anchored against). A zone carries its honest [`Presence`] verdict.
+    pub presence: &'static str,
+}
+
+/// The raw structural DAG (cute-dbt#470, S2) — `nodes` only. Edges +
+/// `node_map.raw` (the 1→N observed correspondence) are S3 (cute-dbt#471) and
+/// are DELIBERATELY ABSENT here, never faked: this payload carries only the
+/// sound raw nodes + the N→1 `node_map.compiled` fold (on [`CodeMapPayload`]).
+#[derive(Debug, Clone, PartialEq, Eq, Serialize)]
+pub struct RawDagPayload {
+    /// The sound raw structural nodes (verbatim CTEs + WITH-less terminal +
+    /// control zones). Empty (the whole `raw_dag` key omitted) for a model with
+    /// no resolvable raw structure, so pre-#470 goldens stay byte-stable.
+    pub nodes: Vec<RawDagNodePayload>,
+}
+
+/// The compiled⇄raw node correspondence (cute-dbt#470, S2) — S2 fills ONLY the
+/// `compiled` half (the N→1 fold `compiledId → rawId`); the `raw` half (1→N,
+/// observed) is S3 (cute-dbt#471) and is absent here, never faked. The whole
+/// `node_map` key is omitted when `compiled` is empty (byte-stable goldens).
+#[derive(Debug, Clone, PartialEq, Eq, Serialize)]
+pub struct NodeMapPayload {
+    /// `compiledId → rawId` (N→1): each compiled DAG node folded back to its
+    /// UNIQUE raw origin. A compiled node with NO unique raw origin OMITS its
+    /// key (never a guessed `rawId`). A `{% for %}`-fanned set of compiled CTEs
+    /// all map back to the ONE template zone raw node (by structural
+    /// containment), omitting where the origin is ambiguous.
+    pub compiled: BTreeMap<String, String>,
+}
+
 /// The per-model source-map render projection (cute-dbt#445) — Serialize-only,
 /// PROJECTS the domain [`SourceMap`] spine fact. The faithful full `compiled`
 /// text plus the derived `CteBody` node-span table; `raw_zones` is the deferred
@@ -366,6 +425,18 @@ pub struct CodeMapPayload {
     /// region). Empty (omitted) until a column resolves a unique raw anchor.
     #[serde(skip_serializing_if = "BTreeMap::is_empty")]
     pub raw_column_spans: BTreeMap<String, SourceSpan>,
+    /// The raw structural DAG (cute-dbt#470, S2) — sound raw nodes (verbatim
+    /// CTEs + WITH-less terminal + control zones). `None` (key omitted) when the
+    /// model resolves no raw structure, so pre-#470 goldens stay byte-stable.
+    /// Edges are S3 (cute-dbt#471) and are absent here, never faked.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub raw_dag: Option<RawDagPayload>,
+    /// The compiled⇄raw node correspondence (cute-dbt#470, S2) — S2 fills ONLY
+    /// the N→1 `compiled` half (`compiledId → rawId`, omit-on-ambiguous); the
+    /// 1→N `raw` half is S3 (cute-dbt#471). `None` (key omitted) when no
+    /// compiled node folds to a unique raw origin, so goldens stay byte-stable.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub node_map: Option<NodeMapPayload>,
 }
 
 impl CodeMapPayload {
@@ -396,6 +467,12 @@ impl CodeMapPayload {
             .into_iter()
             .map(|((node_id, column), span)| (format!("{node_id}\u{1f}{column}"), span))
             .collect();
+        // cute-dbt#470 (S2) — the raw structural DAG nodes + the N→1
+        // `node_map.compiled` fold, built once over the assembled spine
+        // (`node_spans` are the compiled DAG nodes folded back). `None` when no
+        // raw structure / no unique origin resolves, so the keys are omitted and
+        // pre-#470 goldens stay byte-stable.
+        let (raw_dag, node_map) = build_raw_dag_and_node_map(sm, &node_spans);
         Self {
             compiled: sm.compiled.clone(),
             node_spans,
@@ -403,10 +480,141 @@ impl CodeMapPayload {
             column_spans,
             raw_node_spans,
             raw_column_spans,
+            raw_dag,
+            node_map,
         }
     }
 }
 
