nabu.testutils module

nabu.testutils.generate_tests_scenarios(configurations)[source]

Generate “scenarios” of tests.

The parameter is a dictionary where:

  • the key is the name of a parameter

  • the value is a list of possible parameters

This function returns a list of dictionary where:

  • the key is the name of a parameter

  • the value is one value of this parameter

nabu.testutils.get_data(*dataset_path)[source]

Get a dataset file from silx.org/pub/nabu/data dataset_args is a list describing a nested folder structures, ex. [“path”, “to”, “my”, “dataset.h5”]

nabu.testutils.get_big_data(filename)[source]
nabu.testutils.uncompress_file(compressed_file_path, target_directory)[source]
nabu.testutils.get_file(fname)[source]
nabu.testutils.compare_arrays(arr1, arr2, tol, diff=None, absolute_value=True, percent=None, method='max', return_residual=False)[source]

Utility to compare two arrays.

Parameters:

  • arr1 (numpy.ndarray) – First array to compare

  • arr2 (numpy.ndarray) – Second array to compare

  • tol (float) – Tolerance indicating whether arrays are close to eachother.

  • diff (numpy.ndarray, optional) – Difference arr1 - arr2. If provided, this array is taken instead of arr1 and arr2.

  • absolute_value (bool, optional) – Whether to take absolute value of the difference.

  • percent (float) – If set, a “relative” comparison is performed instead of a subtraction: red(|arr1 - arr2|) / (red(|arr1|) * percent) < tol where “red” is the reduction method (mean, max or median).

  • method – Reduction method. Can be “max”, “mean”, or “median”.

Returns:

  • (is_close, residual) if return_residual is set to True

  • is_close otherwise

Examples

When using method=”mean” and absolute_value=True, this function computes the Mean Absolute Difference (MAD) metric. When also using percent=1.0, this computes the Relative Mean Absolute Difference (RMD) metric.

nabu.testutils.gaussian_apodization_window(shape, fwhm_ratio=0.7)[source]
nabu.testutils.compare_shifted_images(img1, img2, fwhm_ratio=0.7, return_upper_bound=False)[source]

Compare two images that are slightly shifted from one another. Typically, tomography reconstruction wight slightly different CoR. Each image is Fourier-transformed, and the modulus is taken to get rid of the shift between the images. An apodization is done to filter the high frequencies that are usually less relevant.

Parameters:

  • img1 (numpy.ndarray) – First image

  • img2 (numpy.ndarray) – Second image

  • fwhm_ratio (float, optional) – Ratio defining the apodization in the frequency domain. A small value (eg. 0.2) means that essentually only the low frequencies will be compared. A value of 1.0 means no apodization

  • return_upper_bound (bool, optional) – Whether to return a (coarse) upper bound of the comparison metric

Notes

This function roughly computes

|phi(F(img1)) - phi(F(img2))|

where F is the absolute value of the Fourier transform, and phi some shrinking function (here arcsinh).

class nabu.testutils.SimpleHDF5TomoScanMock(image_key)[source]

Bases: object

property image_key
save_reduced_flats(*args, **kwargs)[source]
save_reduced_darks(*args, **kwargs)[source]
class nabu.testutils.NXDatasetMock(data_volume, image_keys, rotation_angles=None, incident_energy=19.0, other_params=None)[source]

Bases: object

An alternative to tomoscan.esrf.mock.MockHDF5, with a different interface. Attributes are not supported !

generate_dict()[source]
generate_hdf5_file(fname, h5path=None)[source]
property dataset_hdf5_url
property flats
property darks
get_data_slices(what)[source]
nabu.testutils.generate_nx_dataset(out_fname, image_key, data_volume=None, rotation_angle=None)[source]