# Overview

`pytest-regressions` provides some fixtures that make it easy to maintain tests that generate lots of data or specific data files like images.

This plugin uses a data directory (courtesy of pytest-datadir) to store expected data files, which are stored and used as baseline for future test runs.

## Example

Let’s use `data_regression` as an example, but the workflow is the same for the other `*_regression` fixtures.

Suppose we have a `summary_grids` function which outputs a dictionary containing information about discrete grids for simulation. Of course your function would actually return some computed/read value, but here it is using an inline result for this example:

    def summary_grids():
        return {
            "Main Grid": {
                "id": 0,
                "cell_count": 1000,
                "active_cells": 300,
                "properties": [
                    {"name": "Temperature", "min": 75, "max": 85},
                    {"name": "Porosity", "min": 0.3, "max": 0.4},
                ],
            },
            "Refin1": {
                "id": 1,
                "cell_count": 48,
                "active_cells": 44,
                "properties": [
                    {"name": "Temperature", "min": 78, "max": 81},
                    {"name": "Porosity", "min": 0.36, "max": 0.39},
                ],
            },
        }

We could test the results of this function like this:

    def test_grids():
        data = summary_grids()
        assert data["Main Grid"]["id"] == 0
        assert data["Main Grid"]["cell_count"] == 1000
        assert data["Main Grid"]["active_cells"] == 300
        assert data["Main Grid"]["properties"] == [
            {"name": "Temperature", "min": 75, "max": 85},
            {"name": "Porosity", "min": 0.3, "max": 0.4},
        ]
        ...

But this presents a number of problems:

- Gets old quickly.

- Error-prone.

- If a check fails, we don’t know what else might be wrong with the obtained data.

- Does not scale for large data.

- Maintenance burden: if the data changes in the future (and it will) it will be a major headache to update the values, specially if there are a lot of similar tests like this one.

## Using data_regression

The `data_regression` fixture provides a method to check general dictionary data like the one in the previous example.

There is no need to import anything, just declare the `data_regression` fixture in your test’s arguments and call the `check` method in the test:

    def test_grids2(data_regression):
        data = summary_grids()
        data_regression.check(data)

The first time your run this test, it will fail with a message like this:

    >           pytest.fail(msg)
    E           Failed: File not found in data directory, created:
    E           - C:\Users\bruno\pytest-regressions\tests\test_grids\test_grids2.yml

The fixture will generate a `test_grids2.yml` file (same name as the test) in the data directory with the contents of the dictionary:

    Main Grid:
      active_cells: 300
      cell_count: 1000
      id: 0
      properties:
      - max: 85
        min: 75
        name: Temperature
      - max: 0.4
        min: 0.3
        name: Porosity
    Refin1:
      active_cells: 44
      cell_count: 48
      id: 1
      properties:
      - max: 81
        min: 78
        name: Temperature
      - max: 0.39
        min: 0.36
        name: Porosity

This file should be committed to version control.

The next time you run this test, it will compare the results of `summary_grids()` with the contents of the YAML file. If they match, the test passes. If they don’t match the test will fail, showing a nice diff of the text differences.

If the test fails because the new data is correct (the implementation might be returning more information about the grids for example), then you can use the `--force-regen` flag to update the expected file:

    $ pytest --force-regen

and commit the updated file.

This workflow makes it very simple to keep the files up to date and to check all the information we need.

## Parametrized tests

When using parametrized tests, pytest will give each parametrization of your test a unique name. This means that `pytest-regressions` will create a new file for each parametrization too.

Suppose we have an additional function `summary_grids_2` that generates longer data, we can re-use the same test with the `@pytest.mark.parametrize` decorator:

    @pytest.mark.parametrize('data', [summary_grids(), summary_grids_2()])
    def test_grids3(data_regression, data):
        data_regression.check(data)

Pytest will automatically name these as `test_grids3[data0]` and `test_grids3[data1]`, so files `test_grids3_data0.yml` and `test_grids3_data1.yml` will be created.

The names of these files can be controlled using the `ids` keyword for parametrize, so instead of `data0`, you can define more useful names such as `short` and `long`:

    @pytest.mark.parametrize('data', [summary_grids(), summary_grids_2()], ids=['short' 'long'])
    def test_grids3(data_regression, data):
        data_regression.check(data)

which creates `test_grids3_short.yml` and `test_grids3_long.yml` respectively.