+/// Build the raw structural DAG nodes + the N→1 `node_map.compiled` fold
+/// (cute-dbt#470, S2) from the assembled [`SourceMap`] and the model's compiled
+/// `node_spans` (the compiled DAG node ids → spans).
+///
+/// ## `raw_dag.nodes` (every node a SOUND literal `raw_code` region)
+///
+/// - **CTE raw node** — one per `CteBody` entry whose `raw` span the `raw_scan`
+///   adapter UNIQUELY anchored (a verbatim `WITH` CTE). `presence = compiled_in`
+///   (a verbatim CTE anchored against the compiled output is, by definition, in
+///   it). Id == the CTE alias (== the compiled DAG node id — the N→1 identity).
+/// - **WITH-less terminal raw node** — synthesized as exactly ONE `terminal`
+///   node when the model is WITH-less (the compiled DAG is the lone
+///   [`TERMINAL_NODE_NAME`] node AND no `CteBody` carries a raw span — there is
+///   no `WITH`). Mirrors [`crate::domain::source_map::SourceMap::from_cte_graph`]'s
+///   terminal synthesis so `node_map.compiled` stays TOTAL for WITH-less models.
+///   `presence = compiled_in`.
+/// - **Zone raw node** — one per `Zone` entry, id `zone:<index>` (stable
+///   appearance order). `presence` is the honest 3-state [`Presence`] verdict
+///   (a pruned `is_incremental()` zone is `compiled_out`, NEVER `compiled_in`).
+///
+/// ## `node_map.compiled` (N→1, `compiledId → rawId`, omit-on-ambiguous)
+///
+/// For each compiled DAG node id:
+/// - A raw CTE node with the SAME id ⇒ map to it (the verbatim-CTE identity).
+/// - Else the WITH-less terminal ⇒ map to the synthesized terminal raw node.
+/// - Else fold by STRUCTURAL CONTAINMENT: the raw zone node whose COMPILED span
+///   uniquely contains this compiled node's span ⇒ map to that ONE zone (the
+///   `{% for %}`-fanned CTEs → the ONE template node). Zero or MULTIPLE
+///   containing zones ⇒ OMIT the key (never a guessed `rawId`).
+fn build_raw_dag_and_node_map(
+    sm: &SourceMap,
+    node_spans: &BTreeMap<String, SourceSpan>,
+) -> (Option<RawDagPayload>, Option<NodeMapPayload>) {
+    // The raw CTE nodes: every CteBody whose raw span was uniquely anchored.
+    let raw_cte_ids: std::collections::BTreeSet<String> = sm.raw_node_spans().into_keys().collect();
+    // A WITH-less model: the compiled DAG is the lone terminal node AND no CTE
+    // carries a raw span (no `WITH`). Mirror from_cte_graph's terminal synthesis
+    // so node_map.compiled stays total for WITH-less models. A WITH-bearing
+    // terminal has no verbatim raw region, so it is NOT synthesized here and its
+    // node_map key is honestly omitted.
+    let is_with_less = raw_cte_ids.is_empty()
+        && node_spans.len() == 1
+        && node_spans.contains_key(TERMINAL_NODE_NAME);
+
+    let mut nodes: Vec<RawDagNodePayload> = Vec::new();
+    // CTE raw nodes (stable BTreeSet order).
+    for id in &raw_cte_ids {
+        nodes.push(RawDagNodePayload {
+            id: id.clone(),
+            role: "cte",
+            is_zone: false,
+            presence: Presence::CompiledIn.to_wire(),
+        });
+    }
+    // The WITH-less terminal raw node (exactly one).
+    if is_with_less {
+        nodes.push(RawDagNodePayload {
+            id: TERMINAL_NODE_NAME.to_owned(),
+            role: "terminal",
+            is_zone: false,
+            presence: Presence::CompiledIn.to_wire(),
+        });
+    }
+    // Zone raw nodes — id `zone:<index>` by appearance order; carry the honest
+    // 3-state presence + (compiled span, if any) for the structural fold below.
+    let cte_spans: Vec<SourceSpan> = node_spans.values().copied().collect();
+    let mut zone_compiled: Vec<(String, Option<SourceSpan>)> = Vec::new();
+    for entry in sm
+        .entries
+        .iter()
+        .filter(|e| matches!(e.role, SpanRole::Zone { .. }))
+    {
+        let zone_id = format!("zone:{}", zone_compiled.len());
+        nodes.push(RawDagNodePayload {
+            id: zone_id.clone(),
+            role: "zone",
+            is_zone: true,
+            presence: entry.presence(&cte_spans).to_wire(),
+        });
+        zone_compiled.push((zone_id, entry.compiled));
+    }
+
+    // node_map.compiled — fold each compiled DAG node back to its raw origin.
+    let mut compiled_map: BTreeMap<String, String> = BTreeMap::new();
+    for (compiled_id, compiled_span) in node_spans {
+        if raw_cte_ids.contains(compiled_id) {
+            // Verbatim CTE identity: compiled id == raw id.
+            compiled_map.insert(compiled_id.clone(), compiled_id.clone());
+        } else if is_with_less && compiled_id == TERMINAL_NODE_NAME {
+            // WITH-less terminal ⇒ the synthesized terminal raw node.
+            compiled_map.insert(compiled_id.clone(), TERMINAL_NODE_NAME.to_owned());
+        } else if let Some(zone_id) = unique_containing_zone(&zone_compiled, compiled_span) {
+            // A fanned compiled CTE inside a {% for %} ⇒ the ONE template zone.
+            compiled_map.insert(compiled_id.clone(), zone_id);
+        }
+        // No unique raw origin ⇒ OMIT (never a guessed rawId).
+    }
+
+    let raw_dag = (!nodes.is_empty()).then_some(RawDagPayload { nodes });
+    let node_map = (!compiled_map.is_empty()).then_some(NodeMapPayload {
+        compiled: compiled_map,
+    });
+    (raw_dag, node_map)
+}
+
+/// The id of the ONE zone raw node whose COMPILED span strictly contains
+/// `compiled` (cute-dbt#470, S2). Returns `None` when zero or MULTIPLE zones
+/// contain it (ambiguous origin ⇒ omit — never a guessed rawId). A zone with no
+/// compiled span (`compiled_out`) can never contain a compiled node, so it is
+/// skipped. "Strictly contains" excludes a zone span EQUAL to the compiled node
+/// (an equal span is the node itself, not a containing template).
+fn unique_containing_zone(
+    zone_compiled: &[(String, Option<SourceSpan>)],
+    compiled: &SourceSpan,
+) -> Option<String> {
+    let mut hit: Option<&str> = None;
+    for (zone_id, zspan) in zone_compiled {
+        let Some(zspan) = zspan else { continue };
+        if zspan != compiled && zspan.contains_range(compiled) {
+            if hit.is_some() {
+                // A second containing zone ⇒ ambiguous ⇒ omit.
+                return None;
+            }
+            hit = Some(zone_id);
+        }
+    }
+    hit.map(str::to_owned)
+}
+
 /// Project the `SpanRole::Zone` entries of a [`SourceMap`] into
 /// [`RawZonePayload`]s (cute-dbt#448, Z2) — the single localized `gather_<feat>`
 /// fn. For each zone entry: read `kind` + the raw `start`/`end`; DERIVE the
@@ -8111,6 +8319,384 @@ mod tests {
         );
     }
 
