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        "author": "Wibowo Arindrarto",
        "author_email": "bow@bow.web.id",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Testing",
            "Topic :: Utilities"
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        "description": "pytest-pipeline\n===============\n\n|ci| |coverage| |pypi|\n\n.. |ci| image:: https://travis-ci.org/bow/pytest-pipeline.png?branch=master\n        :target: https://travis-ci.org/bow/pytest-pipeline\n\n.. |coverage| image:: https://codecov.io/gh/bow/pytest-pipeline/branch/master/graph/badge.svg\n        :target: https://codecov.io/gh/bow/pytest-pipeline\n\n.. |pypi| image:: https://badge.fury.io/py/pytest-pipeline.svg\n        :target: http://badge.fury.io/py/pytest-pipeline\n\n\npytest-pipeline is a pytest plugin for functional testing of data analysis\npipelines. They are usually long-running scripts or executables with multiple\ninput and/or output files + directories. The plugin is meant for end-to-end\ntesting where you test for conditions before the pipeline run and after the\npipeline runs (output files, checksums, etc.).\n\npytest-pipeline is tested against Python versions 2.7, 3.3, 3.4, 3.5, and 3.6.\n\n\nInstallation\n============\n\n::\n\n    pip install pytest-pipeline\n\n\nWalkthrough\n===========\n\nFor our example, we will use a super simple pipeline that writes a file and\nprints to stdout:\n\n.. code-block:: python\n\n    #!/usr/bin/env python\n\n    from __future__ import print_function\n\n    if __name__ == \"__main__\":\n\n        with open(\"result.txt\", \"w\") as result:\n            result.write(\"42\\n\")\n        print(\"Result computed\")\n\nAt this point it's just a simple script, but it should be enough to illustrate\nthe plugin. Also, if you want to follow along, save the above file as\n``run_pipeline``.\n\nWith the pipeline above, here's how your test would look like with\n``pytest_pipeline``:\n\n.. code-block:: python\n\n    import os\n    import shutil\n    import unittest\n    from pytest_pipeline import PipelineRun, mark, utils\n\n    # we can subclass `PipelineRun` to add custom methods\n    # using `PipelineRun` as-is is also possible\n    class MyRun(PipelineRun):\n\n        # before_run-marked functions will be run before the pipeline is executed\n        @mark.before_run\n        def test_prep_executable(self):\n            # copy the executable to the run directory\n            shutil.copy2(\"/path/to/run_pipeline\", \"run_pipeline\")\n            # ensure that the file is executable\n            assert os.access(\"run_pipeline\", os.X_OK)\n\n    # a pipeline run is treated as a test fixture\n    run = MyRun.class_fixture(cmd=\"./run_pipeline\", stdout=\"run.stdout\")\n\n    # the fixture is bound to a unittest.TestCase using the usefixtures mark\n    @pytest.mark.usefixtures(\"run\")\n    # tests per-pipeline run are grouped in one unittest.TestCase instance\n    class TestMyPipeline(unittest.TestCase):\n\n        def test_result_md5(self):\n            assert utils.file_md5sum(\"result.txt\") == \"50a2fabfdd276f573ff97ace8b11c5f4\"\n\n        def test_exit_code(self):\n            # the run fixture is stored as the `run_fixture` attribute\n            assert self.run_fixture.exit_code == 0\n\n        # we can check the stdout that we capture as well\n        def test_stdout(self):\n            assert open(\"run.stdout\", \"r\").read().strip() == \"Result computed\"\n\nIf the test above is saved as ``test_demo.py``, you can then run the test by\nexecuting ``py.test -v test_demo.py``. You should see that three tests were\nexecuted and all four passed.\n\nWhat just happened?\n-------------------\n\nYou just executed your first pipeline test. The plugin itself gives you:\n\n- Test directory creation (one class gets one directory).\n  By default, testdirectories are all created in the ``/tmp/pipeline_test``\n  directory. You can tweak this location by supplying the\n  ``--base-pipeline-dir`` command line flag.\n\n- Automatic execution of the pipeline.\n  No need to ``import subprocess``, just define the command via the\n  ``PipelineRun`` object. We optionally captured the standard output to a file\n  called ``run.stdout``. Not a fan of doing disk IO? You can also set ``stdout``\n  and/or ``stderr`` to ``True`` and have their values captured in-memory.\n\n- Timeout control.\n  For long running pipelines, you can also supply a ``timeout`` argument which\n  limits how long the pipeline process can run.\n\nAnd since this is a py.test plugin, test discovery and execution is done via\npy.test.\n\n\nGetting + giving help\n=====================\n\nPlease use the `issue tracker <https://github.com/bow/pytest-pipeline/issues>`_\nto report bugs or feature requests. You can always fork and submit a pull\nrequest as well.\n\n\nLicense\n=======\n\nSee LICENSE.\n\n\n\n\nChangelog\n=========\n\nVersion 0.3\n-----------\n\nRelease 0.3.0\n^^^^^^^^^^^^^\n\nRelease date: 24 January 2017\n\n* Allow stdout and/or stderr capture in-memory. This can be done by\n  setting their respective keyword arguments to ``True`` when creating\n  the run fixture.\n\n\nVersion 0.2\n-----------\n\nRelease 0.2.0\n^^^^^^^^^^^^^\n\nRelease date: 31 March 2015\n\n* Pipeline runs are now modelled differently. Instead of a class attribute,\n  they are now created as pytest fixtures. This allows the pipeline runs\n  to be used in non-`unittest.TestCase` tests.\n\n* The `after_run` decorator is deprecated.\n\n* The command line flags `--xfail-pipeline` and `--skip-run` are deprecated.\n\n\nVersion 0.1\n-----------\n\nRelease 0.1.0\n^^^^^^^^^^^^^\n\nRelease date: 25 August 2014\n\n* First release on PyPI.\n",
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