pystac-client 0.4.0

Python library for working with Spatiotemporal Asset Catalog (STAC).

STAC Client

A Python client for working with STAC Catalogs and APIs.

Table of Contents

• Installation
• Documentation
• Development
  • Pull Requests

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

pre-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 install

These can then be run independent of a commit with:

$ pre-commit run --all-files

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 Pandoc, install the Python documentation requirements via pip, then use the build-docs script:

$ pip install -r requirements-docs.txt
$ scripts/build-docs

Benchmark

By 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.