+    // ── cute-dbt#470 (S2): raw_dag nodes + node_map.compiled (N→1) ───────────
+
+    /// A `SourceMapEntry` for a `CteBody` carrying both a raw and a compiled
+    /// span — the shape `raw_scan::fill_raw_spans` produces for a verbatim CTE.
+    fn cte_entry_with_raw(node_id: &str, raw: SourceSpan, compiled: SourceSpan) -> SourceMapEntry {
+        SourceMapEntry {
+            role: SpanRole::CteBody {
+                node_id: node_id.to_owned(),
+            },
+            raw: Some(raw),
+            compiled: Some(compiled),
+        }
+    }
+
+    fn rs(start: u32, end: u32) -> SourceSpan {
+        SourceSpan {
+            start: SourcePos {
+                line: 1,
+                col: start + 1,
+                byte: start,
+            },
+            end: SourcePos {
+                line: 1,
+                col: end + 1,
+                byte: end,
+            },
+        }
+    }
+
+    #[test]
+    fn raw_dag_nodes_are_sound_literal_regions_with_a_compiled_in_cte() {
+        // A verbatim WITH CTE (raw uniquely anchored) becomes a `cte` raw node;
+        // every node carries a literal-region role + presence. The terminal here
+        // is WITH-bearing (a CTE exists), so NO terminal raw node is synthesized.
+        let compiled = "with a as (select 1) select * from a";
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![
+                cte_entry_with_raw("a", rs(5, 20), rs(5, 20)),
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: TERMINAL_NODE_NAME.to_owned(),
+                    },
+                    raw: None, // terminal has no verbatim raw region (S1 omits it)
+                    compiled: Some(rs(21, 36)),
+                },
+            ],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        let raw_dag = payload.raw_dag.expect("raw_dag present (a CTE resolved)");
+        // Exactly one raw node: the verbatim CTE `a`. No terminal node (WITH-bearing).
+        assert_eq!(raw_dag.nodes.len(), 1);
+        let node = &raw_dag.nodes[0];
+        assert_eq!(node.id, "a");
+        assert_eq!(node.role, "cte");
+        assert!(!node.is_zone);
+        assert_eq!(
+            node.presence, "compiled_in",
+            "a verbatim CTE is compiled-in"
+        );
+        // node_map.compiled folds the compiled CTE `a` → raw CTE `a` (identity).
+        let node_map = payload.node_map.expect("node_map present");
+        assert_eq!(node_map.compiled.get("a").map(String::as_str), Some("a"));
+        // The WITH-bearing terminal has no unique raw origin ⇒ its key is OMITTED.
+        assert!(
+            !node_map.compiled.contains_key(TERMINAL_NODE_NAME),
+            "WITH-bearing terminal omits its node_map key (no verbatim raw region)"
+        );
+    }
+
+    #[test]
+    fn with_less_model_emits_exactly_one_terminal_raw_node() {
+        // A WITH-less model: the compiled DAG is the lone terminal node and no
+        // CTE carries a raw span. Mirror from_cte_graph's terminal synthesis —
+        // exactly ONE terminal raw node — so node_map.compiled stays TOTAL.
+        let compiled = "select id, name from raw_customers";
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![SourceMapEntry {
+                role: SpanRole::CteBody {
+                    node_id: TERMINAL_NODE_NAME.to_owned(),
+                },
+                raw: None,
+                compiled: Some(rs(0, u32::try_from(compiled.len()).unwrap())),
+            }],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        let raw_dag = payload
+            .raw_dag
+            .expect("raw_dag present (terminal synthesized)");
+        assert_eq!(raw_dag.nodes.len(), 1, "exactly one terminal raw node");
+        let node = &raw_dag.nodes[0];
+        assert_eq!(node.id, TERMINAL_NODE_NAME);
+        assert_eq!(node.role, "terminal");
+        assert!(!node.is_zone);
+        assert_eq!(node.presence, "compiled_in");
+        // node_map.compiled is TOTAL: the lone compiled terminal maps to it.
+        let node_map = payload.node_map.expect("node_map present");
+        assert_eq!(
+            node_map
+                .compiled
+                .get(TERMINAL_NODE_NAME)
+                .map(String::as_str),
+            Some(TERMINAL_NODE_NAME),
+            "WITH-less terminal maps to the synthesized terminal raw node (total)"
+        );
+    }
+
+    #[test]
+    fn fanned_compiled_nodes_map_to_one_template_zone() {
+        // A {% for %} loop fans out N compiled CTEs (us_sales, eu_sales) whose
+        // compiled spans are CONTAINED in the loop zone's compiled span. All N
+        // map back to the ONE template zone raw node (by structural containment).
+        let compiled = "with us_sales as (select 1), eu_sales as (select 2) select * from us_sales";
+        // The loop zone's compiled region covers BOTH fanned CTE bodies [0,51).
+        let zone_compiled = rs(0, 51);
+        let us = rs(5, 27); // us_sales as (select 1)
+        let eu = rs(29, 51); // eu_sales as (select 2)
+        let term = rs(52, u32::try_from(compiled.len()).unwrap());
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![
+                // The fanned CTEs carry NO raw span (no verbatim per-iteration
+                // name in raw — they came from `{{ region }}_sales`), so they are
+                // NOT raw CTE nodes; they fold via the zone instead.
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: "us_sales".to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(us),
+                },
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: "eu_sales".to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(eu),
+                },
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: TERMINAL_NODE_NAME.to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(term),
+                },
+                // The ONE template zone (the {% for %} loop), compiling IN.
+                SourceMapEntry {
+                    role: SpanRole::Zone {
+                        kind: ZoneKind::ForLoop,
+                    },
+                    raw: Some(rs(0, 4)),
+                    compiled: Some(zone_compiled),
+                },
+            ],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        let node_map = payload.node_map.expect("node_map present");
+        // Both fanned CTEs map back to the SAME one template zone (zone:0).
+        assert_eq!(
+            node_map.compiled.get("us_sales").map(String::as_str),
+            Some("zone:0"),
+            "us_sales folds to the ONE template zone"
+        );
+        assert_eq!(
+            node_map.compiled.get("eu_sales").map(String::as_str),
+            Some("zone:0"),
+            "eu_sales folds to the SAME one template zone"
+        );
+        // There is exactly one zone raw node (the template), never N.
+        let raw_dag = payload.raw_dag.expect("raw_dag present");
+        let zone_nodes: Vec<_> = raw_dag.nodes.iter().filter(|n| n.is_zone).collect();
+        assert_eq!(zone_nodes.len(), 1, "the fanned loop is ONE template node");
+        assert_eq!(zone_nodes[0].id, "zone:0");
+        assert_eq!(zone_nodes[0].role, "zone");
+    }
+
+    #[test]
+    fn compiled_node_with_no_unique_origin_omits_its_key() {
+        // A macro-expanded compiled CTE with NO verbatim raw region AND no
+        // containing zone (e.g. a `{{ my_macro() }}`-generated CTE outside any
+        // located zone) has no unique raw origin ⇒ its node_map key is OMITTED
+        // (never a guessed rawId).
+        let compiled = "with gen as (select 1) select * from gen";
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: "gen".to_owned(),
+                    },
+                    raw: None, // macro-expanded: no verbatim raw anchor
+                    compiled: Some(rs(5, 22)),
+                },
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: TERMINAL_NODE_NAME.to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(rs(23, 40)),
+                },
+            ],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        // No raw node resolved AND no node_map key (both omitted) ⇒ both None.
+        assert!(
+            payload.raw_dag.is_none(),
+            "no sound raw region ⇒ raw_dag omitted"
+        );
+        assert!(
+            payload.node_map.is_none(),
+            "no unique raw origin for any compiled node ⇒ node_map omitted (never guessed)"
+        );
+    }
+
+    #[test]
+    fn ambiguous_two_containing_zones_omits_the_key() {
+        // A compiled node contained by TWO zones (e.g. nested loops whose
+        // compiled spans both contain it) has an ambiguous origin ⇒ OMIT
+        // (never pick one). Same omit-on-ambiguous posture as the raw-span fold.
+        let compiled = "with x as (select 1) select * from x";
+        let inner = rs(5, 20);
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: "x".to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(inner),
+                },
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: TERMINAL_NODE_NAME.to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(rs(21, 36)),
+                },
+                // Two zones BOTH containing `x`'s compiled span [5,20).
+                SourceMapEntry {
+                    role: SpanRole::Zone {
+                        kind: ZoneKind::ForLoop,
+                    },
+                    raw: Some(rs(0, 3)),
+                    compiled: Some(rs(0, 36)),
+                },
+                SourceMapEntry {
+                    role: SpanRole::Zone {
+                        kind: ZoneKind::IncrementalGuard,
+                    },
+                    raw: Some(rs(3, 6)),
+                    compiled: Some(rs(4, 30)),
+                },
+            ],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        let node_map = payload
+            .node_map
+            .expect("node_map present (terminal zone-less)");
+        assert!(
+            !node_map.compiled.contains_key("x"),
+            "two containing zones ⇒ ambiguous origin ⇒ omit (never pick one)"
+        );
+    }
+
+    #[test]
+    fn incremental_guard_zone_node_is_compiled_out_never_compiled_in() {
+        // A {% if is_incremental() %} guard pruned this build is a first-class
+        // raw node with presence `compiled_out` — NEVER "compiled"/"compiled_in".
+        // This is the dogfood honesty case: an incremental zone is visible in the
+        // raw DAG as compiled-out, the future toggle's show/hide target.
+        let compiled = "select *\nfrom events"; // the guard body is pruned
+        let sm = SourceMap {
+            compiled: compiled.to_owned(),
+            entries: vec![
+                SourceMapEntry {
+                    role: SpanRole::CteBody {
+                        node_id: TERMINAL_NODE_NAME.to_owned(),
+                    },
+                    raw: None,
+                    compiled: Some(rs(0, u32::try_from(compiled.len()).unwrap())),
+                },
+                SourceMapEntry {
+                    role: SpanRole::Zone {
+                        kind: ZoneKind::IncrementalGuard,
+                    },
+                    raw: Some(rs(0, 30)),
+                    compiled: None, // pruned this build
+                },
+            ],
+        };
+        let payload = CodeMapPayload::from_source_map(&sm);
+        let raw_dag = payload.raw_dag.expect("raw_dag present");
+        let zone = raw_dag
+            .nodes
+            .iter()
+            .find(|n| n.is_zone)
+            .expect("the incremental zone is a raw node");
+        assert_eq!(
+            zone.presence, "compiled_out",
+            "a pruned is_incremental() zone is compiled_out, NEVER compiled_in"
+        );
+        assert_eq!(zone.role, "zone");
+        // A compiled_out zone (no compiled span) can never be a fold target — the
+        // terminal still maps via WITH-less synthesis, not the zone.
+        let node_map = payload.node_map.expect("node_map present");
+        assert_eq!(
+            node_map
+                .compiled
+                .get(TERMINAL_NODE_NAME)
+                .map(String::as_str),
+            Some(TERMINAL_NODE_NAME),
+            "the WITH-less terminal maps to the terminal raw node, not the compiled_out zone"
+        );
+    }
+
+    #[test]
+    fn raw_dag_and_node_map_serialize_with_stable_wire_shape() {
+        // Pin the wire shape the prototype consumes: raw_dag.nodes carries
+        // id/role/is_zone/presence; node_map.compiled is a flat compiledId→rawId
+        // map. (Serialize-only payloads — no Deserialize round-trip needed.)
+        let raw_dag = RawDagPayload {
+            nodes: vec![
+                RawDagNodePayload {
+                    id: "a".to_owned(),
+                    role: "cte",
+                    is_zone: false,
+                    presence: "compiled_in",
+                },
+                RawDagNodePayload {
+                    id: "zone:0".to_owned(),
+                    role: "zone",
+                    is_zone: true,
+                    presence: "compiled_out",
+                },
+            ],
+        };
+        let json = serde_json::to_string(&raw_dag).unwrap();
+        assert_eq!(
+            json,
+            r#"{"nodes":[{"id":"a","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"zone:0","role":"zone","is_zone":true,"presence":"compiled_out"}]}"#
+        );
+        let mut compiled = BTreeMap::new();
+        compiled.insert("a".to_owned(), "a".to_owned());
+        compiled.insert("eu_sales".to_owned(), "zone:0".to_owned());
+        let node_map = NodeMapPayload { compiled };
+        let json = serde_json::to_string(&node_map).unwrap();
+        assert_eq!(json, r#"{"compiled":{"a":"a","eu_sales":"zone:0"}}"#);
+    }
+
+    #[test]
+    fn no_raw_structure_omits_both_keys_for_byte_stable_goldens() {
+        // A WITH-bearing model whose CTEs carry no raw span and that has no zones
+        // resolves NO raw nodes and NO node_map keys ⇒ both keys omitted, so
+        // pre-#470 goldens stay byte-stable.
+        let compiled = "with a as (select 1) select * from a";
+        let graph = parse_cte_graph(compiled).unwrap();
+        let sm = SourceMap::from_cte_graph(&graph, compiled, TERMINAL_NODE_NAME).unwrap();
+        // from_cte_graph leaves every raw: None (no scanner run) and adds no zones.
+        let payload = CodeMapPayload::from_source_map(&sm);
+        assert!(
+            payload.raw_dag.is_none(),
+            "no raw structure ⇒ raw_dag omitted"
+        );
+        assert!(
+            payload.node_map.is_none(),
+            "no unique origin ⇒ node_map omitted"
+        );
+        // And the JSON carries neither key (byte-stability).
+        let json = serde_json::to_string(&payload).unwrap();
+        assert!(!json.contains("raw_dag"), "raw_dag key absent on the wire");
+        assert!(
+            !json.contains("node_map"),
+            "node_map key absent on the wire"
+        );
+    }
+
     #[test]
     fn longest_literal_fragment_skips_jinja_constructs() {
         // The anchor is the longest literal run with Jinja stripped.
diff --git a/tests/fixtures/MANIFEST.toml b/tests/fixtures/MANIFEST.toml
index 955ad06..5e36224 100644
--- a/tests/fixtures/MANIFEST.toml
+++ b/tests/fixtures/MANIFEST.toml
@@ -808,6 +808,80 @@ to reconstruct the OLD side. Resolved by the ProjectFileReader under \
 generic state codes, no real records.\
 """
 
