API Reference¶

data_regression¶

DataRegressionFixture.check(data_dict: Dict[str, Any], basename: Optional[str, None] = None, fullpath: Optional[os.PathLike[str], 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 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.

dataframe_regression¶

DataFrameRegressionFixture.check(data_frame: Any, basename: Optional[str, None] = None, fullpath: Optional[os.PathLike[str], None] = None, tolerances: Optional[Dict[str, Dict[str, float]], None] = None, default_tolerance: Optional[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: Union[str, bytes], encoding: Optional[str, None] = None, extension: str = '.txt', newline: Optional[str, None] = None, basename: Optional[str, None] = None, fullpath: Optional[os.PathLike[str], None] = None, binary: bool = False, obtained_filename: Optional[os.PathLike[str], None] = None, check_fn: Optional[Callable[[pathlib.Path, pathlib.Path], None], None] = None) → None¶

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

Parameters:

contents – content to be verified.
encoding – Encoding used to write file, if any.
extension – Extension of file.
newline – See io.open docs.
binary – If the file 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: Optional[str, None] = None, fullpath: Optional[os.PathLike[str], None] = None, tolerances: Optional[Dict[str, Dict[str, float]], None] = None, default_tolerance: Optional[Dict[str, float], None] = None, data_index: Optional[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: Optional[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.

ndarrays_regression¶

NDArraysRegressionFixture.check(data_dict: Dict[str, Any], basename: Optional[str, None] = None, fullpath: Optional[os.PathLike[str], None] = None, tolerances: Optional[Dict[str, Dict[str, float]], None] = None, default_tolerance: Optional[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.