Python library for working with Spatiotemporal Asset Catalog (STAC).
Project description
STAC Client
A Python client for working with STAC Catalogs and APIs.
Installation
Install from PyPi. Other than PySTAC itself, the only dependencies for pystac-client is the Python requests and dateutil libraries.
$ pip install pystac-client
Documentation
See the documentation page for the latest docs.
Development
For development, clone the repository and use the standard Python method for installing the library as an "editable link", then install the development dependencies:
$ git clone https://github.com/stac-utils/pystac-client.git
$ cd pystac-client
$ pip install -e .
$ pip install -r requirements-dev.txt
To run just the tests
$ pytest -v -s --block-network --cov pystac_client --cov-report term-missing
The pystac-client tests use vcrpy to mock API calls
with "pre-recorded" API responses. When adding new tests use the @pytest.mark.vcr decorator
function to indicate vcrpy should be used. Record the new responses and commit them to the
repository.
$ pytest -v -s --record-mode new_episodes --block-network
$ git add <new files here>
$ git commit -a -m 'new test episodes'
To update pystac-client to use future versions of STAC API, the existing recorded API responsees should be "re-recorded":
$ pytest -v -s --record-mode rewrite --block-network
$ git commit -a -m 'updated test episodes'
Pull Requests
To make Pull Requests to pystac-client, the code must pass linting, formatting, and code tests. To run
the entire suit of checks and tests that will be run the GitHub Action Pipeline, use the test script.
$ scripts/test
If automatic formatting is desired (incorrect formatting will cause the GitHub Action to fail), use the format script and commit the resulting files:
$ scripts/format
$ git commit -a -m 'formatting updates'
To build the documentation, install the documentation requirements, then use the build-docs script:
$ pip install -r requirements-docs.txt
$ scripts/build-docs
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