+[[fixture]]
+path = "incremental-showcase-current.json"
+origin = "synthetic-handcrafted"
+origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/470"
+sha256 = "e212f5b896f5bbeaebc9335dbb23ed6f2beeeabf39dfd8280b7308afdfe47582"
+synthetic_only = true
+synthetic_handcrafted = true
+license = "MIT"
+description = """
+The SYNTHETIC manifest for the raw-DAG incremental GOLDEN (cute-dbt#470, epic \
+#468, S2). One incremental model (`fct_events`) + its staging dep \
+(`stg_events`). `fct_events` raw_code wraps an inner CTE inside a \
+`{% if is_incremental() %}` guard; its compiled_code is the FRESH-build shape \
+(the guard body PRUNED), so the report's raw structural DAG shows the verbatim \
+`base` CTE as a `compiled_in` raw node and the pruned incremental guard as a \
+first-class `compiled_out` zone raw node (never falsely "compiled") — the \
+honest incremental-CTE visibility the spine was built for. The compiled_code \
+is HAND-AUTHORED (the fresh-build compile), never from a real `dbt compile`, \
+so it leaks no root_path/username. Pure synthetic data — generic event \
+columns, no real records.\
+"""
+
+[[fixture]]
+path = "incremental-showcase-pr-diff.patch"
+origin = "synthetic-handcrafted"
+origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/470"
+sha256 = "c6635e0afb441a6ebdaa52b0583d025ed6555c62706d882bfffa159ded862cf4"
+synthetic_only = true
+synthetic_handcrafted = true
+license = "MIT"
+description = """
+The hand-crafted PR diff for the incremental-showcase GOLDEN (cute-dbt#470). A \
+synthetic one-line edit to `models/marts/fct_events.sql` so the incremental \
+model lands in --pr-diff scope and its raw structural DAG renders in the \
+golden. No baseline — the showcase is --pr-diff only. Synthetic, no real \
+records.\
+"""
+
+[[fixture]]
+path = "incremental-showcase-source/models/marts/fct_events.sql"
+origin = "synthetic-handcrafted"
+origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/470"
+sha256 = "ebcab8a65bf1a843f5658c147bfa172942579a98ff89521ed411f84c0ae9d39f"
+synthetic_only = true
+synthetic_handcrafted = true
+license = "MIT"
+description = """
+The working-tree MODEL SQL for the incremental-showcase GOLDEN (cute-dbt#470). \
+A synthetic incremental model (`fct_events`) whose `with base as (...)` import \
+CTE precedes an inner `recent` CTE wrapped in a `{% if is_incremental() %}` \
+guard — byte-aligned to the committed `model.analytics.fct_events` raw_code in \
+incremental-showcase-current.json (the same-revision contract), so reading it \
+through `--project-root tests/fixtures/incremental-showcase-source` matches \
+the manifest. The incremental-showcase-pr-diff.patch carries an edit hunk \
+against this file so the model lands in --pr-diff scope. Pure synthetic SQL — \
+no real data.\
+"""
+
+[[fixture]]
+path = "incremental-showcase-source/models/staging/stg_events.sql"
+origin = "synthetic-handcrafted"
+origin_url = "https://github.com/breezy-bays-labs/cute-dbt/issues/470"
+sha256 = "ce7eb04cf0ed429e06a11a74cf6ace879218f76e61656583873b4d26ae4ea4de"
+synthetic_only = true
+synthetic_handcrafted = true
+license = "MIT"
+description = """
+The working-tree staging SQL for the incremental-showcase GOLDEN \
+(cute-dbt#470). A synthetic staging model (`stg_events`) the incremental \
+`fct_events` model `ref()`s — byte-aligned to the committed \
+`model.analytics.stg_events` raw_code in incremental-showcase-current.json (the \
+same-revision contract). Pure synthetic SQL — no real data.\
+"""
+
 [[fixture]]
 path = "prdiff-minidag-current.json"
 origin = "synthetic-handcrafted"
diff --git a/tests/fixtures/incremental-showcase-current.json b/tests/fixtures/incremental-showcase-current.json
new file mode 100644
index 0000000..49ed594
--- /dev/null
+++ b/tests/fixtures/incremental-showcase-current.json
@@ -0,0 +1,96 @@
+{
+    "metadata": {
+        "dbt_schema_version": "https://schemas.getdbt.com/dbt/manifest/v12.json",
+        "project_name": "analytics"
+    },
+    "nodes": {
+        "model.analytics.fct_events": {
+            "name": "fct_events",
+            "resource_type": "model",
+            "package_name": "analytics",
+            "unique_id": "model.analytics.fct_events",
+            "original_file_path": "models/marts/fct_events.sql",
+            "path": "marts/fct_events.sql",
+            "checksum": {
+                "name": "sha256",
+                "checksum": "c0ffee00c0ffee00c0ffee00c0ffee00c0ffee00c0ffee00c0ffee00c0ffee00"
+            },
+            "compiled_code": "with base as (\n    select event_id, occurred_at, amount from \"memory\".\"main\".\"stg_events\"\n)\nselect event_id, occurred_at, amount from base\n",
+            "raw_code": "{{ config(materialized='incremental', unique_key='event_id') }}\n\nwith base as (\n    select event_id, occurred_at, amount from {{ ref('stg_events') }}\n)\n\n{% if is_incremental() %}\n, recent as (\n    select event_id, occurred_at, amount\n    from base\n    where occurred_at > (select max(occurred_at) from {{ this }})\n)\n{% endif %}\n\nselect event_id, occurred_at, amount from base\n",
+            "depends_on": {
+                "macros": [],
+                "nodes": [
+                    "model.analytics.stg_events"
+                ]
+            },
+            "config": {
+                "materialized": "incremental",
+                "unique_key": "event_id"
+            }
+        },
+        "model.analytics.stg_events": {
+            "name": "stg_events",
+            "resource_type": "model",
+            "package_name": "analytics",
+            "unique_id": "model.analytics.stg_events",
+            "original_file_path": "models/staging/stg_events.sql",
+            "path": "staging/stg_events.sql",
+            "checksum": {
+                "name": "sha256",
+                "checksum": "d00dad00d00dad00d00dad00d00dad00d00dad00d00dad00d00dad00d00dad000"
+            },
+            "compiled_code": "select event_id, occurred_at, amount from \"memory\".\"main\".\"raw_events\"\n",
+            "raw_code": "select event_id, occurred_at, amount from {{ source('raw', 'raw_events') }}\n",
+            "depends_on": {
+                "macros": [],
+                "nodes": []
+            },
+            "config": {}
+        }
+    },
+    "sources": {},
+    "macros": {},
+    "unit_tests": {
+        "unit_test.analytics.fct_events.test_fct_events_passthrough": {
+            "model": "fct_events",
+            "given": [
+                {
+                    "input": "ref('stg_events')",
+                    "rows": [
+                        {
+                            "event_id": "e1",
+                            "occurred_at": "2024-01-01",
+                            "amount": "10"
+                        }
+                    ],
+                    "format": "dict",
+                    "fixture": null
+                }
+            ],
+            "expect": {
+                "rows": [
+                    {
+                        "event_id": "e1",
+                        "occurred_at": "2024-01-01",
+                        "amount": "10"
+                    }
+                ],
+                "format": "dict",
+                "fixture": null
+            },
+            "name": "test_fct_events_passthrough",
+            "resource_type": "unit_test",
+            "package_name": "analytics",
+            "path": "marts/unit_tests.yml",
+            "original_file_path": "models/marts/unit_tests.yml",
+            "unique_id": "unit_test.analytics.fct_events.test_fct_events_passthrough",
+            "depends_on": {
+                "macros": [],
+                "nodes": [
+                    "model.analytics.fct_events"
+                ]
+            },
+            "config": {}
+        }
+    }
+}
diff --git a/tests/fixtures/incremental-showcase-pr-diff.patch b/tests/fixtures/incremental-showcase-pr-diff.patch
new file mode 100644
index 0000000..2580a18
--- /dev/null
+++ b/tests/fixtures/incremental-showcase-pr-diff.patch
@@ -0,0 +1,6 @@
+diff --git a/models/marts/fct_events.sql b/models/marts/fct_events.sql
+--- a/models/marts/fct_events.sql
++++ b/models/marts/fct_events.sql
+@@ -3 +3 @@
+-with base as (   -- old
++with base as (
diff --git a/tests/fixtures/incremental-showcase-source/models/marts/fct_events.sql b/tests/fixtures/incremental-showcase-source/models/marts/fct_events.sql
new file mode 100644
index 0000000..bc195ca
--- /dev/null
+++ b/tests/fixtures/incremental-showcase-source/models/marts/fct_events.sql
@@ -0,0 +1,15 @@
+{{ config(materialized='incremental', unique_key='event_id') }}
+
+with base as (
+    select event_id, occurred_at, amount from {{ ref('stg_events') }}
+)
+
+{% if is_incremental() %}
+, recent as (
+    select event_id, occurred_at, amount
+    from base
+    where occurred_at > (select max(occurred_at) from {{ this }})
+)
+{% endif %}
+
+select event_id, occurred_at, amount from base
diff --git a/tests/fixtures/incremental-showcase-source/models/staging/stg_events.sql b/tests/fixtures/incremental-showcase-source/models/staging/stg_events.sql
new file mode 100644
index 0000000..7cf934e
--- /dev/null
+++ b/tests/fixtures/incremental-showcase-source/models/staging/stg_events.sql
@@ -0,0 +1 @@
+select event_id, occurred_at, amount from {{ source('raw', 'raw_events') }}
diff --git a/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap b/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
index 880d317..883a882 100644
--- a/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
+++ b/tests/snapshots/render_integration__rendered_chrome_jaffle_shop.snap
@@ -2357,7 +2357,7 @@ html[data-style="swiss"] .degraded-chip { border-radius: 0; }
 <div class="lens-panel" role="tabpanel" id="lens-panel-project" aria-labelledby="lens-tab-project" data-lens="project" data-testid="lens-panel-project" hidden>
 </div>
 
