datapi

ECMWF Software EnginE (ESEE) Data Stores API Python Client.

Technical documentation: https://ecmwf-projects.github.io/datapi/

Installation

Install with conda:

$ conda install -c conda-forge datapi

Install with pip:

$ pip install datapi

Configuration

The ApiClient requires the url to the API root and a valid API key. You can also set the DATAPI_URL and DATAPI_KEY environment variables, or use a configuration file. The configuration file must be located at ~/.datapirc, or at the path specified by the DATAPI_RC environment variable.

$ cat $HOME/.datapirc
url: https://cds.climate.copernicus.eu/api
key: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

It is possible (though not recommended) to use the API key of one of the test users:

00112233-4455-6677-c899-aabbccddeeff

This key is used for anonymous tests and is designed to be the least performant option for accessing the system.

Quick Start

Configure the logging level to display INFO messages:

>>> import logging
>>> logging.basicConfig(level="INFO")

Instantiate the API client and optionally verify authentication:

>>> import os
>>> from datapi import ApiClient
>>> client = ApiClient(
...     url=os.getenv("DATAPI_URL"),
...     key=os.getenv("DATAPI_KEY"),
... )
>>> client.check_authentication()  # optional check
{...}

Retrieve data:

>>> collection_id = "reanalysis-era5-pressure-levels"
>>> request = {
...     "product_type": ["reanalysis"],
...     "variable": ["temperature"],
...     "year": ["2022"],
...     "month": ["01"],
...     "day": ["01"],
...     "time": ["00:00"],
...     "pressure_level": ["1000"],
...     "data_format": "grib",
...     "download_format": "unarchived"
...     }

>>> client.retrieve(collection_id, request, target="target_1.grib")  # blocks
'target_1.grib'

>>> remote = client.submit(collection_id, request)  # doesn't block
>>> remote
Remote(...)
>>> remote.download("target_2.grib")  # blocks
'target_2.grib'

>>> results = client.submit_and_wait_on_results(collection_id, request)  # blocks
>>> results
Results(...)
>>> results.download("target_3.grib")
'target_3.grib'

>>> client.download_results(remote.request_id, "target_4.grib")  # blocks
'target_4.grib'

List all collection IDs sorted by last update:

>>> collections = client.get_collections(sortby="update")

>>> collection_ids = []
>>> while collections is not None:  # Loop over pages
...     collection_ids.extend(collections.collection_ids)
...     collections = collections.next  # Move to the next page

>>> collection_ids
[...]
>>> collection_id in collection_ids
True

Explore a collection:

>>> collection = client.get_collection(collection_id)

>>> collection.id == collection_id
True
>>> collection.title
'...'
>>> collection.description
'...'

>>> collection.published_at
datetime.datetime(...)
>>> collection.updated_at
datetime.datetime(...)

>>> collection.begin_datetime
datetime.datetime(...)
>>> collection.end_datetime
datetime.datetime(...)
>>> collection.bbox
(...)

>>> collection.submit(request)
Remote(...)

>>> collection.apply_constraints(request)
{...}

Interact with results:

>>> results = client.get_results(remote.request_id)

>>> results.content_length > 0
True
>>> results.content_type
'application/x-grib'
>>> results.location
'...'

>>> results.download("target_5.grib")
'target_5.grib'

List all successful jobs, sorted by newest first:

>>> jobs = client.get_jobs(sortby="-created", status="successful")

>>> request_ids = []
>>> while jobs is not None:  # Loop over pages
...     request_ids.extend(jobs.request_ids)
...     jobs = jobs.next  # Move to the next page

>>> request_ids
[...]
>>> remote.request_id in request_ids
True

Interact with a previously submitted job:

>>> remote = client.get_remote(remote.request_id)

>>> remote.collection_id == collection_id
True
>>> remote.request == request
True

>>> remote.status
'successful'
>>> remote.results_ready
True

>>> remote.created_at
datetime.datetime(...)
>>> remote.started_at
datetime.datetime(...)
>>> remote.finished_at
datetime.datetime(...)
>>> remote.updated_at == remote.finished_at
True

>>> remote.download("target_6.grib")
'target_6.grib'

>>> remote.get_results()
Results(...)

>>> remote.delete()
{...}

Apply constraints and find the number of available days in a given month:

>>> month = {"year": "2000", "month": "02"}
>>> constrained_request = client.apply_constraints(collection_id, month)

>>> len(constrained_request["day"])
29

Workflow for developers/contributors

For best experience create a new conda environment (e.g. DEVELOP) with Python 3.11:

conda create -n DEVELOP -c conda-forge python=3.11
conda activate DEVELOP

Before pushing to GitHub, run the following commands:

1. Update conda environment: make conda-env-update
2. Install this package: pip install -e .
3. Sync with the latest template (optional): make template-update
4. Run quality assurance checks: make qa
5. Run tests: make unit-tests
6. Run the static type checker: make type-check
7. Build the documentation (see Sphinx tutorial): make docs-build

License

Copyright 2022, European Union.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.