adds unit mapping functions to utilities.py

DataSet.py

@@ -91,6 +91,7 @@ def get_data( self, region_type, region_value, state=None,
                             + filter
             else:
                 x_sql = self.sql + ' where ' + filter
+            print (x_sql) #Debugging
             program_data = get_echo_data( x_sql, self.idx_field )
         except pd.errors.EmptyDataError:
             print( "No program records were found." )
@@ -336,7 +337,7 @@ def _set_facility_filter( self, region_type, region_value=None, state=None ):
             value_type = type(region_value)
             if ( value_type == list or value_type == tuple ):
                 for region in region_value:
-                    if ( region_type == 'Congressional District' ):
+                    if ( region_type == 'Congressional District' or region_type == 'Census Tract'):
                         id_string += str( region ) + ','
                     else:
                         id_string += '\'' + str( region ) + '\','
@@ -346,4 +347,5 @@ def _set_facility_filter( self, region_type, region_value=None, state=None ):
                 filter += ' = \'' + region_value + '\'' 
         if ( region_type == 'Congressional District' or region_type == 'County' ):
             filter += ' and "FAC_STATE" = \'' + state + '\''
+        print(filter) #debugging
         return filter

geographies.py

@@ -1,16 +1,151 @@
+spatial_tables = {
+    #table_name = name of table in spatial database
+    #id_field = field used to identify the data
+    #match_field = field used to match data with ECHO
+    #pretty_field = field that has a human-readable ID e.g. county name
+
+    # HUC8
+    "Watershed": dict(
+        table_name="wbdhu8",
+        id_field="huc8",
+        match_field="huc8",
+        pretty_field="NAME"
+    ),
+
+    "HUC10 Watersheds": dict(
+        table_name="wbdhu10",
+        id_field="huc10",
+        match_field="huc10",
+        pretty_field="NAME"
+    ),
+
+    "HUC12 Watersheds": dict(
+        table_name="wbdhu12",
+        id_field="huc12",
+        match_field="huc12",
+        pretty_field="NAME"
+    ),
+
+    "Ecoregions": dict(
+        table_name="eco_level3",
+        id_field="US_L3NAME" #e.g. Atlantic Coastal Pine Barrens     
+    ),
+
+    "County": dict(
+        table_name="tl_2019_us_county",
+        id_field="GEOID", # four or five digit code corresponding to two digit state number (e.g. 55) and 2-3 digit county code! 
+        match_field="NAME", #match with state_counties.csv
+        pretty_field="NAME" # e.g. CEDAR
+    ),
+
+    "Zip Code": dict(
+        table_name="tl_2019_us_zcta510",
+        id_field="zcta5ce10",
+        match_field="zcta5ce10",
+        pretty_field="zcta5ce10" 
+    ),
+
+    "EPA Region": dict(
+        table_name="epa_regions",
+        id_field="eparegion" # In the form of "Region 1", "Region 2", up to "Region 10"
+    ),
+
+    "State": dict(
+        table_name = "tl_2019_us_state",
+        id_field = "STUSPS", # e.g. MS, IA, AK
+        match_field="STUSPS",
+        pretty_field="NAME" # e.g. cedar
+    ),
+
+    "Congressional District": dict(
+        table_name = "tl_2019_us_cd116", # Unfortunately, ECHO seems based on 113th Congress
+        id_field = "GEOID", # this is the combination of the state id and the CD e.g. AR-2 = 0502
+        match_field="CD116FP", #match with state_counties.csv
+        pretty_field="CD116FP" # two digit state-specific district number
+    )
+    ,
+
+    "Census Tract": dict(
+        table_name = "###", # 
+        id_field = "GEOID10", # 
+        match_field="GEOID10", # 
+        pretty_field="GEOID10" # NAMELSAD10 ?
+    )
+}
+
 region_field = {
     'State': { "field": 'FAC_STATE' },
     'Congressional District': { "field": 'FAC_DERIVED_CD113' },
     'County': { "field": 'FAC_COUNTY' },
     'Zip Code': { "field": 'FAC_ZIP' },
+    'Watershed': {"field": 'FAC_DERIVED_HUC'},
+    'Census Tract': {"field": 'FAC_DERIVED_CB2010'},
 }
 # Commenting out these region types until implemented
-#     'Watershed': {"field": 'FAC_DERIVED_HUC'},
-#     'Census Block': {"field": 'FAC_DERIVED_CB2010'}
+#     'Census Block': {"field": 'FAC_DERIVED_CB2010'} # We don't have this in the spatial database because there are too many CBs - too much data!
+#     'EPA Region': { "field": 'FAC_EPA_REGION'} # Possibly too large to handle in Colab environment
 
 states = ["AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DC", "DE", "FL", "GA",
           "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
           "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
           "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
           "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY"]
 
+fips = {
+  "AK": "02",
+  "AL": "01",
+  "AR": "05",
+  "AS": "60",
+  "AZ": "04",
+  "CA": "06",
+  "CO": "08",
+  "CT": "09",
+  "DC": "11",
+  "DE": "10",
+  "FL": "12",
+  "GA": "13",
+  "GU": "66",
+  "HI": "15",
+  "IA": "19",
+  "ID": "16",
+  "IL": "17",
+  "IN": "18",
+  "KS": "20",
+  "KY": "21",
+  "LA": "22",
+  "MA": "25",
+  "MD": "24",
+  "ME": "23",
+  "MI": "26",
+  "MN": "27",
+  "MO": "29",
+  "MS": "28",
+  "MT": "30",
+  "NC": "37",
+  "ND": "38",
+  "NE": "31",
+  "NH": "33",
+  "NJ": "34",
+  "NM": "35",
+  "NV": "32",
+  "NY": "36",
+  "OH": "39",
+  "OK": "40",
+  "OR": "41",
+  "PA": "42",
+  "PR": "72",
+  "RI": "44",
+  "SC": "45",
+  "SD": "46",
+  "TN": "47",
+  "TX": "48",
+  "UT": "49",
+  "VA": "51",
+  "VI": "78",
+  "VT": "50",
+  "WA": "53",
+  "WI": "55",
+  "WV": "54",
+  "WY": "56"
+}
+