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+<script type="application/json" id="cute-dbt-data">{"baseline":"jaffle-shop-baseline.json","models":[{"name":"stg_customers","path":"models/staging/stg_customers.sql","dag":{"nodes":[{"id":"source","role":"import"},{"id":"renamed","role":"transform"},{"id":"(final select)","label":"stg_customers.sql","role":"final"}],"edges":[{"from":"source","to":"renamed","edge_type":"from"},{"from":"renamed","to":"(final select)","edge_type":"from"}]},"compiled_sql":{"(final select)":"select * from renamed","renamed":"renamed as (\n\n    select\n        id as customer_id,\n        trim(first_name) as first_name,\n        trim(last_name) as last_name\n\n    from source\n\n)","source":"source as (\n    select * from \"jaffle_shop\".\"main\".\"raw_customers\"\n\n)"},"code_map":{"compiled":"with source as (\n    select * from \"jaffle_shop\".\"main\".\"raw_customers\"\n\n),\n\nrenamed as (\n\n    select\n        id as customer_id,\n        trim(first_name) as first_name,\n        trim(last_name) as last_name\n\n    from source\n\n)\n\nselect * from renamed","node_spans":{"(final select)":{"start":{"line":17,"col":1,"byte":227},"end":{"line":17,"col":22,"byte":248}},"renamed":{"start":{"line":6,"col":1,"byte":77},"end":{"line":15,"col":2,"byte":225}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":4,"col":2,"byte":74}}},"column_spans":{"(final select)\u001f*":{"start":{"line":17,"col":8,"byte":234},"end":{"line":17,"col":9,"byte":235}},"renamed\u001fcustomer_id":{"start":{"line":9,"col":9,"byte":110},"end":{"line":9,"col":26,"byte":127}},"renamed\u001ffirst_name":{"start":{"line":10,"col":14,"byte":142},"end":{"line":10,"col":39,"byte":167}},"renamed\u001flast_name":{"start":{"line":11,"col":14,"byte":182},"end":{"line":11,"col":37,"byte":205}},"source\u001f*":{"start":{"line":2,"col":12,"byte":28},"end":{"line":2,"col":13,"byte":29}}},"raw_node_spans":{"renamed":{"start":{"line":8,"col":1,"byte":301},"end":{"line":17,"col":2,"byte":449}},"source":{"start":{"line":1,"col":6,"byte":5},"end":{"line":6,"col":2,"byte":298}}},"raw_column_spans":{"renamed\u001fcustomer_id":{"start":{"line":11,"col":15,"byte":340},"end":{"line":11,"col":26,"byte":351}}},"raw_dag":{"nodes":[{"id":"renamed","role":"cte","is_zone":false,"presence":"compiled_in"},{"id":"source","role":"cte","is_zone":false,"presence":"compiled_in"}]},"node_map":{"compiled":{"renamed":"renamed","source":"source"}}},"column_lineage":{"context":{"customer_id":{"tests":[{"kind":"not_null","kwargs":{"column_name":"customer_id","model":"{{ get_where_subquery(ref('stg_customers')) }}"},"node_id":"test.jaffle_shop.not_null_stg_customers_customer_id.e2cfb1f9aa"},{"kind":"unique","kwargs":{"column_name":"customer_id","model":"{{ get_where_subquery(ref('stg_customers')) }}"},"node_id":"test.jaffle_shop.unique_stg_customers_customer_id.c7614daada"}],"documented":true}},"edges":[{"from_col":{"scope":{"intra":{"node_id":"raw_customers"}},"column":"*"},"to_col":{"scope":{"intra":{"node_id":"source"}},"column":"*"},"kind":"source","confidence":"opaque"},{"from_col":{"scope":{"intra":{"node_id":"source"}},"column":"id"},"to_col":{"scope":{"intra":{"node_id":"renamed"}},"column":"customer_id"},"kind":"renamed","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"renamed"}},"column":"customer_id"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"customer_id"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"renamed"}},"column":"first_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"first_name"},"kind":"pass_through","confidence":"resolved"},{"from_col":{"scope":{"intra":{"node_id":"renamed"}},"column":"last_name"},"to_col":{"scope":{"intra":{"node_id":"(final select)"}},"column":"last_name"},"kind":"pass_through","confidence":"resolved"}]},"raw_sql":"with source as (\n\n    {#-\n    Normally we would select from the table here, but we are using seeds to load\n    our data in this project. 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 <script>
 <<inlined-asset>>

