Review test functions and group test cases

[bobleesj]

Problem

Originally posted by @sbillinge in #255 (comment)

The majority of code has been listing "cases", but we can further scale our tests by grouping them:

From:

@pytest.mark.parametrize(
    "org_do_args, target_do_args, scale_inputs, expected",
    [
        # Case 1: same x-array and y-array, check offset
        (
            {
                "xarray": np.array([10, 15, 25, 30, 60, 140]),
                "yarray": np.array([2, 3, 4, 5, 6, 7]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "xarray": np.array([10, 15, 25, 30, 60, 140]),
                "yarray": np.array([2, 3, 4, 5, 6, 7]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "q": None,
                "tth": 60,
                "d": None,
                "offset": 2.1,
            },
            {"xtype": "tth", "yarray": np.array([4.1, 5.1, 6.1, 7.1, 8.1, 9.1])},
        ),
        # Case 2: same length x-arrays with exact x-value match
        (
            {
                "xarray": np.array([10, 15, 25, 30, 60, 140]),
                "yarray": np.array([10, 20, 25, 30, 60, 100]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "xarray": np.array([10, 20, 25, 30, 60, 140]),
                "yarray": np.array([2, 3, 4, 5, 6, 7]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "q": None,
                "tth": 60,
                "d": None,
                "offset": 0,
            },
            {"xtype": "tth", "yarray": np.array([1, 2, 2.5, 3, 6, 10])},
        ),
        # Case 3: same length x-arrays with approximate x-value match
        (
            {
                "xarray": np.array([0.12, 0.24, 0.31, 0.4]),
                "yarray": np.array([10, 20, 40, 60]),
                "xtype": "q",
                "wavelength": 2 * np.pi,
            },
            {
                "xarray": np.array([0.14, 0.24, 0.31, 0.4]),
                "yarray": np.array([1, 3, 4, 5]),
                "xtype": "q",
                "wavelength": 2 * np.pi,
            },
            {
                "q": 0.1,
                "tth": None,
                "d": None,
                "offset": 0,
            },
            {"xtype": "q", "yarray": np.array([1, 2, 4, 6])},
        ),
        # Case 4: different x-array lengths with approximate x-value match
        (
            {
                "xarray": np.array([10, 25, 30.1, 40.2, 61, 120, 140]),
                "yarray": np.array([10, 20, 30, 40, 50, 60, 100]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "xarray": np.array([20, 25.5, 32, 45, 50, 62, 100, 125, 140]),
                "yarray": np.array([1.1, 2, 3, 3.5, 4, 5, 10, 12, 13]),
                "xtype": "tth",
                "wavelength": 2 * np.pi,
            },
            {
                "q": None,
                "tth": 60,
                "d": None,
                "offset": 0,
            },
            # Case 5 Scaling factor is calculated at index = 4 (tth=61) for self and index = 5 for target (tth=62)
            {"xtype": "tth", "yarray": np.array([1, 2, 3, 4, 5, 6, 10])},
        ),
    ],
)
...

To:

@pytest.mark.parametrize(
    "wavelength, q, expected_tth",
    [
        # Case 1: Allow empty arrays for q
        # 1. Empty q values, no wavelength, return empty arrays
        (None, np.empty((0)), np.empty((0))),
        # 2. Empty q values, wavelength specified, return empty arrays
        (4 * np.pi, np.empty((0)), np.empty(0)),
       
        # Case 2: Allow wavelength to be missing.
        # Valid q values, no wavelength, return index array
        (
            None,
            np.array([0, 0.2, 0.4, 0.6, 0.8, 1]),
            np.array([0, 1, 2, 3, 4, 5]),
        ),

        # Case 3: Correctly specified q and wavelength
        # Expected tth values are 2*arcsin(q) in degrees
        (4 * np.pi, np.array([0, 1 / np.sqrt(2), 1.0]), np.array([0, 90.0, 180.0])),
    ],
)

Proposed solution

For all test functions diffpy.utils, review each test function and group test cases.

[bobleesj]

I think we could close this issue once #273 for test_diffraction_objects.py and #275 for test_transform.py are merged?

@sbillinge

[sbillinge]

closed as completed