pytest-datarecorder 2.0.0

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/