From 46e4a4fec5dbeffee9e575f953dc12bf33cba473 Mon Sep 17 00:00:00 2001
From: Christopher Bays <cmbays91@gmail.com>
Date: Tue, 23 Jun 2026 01:59:49 -0400
Subject: [PATCH 2/2] =?UTF-8?q?fix(adapters):=20#470=20=E2=80=94=20S2=20fo?=
 =?UTF-8?q?ld=20maps=20only=20uniquely-locatable=20origins;=20defer=20fann?=
 =?UTF-8?q?ed=E2=86=92zone=20mapping=20to=20S3=20(CodeRabbit=20Major)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

`build_raw_dag_and_node_map` folded compiled CTEs to a template zone via
`unique_containing_zone`, which checked whether a zone's `entry.compiled`
span CONTAINS the compiled node. But `entry.compiled` (from
`resolve_zone_compiled`) is a single unambiguous literal ANCHOR (one
≥3-char fragment), NOT the zone's compiled EXTENT — so it can never
contain the separate fanned CTE bodies. The branch was dead in every real
golden, and the test `fanned_compiled_nodes_map_to_one_template_zone`
masked this by hand-authoring an unrealizable span covering two CTE
bodies. CodeRabbit (Major) flagged it correctly.

Honest rescope: S2 now maps ONLY uniquely-locatable origins — a verbatim
CTE (same id) and the WITH-less terminal. Fanned/macro CTEs have no
uniquely-locatable raw origin in S2 and are honestly OMITTED (never a
guessed rawId). The observed fanned→zone mapping needs the zone's
compiled EXTENT + node_map.raw, so it lands in S3 #471.

- Remove the zone-containment fold branch + the `zone_compiled` vec it fed
  (the zone raw NODES are still emitted, unchanged).
- Delete the now-unused `unique_containing_zone` fn.
- Rewrite `fanned_compiled_nodes_map_to_one_template_zone` →
  `fanned_cte_with_no_verbatim_raw_origin_is_omitted_in_s2` (a realistic
  anchor, asserts the fanned CTEs are OMITTED + the zone node is present).
- Delete `ambiguous_two_containing_zones_omits_the_key` (tested the deleted
  fn; the omit-on-ambiguous concern moves to S3's observed mapping).

