===================
pytest-datarecorder
===================
|PyPi| |Waf Python Tests| |Black| |Flake8| |Pip Install|
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Testing code that generates output can be tedious to maintain
``pytest-datarecorder`` aims to simplify this task.
.. contents:: Table of Contents:
:local:
Installation
============
To install pytest-datarecorder::
pip install pytest-datarecorder
Usage
=====
To make it easy to use in with py.test the DataRecorder object can be
injected into a test function by using the datarecorder fixture.
Example::
def test_this_function(datarecorder):
datarecorder.record_data(
data={'a':1, 'b':2}, recording_file="test/data/recording.json")
The ``data`` passed will be serialized to JSON since the recording file
has a ``.json`` extension. If ``data`` changes, we will get an exception
containing a diff with what changed. If we want to accept the changes
we simply delete the recording and run the code again.
Relase new version
==================
1. Edit NEWS.rst and wscript (set correct VERSION)
2. Run ::
./waf upload
Source code
===========
The main functionality is found in ``src/datarecorder.py`` and the
corresponding unit test is in ``test/test_datarecorder.py`` if you
want to play/modify/fix the code this would, in most cases, be the place
to start.
Developer Notes
===============
We try to make our projects as independent as possible of a local system setup.
For example with our native code (C/C++) we compile as much as possible from
source, since this makes us independent of what is currently installed
(libraries etc.) on a specific machine.
To "fetch" sources we use Waf (https://waf.io/) augmented with dependency
resolution capabilities: https://github.com/steinwurf/waf
The goal is to enable a work-flow where running::
./waf configure
./waf build --run_tests
Configures, builds and runs any available tests for a given project, such that
you as a developer can start hacking at the code.
For Python project this is a bit unconventional, but we think it works well.
Tests
=====
The tests will run automatically by passing ``--run_tests`` to waf::
./waf --run_tests
This follows what seems to be "best practice" advise, namely to install the
package in editable mode in a virtualenv.
Notes
=====
* Why use an ``src`` folder (https://hynek.me/articles/testing-packaging/).
tl;dr you should run your tests in the same environment as your users would
run your code. So by placing the source files in a non-importable folder you
avoid accidentally having access to resources not added to the Python
package your users will install...
* Python packaging guide: https://packaging.python.org/distributing/
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"description": "===================\npytest-datarecorder\n===================\n\n|PyPi| |Waf Python Tests| |Black| |Flake8| |Pip Install|\n\n.. |PyPi| image:: https://badge.fury.io/py/pytest-datarecorder.svg\n :target: https://badge.fury.io/py/pytest-datarecorder\n\n.. |Waf Python Tests| image:: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/python-waf.yml/badge.svg\n :target: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/python-waf.yml\n\n.. |Flake8| image:: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/flake.yml/badge.svg\n :target: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/flake.yml\n\n.. |Black| image:: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/black.yml/badge.svg\n :target: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/black.yml\n\n.. |Pip Install| image:: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/pip.yml/badge.svg\n :target: https://github.com/steinwurf/pytest-datarecorder/actions/workflows/pip.yml\n\nTesting code that generates output can be tedious to maintain\n ``pytest-datarecorder`` aims to simplify this task.\n\n.. contents:: Table of Contents:\n :local:\n\nInstallation\n============\n\nTo install pytest-datarecorder::\n\n pip install pytest-datarecorder\n\nUsage\n=====\n\nTo make it easy to use in with py.test the DataRecorder object can be\ninjected into a test function by using the datarecorder fixture.\n\nExample::\n\n def test_this_function(datarecorder):\n\n datarecorder.record_data(\n data={'a':1, 'b':2}, recording_file=\"test/data/recording.json\")\n\nThe ``data`` passed will be serialized to JSON since the recording file\nhas a ``.json`` extension. If ``data`` changes, we will get an exception\ncontaining a diff with what changed. If we want to accept the changes\nwe simply delete the recording and run the code again.\n\nRelase new version\n==================\n\n1. Edit NEWS.rst and wscript (set correct VERSION)\n2. Run ::\n\n ./waf upload\n\nSource code\n===========\n\nThe main functionality is found in ``src/datarecorder.py`` and the\ncorresponding unit test is in ``test/test_datarecorder.py`` if you\nwant to play/modify/fix the code this would, in most cases, be the place\nto start.\n\nDeveloper Notes\n===============\n\nWe try to make our projects as independent as possible of a local system setup.\nFor example with our native code (C/C++) we compile as much as possible from\nsource, since this makes us independent of what is currently installed\n(libraries etc.) on a specific machine.\n\nTo \"fetch\" sources we use Waf (https://waf.io/) augmented with dependency\nresolution capabilities: https://github.com/steinwurf/waf\n\nThe goal is to enable a work-flow where running::\n\n ./waf configure\n ./waf build --run_tests\n\nConfigures, builds and runs any available tests for a given project, such that\nyou as a developer can start hacking at the code.\n\nFor Python project this is a bit unconventional, but we think it works well.\n\nTests\n=====\n\nThe tests will run automatically by passing ``--run_tests`` to waf::\n\n ./waf --run_tests\n\nThis follows what seems to be \"best practice\" advise, namely to install the\npackage in editable mode in a virtualenv.\n\nNotes\n=====\n\n* Why use an ``src`` folder (https://hynek.me/articles/testing-packaging/).\n tl;dr you should run your tests in the same environment as your users would\n run your code. So by placing the source files in a non-importable folder you\n avoid accidentally having access to resources not added to the Python\n package your users will install...\n* Python packaging guide: https://packaging.python.org/distributing/\n",
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