Source code for mantidimaging.core.operations.rescale.rescale_test

# Copyright (C) 2022 ISIS Rutherford Appleton Laboratory UKRI
# SPDX - License - Identifier: GPL-3.0-or-later

import math
import numpy as np
import pytest
from numpy import testing as npt, copy

import mantidimaging.test_helpers.unit_test_helper as th
from mantidimaging.core.operations.rescale import RescaleFilter
from mantidimaging.test_helpers.qt_mocks import MockQSpinBox, MockQComboBox


[docs] @pytest.mark.parametrize('value', [255.0, 65535.0, 2147483647.0]) def test_rescale(value): images = th.generate_images((10, 100, 100)) images.data[0:3] = -100 images.data[3:6] = 0.5 images.data[6:10] = 1.0 expected_min_input = 0.0 images = RescaleFilter.filter_func(images, min_input=expected_min_input, max_input=images.data.max(), max_output=value) # below min_input has been clipped to 0 npt.assert_equal(0, images.data[0:3]) npt.assert_equal(images.data[3:6], value / 2) npt.assert_equal(images.data[6:10], value)
[docs] def test_execute_wrapper_no_preset(): min_input_value = 12 max_input_value = 34 min_input = MockQSpinBox(min_input_value) max_input = MockQSpinBox(max_input_value) max_output = MockQSpinBox(420.0) preset = MockQComboBox('None') partial = RescaleFilter.execute_wrapper(min_input, max_input, max_output, preset) assert partial.keywords['min_input'] == min_input_value assert partial.keywords['max_input'] == max_input_value assert partial.keywords['max_output'] == 420.0
[docs] @pytest.mark.parametrize('type, expected_max', [ ('int8', 255.0), ('int16', 65535), ('int32', 2147483647.0), ]) def test_execute_wrapper_with_preset(type: str, expected_max: float): min_input_value = 12 max_input_value = 34 min_input = MockQSpinBox(min_input_value) max_input = MockQSpinBox(max_input_value) max_output = MockQSpinBox(420.0) # this value is overridden by preset preset = MockQComboBox(type) partial = RescaleFilter.execute_wrapper(min_input, max_input, max_output, preset) # type: ignore assert partial.keywords['min_input'] == min_input_value assert partial.keywords['max_input'] == max_input_value assert partial.keywords['max_output'] == expected_max
[docs] def test_scale_single_image(): images = th.generate_images((2, 100, 100)) images.data[0:2] = np.arange(-1, 1, step=0.0002).reshape(100, 100) scaled_image = RescaleFilter.filter_array(copy(images.data[0]), min_input=images.data[0].min(), max_input=images.data[0].max(), max_output=65535) assert scaled_image.min() == 0 assert scaled_image.max() == 65535
[docs] def test_scale_single_image_bad_offset(): images = th.generate_images((2, 100, 100)) try: RescaleFilter.filter_array(copy(images.data[0]), min_input=-5000, max_input=5000, max_output=65535) except ValueError: pass except Exception as e: assert False, f"Unexpected exception was triggered: {e}"
[docs] @pytest.mark.parametrize('value', [255.0, 65535.0, 2147483647.0]) def test_rescale_ignores_nans(value): images = th.generate_images((10, 100, 100)) images.data[0:3] = -100.0 images.data[3:5] = 0.5 images.data[6][0:10] = np.nan images.data[7:10] = 1.0 expected_min_input = 0.0 images = RescaleFilter.filter_func(images, min_input=expected_min_input, max_input=np.nanmax(images.data), max_output=value) # below min_input has been clipped to 0 npt.assert_equal(0, images.data[0:3]) npt.assert_equal(images.data[3:5], value / 2) npt.assert_equal(images.data[7:10], value) assert all([math.isnan(x) for x in images.data[6][0:10].flatten()])
if __name__ == "__main__": import pytest pytest.main([__file__])