All goldens stay byte-identical (the branch was dead in all real goldens).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0199AmBCec5kyVEEF1Qd7TSF
---
 src/adapters/render.rs | 175 ++++++++++++++---------------------------
 1 file changed, 60 insertions(+), 115 deletions(-)

diff --git a/src/adapters/render.rs b/src/adapters/render.rs
index dcb7230..c7c360b 100644
--- a/src/adapters/render.rs
+++ b/src/adapters/render.rs
@@ -506,15 +506,18 @@ impl CodeMapPayload {
 ///   appearance order). `presence` is the honest 3-state [`Presence`] verdict
 ///   (a pruned `is_incremental()` zone is `compiled_out`, NEVER `compiled_in`).
 ///
-/// ## `node_map.compiled` (N→1, `compiledId → rawId`, omit-on-ambiguous)
+/// ## `node_map.compiled` (N→1, `compiledId → rawId`, S2 = uniquely-locatable only)
 ///
-/// For each compiled DAG node id:
+/// For each compiled DAG node id, S2 maps ONLY the two origins it can locate
+/// UNAMBIGUOUSLY without a zone compiled-EXTENT:
 /// - A raw CTE node with the SAME id ⇒ map to it (the verbatim-CTE identity).
 /// - Else the WITH-less terminal ⇒ map to the synthesized terminal raw node.
-/// - Else fold by STRUCTURAL CONTAINMENT: the raw zone node whose COMPILED span
-///   uniquely contains this compiled node's span ⇒ map to that ONE zone (the
-///   `{% for %}`-fanned CTEs → the ONE template node). Zero or MULTIPLE
-///   containing zones ⇒ OMIT the key (never a guessed `rawId`).
+/// - Everything else (a `{% for %}`-fanned CTE, a macro-expanded CTE) has NO
+///   uniquely-locatable raw origin in S2 and is honestly OMITTED — never a
+///   guessed `rawId`. The observed fanned→zone mapping needs the zone's compiled
+///   EXTENT (a zone's `entry.compiled` is a single literal ANCHOR, not an
+///   extent — see `resolve_zone_compiled`), so it lands in S3 #471 alongside
+///   `node_map.raw`.
 fn build_raw_dag_and_node_map(
     sm: &SourceMap,
     node_spans: &BTreeMap<String, SourceSpan>,
@@ -550,38 +553,41 @@ fn build_raw_dag_and_node_map(
         });
     }
     // Zone raw nodes — id `zone:<index>` by appearance order; carry the honest
-    // 3-state presence + (compiled span, if any) for the structural fold below.
+    // 3-state presence. The zone's `entry.compiled` is a single literal ANCHOR
+    // (`resolve_zone_compiled`), NOT the zone's compiled EXTENT, so it cannot
+    // locate the separate fanned CTE bodies — S2 emits the zone NODE but folds
+    // nothing through it (the fanned→zone mapping is S3 #471, which needs the
+    // extent + `node_map.raw`).
     let cte_spans: Vec<SourceSpan> = node_spans.values().copied().collect();
-    let mut zone_compiled: Vec<(String, Option<SourceSpan>)> = Vec::new();
-    for entry in sm
+    for (zone_index, entry) in sm
         .entries
         .iter()
         .filter(|e| matches!(e.role, SpanRole::Zone { .. }))
+        .enumerate()
     {
-        let zone_id = format!("zone:{}", zone_compiled.len());
         nodes.push(RawDagNodePayload {
-            id: zone_id.clone(),
+            id: format!("zone:{zone_index}"),
             role: "zone",
             is_zone: true,
             presence: entry.presence(&cte_spans).to_wire(),
         });
-        zone_compiled.push((zone_id, entry.compiled));
     }
 
-    // node_map.compiled — fold each compiled DAG node back to its raw origin.
+    // node_map.compiled — fold each compiled DAG node back to the ONLY origins
+    // S2 can locate UNAMBIGUOUSLY: a verbatim CTE (same id) and the WITH-less
+    // terminal. A fanned/macro CTE has no uniquely-locatable raw origin here (a
+    // zone's `entry.compiled` is an anchor, not an extent), so it is honestly
+    // OMITTED — never a guessed rawId. The fanned→zone mapping lands in S3 #471.
     let mut compiled_map: BTreeMap<String, String> = BTreeMap::new();
-    for (compiled_id, compiled_span) in node_spans {
+    for compiled_id in node_spans.keys() {
         if raw_cte_ids.contains(compiled_id) {
             // Verbatim CTE identity: compiled id == raw id.
             compiled_map.insert(compiled_id.clone(), compiled_id.clone());
         } else if is_with_less && compiled_id == TERMINAL_NODE_NAME {
             // WITH-less terminal ⇒ the synthesized terminal raw node.
             compiled_map.insert(compiled_id.clone(), TERMINAL_NODE_NAME.to_owned());
-        } else if let Some(zone_id) = unique_containing_zone(&zone_compiled, compiled_span) {
-            // A fanned compiled CTE inside a {% for %} ⇒ the ONE template zone.
-            compiled_map.insert(compiled_id.clone(), zone_id);
         }
-        // No unique raw origin ⇒ OMIT (never a guessed rawId).
+        // No uniquely-locatable raw origin ⇒ OMIT (never a guessed rawId).
     }
 
     let raw_dag = (!nodes.is_empty()).then_some(RawDagPayload { nodes });
@@ -591,30 +597,6 @@ fn build_raw_dag_and_node_map(
     (raw_dag, node_map)
 }
 
-/// The id of the ONE zone raw node whose COMPILED span strictly contains
-/// `compiled` (cute-dbt#470, S2). Returns `None` when zero or MULTIPLE zones
-/// contain it (ambiguous origin ⇒ omit — never a guessed rawId). A zone with no
-/// compiled span (`compiled_out`) can never contain a compiled node, so it is
-/// skipped. "Strictly contains" excludes a zone span EQUAL to the compiled node
-/// (an equal span is the node itself, not a containing template).
-fn unique_containing_zone(
-    zone_compiled: &[(String, Option<SourceSpan>)],
-    compiled: &SourceSpan,
-) -> Option<String> {
-    let mut hit: Option<&str> = None;
-    for (zone_id, zspan) in zone_compiled {
-        let Some(zspan) = zspan else { continue };
-        if zspan != compiled && zspan.contains_range(compiled) {
-            if hit.is_some() {
-                // A second containing zone ⇒ ambiguous ⇒ omit.
-                return None;
-            }
-            hit = Some(zone_id);
-        }
-    }
-    hit.map(str::to_owned)
-}
-
 /// Project the `SpanRole::Zone` entries of a [`SourceMap`] into
 /// [`RawZonePayload`]s (cute-dbt#448, Z2) — the single localized `gather_<feat>`
 /// fn. For each zone entry: read `kind` + the raw `start`/`end`; DERIVE the
@@ -8428,22 +8410,33 @@ mod tests {
     }
 
