A Python client for working with STAC Catalogs and APIs.
Install from PyPi. Other than PySTAC itself, the only dependencies for pystac-client is the Python requests and dateutil libraries.
$ pip install pystac-clientSee the documentation page for the latest docs.
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.txtpre-commit is used to ensure a standard set of formatting and linting is run before every commit. These hooks should be installed with:
$ pre-commit installThese can then be run independent of a commit with:
$ pre-commit run --all-filesTo run just the tests:
$ pytest -v -s --block-network --cov pystac_client --cov-report term-missingThe 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 responses should be "re-recorded":
$ pytest -v -s --record-mode rewrite --block-network
$ git commit -a -m 'updated test episodes'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/testIf 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 Pandoc, install the
Python documentation requirements via pip, then use the build-docs script:
$ pip install -r requirements-docs.txt
$ scripts/build-docsBy default, pystac-client benchmarks are skipped during test runs.
To run the benchmarks, use the --benchmark-only flag:
$ pytest --benchmark-only
============================= test session starts ==============================
platform darwin -- Python 3.9.13, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /Users/gadomski/Code/pystac-client, configfile: pytest.ini
plugins: benchmark-3.4.1, recording-0.11.0, console-scripts-1.1.0, requests-mock-1.9.3, cov-2.11.1, typeguard-2.13.3
collected 75 items
tests/test_cli.py ss [ 2%]
tests/test_client.py ssssssssssssssss [ 24%]
tests/test_collection_client.py ss [ 26%]
tests/test_item_search.py ...sssssssssssssssssssssssssssssssssssssssssss [ 88%]
s [ 89%]
tests/test_stac_api_io.py ssssssss [100%]
--------------------------------------------------------------------------------------- benchmark: 3 tests --------------------------------------------------------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_single_item_search 213.4729 (1.0) 284.8732 (1.0) 254.9405 (1.0) 32.9424 (3.27) 271.0926 (1.0) 58.2907 (4.95) 1;0 3.9225 (1.0) 5 1
test_single_item 314.6746 (1.47) 679.7592 (2.39) 563.9692 (2.21) 142.7451 (14.18) 609.5605 (2.25) 93.9942 (7.98) 1;1 1.7731 (0.45) 5 1
test_requests 612.9212 (2.87) 640.5024 (2.25) 625.6871 (2.45) 10.0637 (1.0) 625.1143 (2.31) 11.7822 (1.0) 2;0 1.5982 (0.41) 5 1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
======================== 3 passed, 72 skipped in 11.86s ========================For more information on running and comparing benchmarks, see the pytest-benchmark documentation.
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