API Reference

data_regression

DataRegressionFixture.check(data_dict: dict[str, Any], basename: str | None = None, fullpath: PathLike[str] | None = None, round_digits: int | None = None) → None

Checks the given dict against a previously recorded version, or generate a new file.

Parameters:

data_dict – any yaml serializable dict.
basename – basename of the file to test/record. If not given the name of the test is used. Use either basename or fullpath.
fullpath – complete path to use as a reference file. This option will ignore lazy_datadir fixture when reading expected files but will still use it to write obtained files. Useful if a reference file is located in the session data dir for example.
round_digits – If given, round all floats in the dict to the given number of digits.

basename and fullpath are exclusive.

dataframe_regression

DataFrameRegressionFixture.check(data_frame: Any, basename: str | None = None, fullpath: PathLike[str] | None = None, tolerances: dict[str, dict[str, float]] | None = None, default_tolerance: dict[str, float] | None = None) → None

Checks a pandas dataframe, containing only numeric data, against a previously recorded version, or generate a new file.

Example:

data_frame = pandas.DataFrame.from_dict({
    'U_gas': U[0][positions],
    'U_liquid': U[1][positions],
    'gas_vol_frac [-]': vol_frac[0][positions],
    'liquid_vol_frac [-]': vol_frac[1][positions],
    'P': Pa_to_bar(P)[positions],
})
dataframe_regression.check(data_frame)

Parameters:

data_frame – pandas DataFrame containing data for regression check.
basename – basename of the file to test/record. If not given the name of the test is used.
fullpath – complete path to use as a reference file. This option will ignore embed_data completely, being useful if a reference file is located in the session data dir for example.
tolerances –
dict mapping keys from the data_frame to tolerance settings for the given data. Example:
```
tolerances={'U': dict(atol=1e-2)}
```
default_tolerance –
dict mapping the default tolerance for the current check call. Example:
```
default_tolerance=dict(atol=1e-7, rtol=1e-18).
```
If not provided, will use defaults from numpy’s isclose function.

basename and fullpath are exclusive.

file_regression

FileRegressionFixture.check(contents: str | bytes, encoding: str | None = None, extension: str = '.txt', newline: str | None = None, basename: str | None = None, fullpath: PathLike[str] | None = None, binary: bool = False, obtained_filename: PathLike[str] | None = None, check_fn: Callable[[Path, Path], None] | None = None) → None

Checks the contents against a previously recorded version, or generate a new file.

Parameters:

contents – content of the file to be verified as text or bytes.
encoding – Encoding used to write file, if any.
extension – Extension of file.
newline – See io.open docs.
binary – If the provided content is binary or text.
obtained_filename – ..see:: FileRegressionCheck
check_fn – a function with signature (obtained_filename, expected_filename) that should raise AssertionError if both files differ. If not given, use internal function which compares text using difflib.

num_regression

NumericRegressionFixture.check(data_dict: dict[str, Any], basename: str | None = None, fullpath: PathLike[str] | None = None, tolerances: dict[str, dict[str, float]] | None = None, default_tolerance: dict[str, float] | None = None, data_index: Sequence[int] | None = None, fill_different_shape_with_nan: bool = True) → None

Checks the given dict against a previously recorded version, or generate a new file. The dict must map from user-defined keys to 1d numpy arrays or array-like values.

Example:

num_regression.check({
    'U_gas': U[0][positions],
    'U_liquid': U[1][positions],
    'gas_vol_frac [-]': vol_frac[0][positions],
    'liquid_vol_frac [-]': vol_frac[1][positions],
    'P': Pa_to_bar(P)[positions],
})

Parameters:

data_dict – dict mapping keys to numpy arrays, or objects that can be coerced to 1d numpy arrays with a numeric dtype (e.g. list, tuple, etc).
basename – basename of the file to test/record. If not given the name of the test is used.
fullpath – complete path to use as a reference file. This option will ignore embed_data completely, being useful if a reference file is located in the session data dir for example.
tolerances –
dict mapping keys from the data_dict to tolerance settings for the given data. Example:
```
tolerances={'U': Tolerance(atol=1e-2)}
```
default_tolerance –
dict mapping the default tolerance for the current check call. Example:
```
default_tolerance=dict(atol=1e-7, rtol=1e-18).
```
If not provided, will use defaults from numpy’s isclose function.
data_index – If set, will override the indexes shown in the outputs. Default is panda’s default, which is range(0, len(data)).
fill_different_shape_with_nan – If set, all the data provided in the data_dict that has size lower than the bigger size will be filled with np.NaN, in order to save the data in a CSV file.

basename and fullpath are exclusive.

image_regression

ImageRegressionFixture.check(image_data: bytes, diff_threshold: float = 0.1, expect_equal: bool = True, basename: str | None = None, fullpath: PathLike[str] | None = None) → None

Checks that the given image contents are comparable with the ones stored in the data directory.

Parameters:

image_data – image data
basename – basename to store the information in the data directory. If none, use the name of the test function.
expect_equal – if the image should considered equal below of the given threshold. If False, the image should be considered different at least above the threshold.
diff_threshold – Tolerance as a percentage (1 to 100) on how the images are allowed to differ.
fullpath – complete path to use as a reference file. This option will ignore lazy_datadir fixture when reading expected files but will still use it to write obtained files. Useful if a reference file is located in the session data dir for example.

basename and fullpath are exclusive.

ndarrays_regression

NDArraysRegressionFixture.check(data_dict: dict[str, Any], basename: str | None = None, fullpath: PathLike[str] | None = None, tolerances: dict[str, dict[str, float]] | None = None, default_tolerance: dict[str, float] | None = None) → None

Checks a dictionary of NumPy ndarrays, containing only numeric data, against a previously recorded version, or generate a new file.

Example:

def test_some_data(ndarrays_regression):
    points, values = some_function()
    ndarrays_regression.check(
        {
            'points': points,  # array with shape (100, 3)
            'values': values,  # array with shape (100,)
        },
        default_tolerance=dict(atol=1e-8, rtol=1e-8)
    )

Parameters:

data_dict – dictionary of NumPy ndarrays containing data for regression check. The arrays can have any shape.
basename – basename of the file to test/record. If not given the name of the test is used.
fullpath – complete path to use as a reference file. This option will ignore embed_data completely, being useful if a reference file is located in the session data dir for example.
tolerances –
dict mapping keys from the data_dict to tolerance settings for the given data. Example:
```
tolerances={'U': Tolerance(atol=1e-2)}
```
default_tolerance –
dict mapping the default tolerance for the current check call. Example:
```
default_tolerance=dict(atol=1e-7, rtol=1e-18).
```
If not provided, will use defaults from numpy’s isclose function.

basename and fullpath are exclusive.