     #[test]
-    fn fanned_compiled_nodes_map_to_one_template_zone() {
-        // A {% for %} loop fans out N compiled CTEs (us_sales, eu_sales) whose
-        // compiled spans are CONTAINED in the loop zone's compiled span. All N
-        // map back to the ONE template zone raw node (by structural containment).
+    fn fanned_cte_with_no_verbatim_raw_origin_is_omitted_in_s2() {
+        // A {% for %} loop fans out N compiled CTEs (us_sales, eu_sales) from a
+        // single `{{ region }}_sales` template. In S2 the zone's `entry.compiled`
+        // is a single literal ANCHOR (what `resolve_zone_compiled` produces — one
+        // ≥3-char fragment located unambiguously), NOT the zone's compiled
+        // EXTENT, so it cannot locate the separate fanned CTE bodies. The fanned
+        // CTEs therefore have NO uniquely-locatable raw origin in S2 and are
+        // honestly OMITTED from node_map.compiled (never mapped to a guessed
+        // zone) — while the ONE template zone is still emitted as a raw node with
+        // its honest presence. The observed fanned→zone mapping (which needs the
+        // zone compiled-EXTENT + node_map.raw) is S3 #471's job.
         let compiled = "with us_sales as (select 1), eu_sales as (select 2) select * from us_sales";
-        // The loop zone's compiled region covers BOTH fanned CTE bodies [0,51).
-        let zone_compiled = rs(0, 51);
         let us = rs(5, 27); // us_sales as (select 1)
         let eu = rs(29, 51); // eu_sales as (select 2)
         let term = rs(52, u32::try_from(compiled.len()).unwrap());
+        // A realistic anchor: the loop body's one unambiguous literal fragment
+        // resolved inside the first fanned CTE's body — strictly nested in the
+        // `us_sales` CteBody span, so its honest presence is `structural`. It is
+        // an ANCHOR, NOT the loop's extent: it does NOT cover the `eu_sales` body.
+        let zone_anchor = rs(18, 26); // "select 1" — a literal fragment inside us_sales
         let sm = SourceMap {
             compiled: compiled.to_owned(),
             entries: vec![
                 // The fanned CTEs carry NO raw span (no verbatim per-iteration
                 // name in raw — they came from `{{ region }}_sales`), so they are
-                // NOT raw CTE nodes; they fold via the zone instead.
+                // NOT raw CTE nodes. With only an anchor (not an extent) for the
+                // zone, S2 cannot locate them ⇒ omitted.
                 SourceMapEntry {
                     role: SpanRole::CteBody {
                         node_id: "us_sales".to_owned(),
@@ -8465,35 +8458,38 @@ mod tests {
                     raw: None,
                     compiled: Some(term),
                 },
-                // The ONE template zone (the {% for %} loop), compiling IN.
+                // The ONE template zone (the {% for %} loop). Its compiled span
+                // is the resolved literal anchor, NOT the loop's extent.
                 SourceMapEntry {
                     role: SpanRole::Zone {
                         kind: ZoneKind::ForLoop,
                     },
                     raw: Some(rs(0, 4)),
-                    compiled: Some(zone_compiled),
+                    compiled: Some(zone_anchor),
                 },
             ],
         };
         let payload = CodeMapPayload::from_source_map(&sm);
-        let node_map = payload.node_map.expect("node_map present");
-        // Both fanned CTEs map back to the SAME one template zone (zone:0).
-        assert_eq!(
-            node_map.compiled.get("us_sales").map(String::as_str),
-            Some("zone:0"),
-            "us_sales folds to the ONE template zone"
-        );
-        assert_eq!(
-            node_map.compiled.get("eu_sales").map(String::as_str),
-            Some("zone:0"),
-            "eu_sales folds to the SAME one template zone"
+        // The fanned CTEs are OMITTED — never mapped (no uniquely-locatable raw
+        // origin in S2). The terminal is WITH-bearing (CTEs exist) so it is also
+        // omitted, leaving node_map.compiled empty ⇒ the whole key is omitted.
+        assert!(
+            payload.node_map.is_none(),
+            "fanned CTEs + a WITH-bearing terminal have no uniquely-locatable raw \
+             origin in S2 ⇒ node_map omitted (the fanned→zone mapping is S3 #471)"
         );
-        // There is exactly one zone raw node (the template), never N.
-        let raw_dag = payload.raw_dag.expect("raw_dag present");
+        // The ONE template zone is still emitted as a raw node with its honest
+        // presence — the zone node lands in S2; only its fold target is deferred.
+        let raw_dag = payload.raw_dag.expect("raw_dag present (the zone node)");
         let zone_nodes: Vec<_> = raw_dag.nodes.iter().filter(|n| n.is_zone).collect();
         assert_eq!(zone_nodes.len(), 1, "the fanned loop is ONE template node");
         assert_eq!(zone_nodes[0].id, "zone:0");
         assert_eq!(zone_nodes[0].role, "zone");
+        assert_eq!(
+            zone_nodes[0].presence, "structural",
+            "the zone's anchor is strictly nested inside the us_sales CteBody ⇒ \
+             structural (the honest 3-state presence verdict)"
+        );
     }
 
     #[test]
@@ -8534,57 +8530,6 @@ mod tests {
         );
     }
 
-    #[test]
-    fn ambiguous_two_containing_zones_omits_the_key() {
-        // A compiled node contained by TWO zones (e.g. nested loops whose
-        // compiled spans both contain it) has an ambiguous origin ⇒ OMIT
-        // (never pick one). Same omit-on-ambiguous posture as the raw-span fold.
-        let compiled = "with x as (select 1) select * from x";
-        let inner = rs(5, 20);
-        let sm = SourceMap {
-            compiled: compiled.to_owned(),
-            entries: vec![
-                SourceMapEntry {
-                    role: SpanRole::CteBody {
-                        node_id: "x".to_owned(),
-                    },
-                    raw: None,
-                    compiled: Some(inner),
-                },
-                SourceMapEntry {
-                    role: SpanRole::CteBody {
-                        node_id: TERMINAL_NODE_NAME.to_owned(),
-                    },
-                    raw: None,
-                    compiled: Some(rs(21, 36)),
-                },
-                // Two zones BOTH containing `x`'s compiled span [5,20).
-                SourceMapEntry {
-                    role: SpanRole::Zone {
-                        kind: ZoneKind::ForLoop,
-                    },
-                    raw: Some(rs(0, 3)),
-                    compiled: Some(rs(0, 36)),
-                },
-                SourceMapEntry {
-                    role: SpanRole::Zone {
-                        kind: ZoneKind::IncrementalGuard,
-                    },
-                    raw: Some(rs(3, 6)),
-                    compiled: Some(rs(4, 30)),
-                },
-            ],
-        };
-        let payload = CodeMapPayload::from_source_map(&sm);
-        let node_map = payload
-            .node_map
-            .expect("node_map present (terminal zone-less)");
-        assert!(
-            !node_map.compiled.contains_key("x"),
-            "two containing zones ⇒ ambiguous origin ⇒ omit (never pick one)"
-        );
-    }
-
     #[test]
     fn incremental_guard_zone_node_is_compiled_out_never_compiled_in() {
         // A {% if is_incremental() %} guard pruned this build is a first-class