class ImgDataUnitTest(unittest.TestCase): def setUp(self): self.app = QtGui.QApplication([]) self.img_model = ImgModel() self.img_model.load(os.path.join(data_path, 'image_001.tif')) def tearDown(self): del self.app del self.img_model gc.collect() def perform_transformations_tests(self): self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.rotate_img_m90() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.flip_img_horizontally() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.rotate_img_p90() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.flip_img_vertically() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.reset_img_transformations() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) def test_flipping_images(self): original_image = np.copy(self.img_model._img_data) self.img_model.flip_img_vertically() self.assertTrue(np.array_equal(self.img_model._img_data, np.flipud(original_image))) def test_simple_background_subtraction(self): self.first_image = np.copy(self.img_model.get_img_data()) self.img_model.load_next_file() self.second_image = np.copy(self.img_model.get_img_data()) self.img_model.load(os.path.join(data_path, 'image_001.tif')) self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.assertFalse(np.array_equal(self.first_image, self.img_model.get_img_data())) self.img_model.load_next_file() self.assertEqual(np.sum(self.img_model.get_img_data()), 0) def test_background_subtraction_with_supersampling(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.img_model.set_supersampling(2) self.img_model.get_img_data() self.img_model.set_supersampling(3) self.img_model.get_img_data() self.img_model.load_next_file() self.img_model.get_img_data() def test_background_subtraction_with_transformation(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) original_img = np.copy(self.img_model._img_data) original_background = np.copy(self.img_model._background_data) self.assertNotEqual(self.img_model._background_data, None) self.assertFalse(np.array_equal(self.img_model.img_data, self.img_model._img_data)) original_img_background_subtracted = np.copy(self.img_model.get_img_data()) self.assertTrue(np.array_equal(original_img_background_subtracted, original_img-original_background)) ### now comes the main process - flipping the image self.img_model.flip_img_vertically() flipped_img = np.copy(self.img_model._img_data) self.assertTrue(np.array_equal(np.flipud(original_img), flipped_img)) flipped_background = np.copy(self.img_model._background_data) self.assertTrue(np.array_equal(np.flipud(original_background), flipped_background)) flipped_img_background_subtracted = np.copy(self.img_model.get_img_data()) self.assertTrue(np.array_equal(flipped_img_background_subtracted, flipped_img-flipped_background)) self.assertTrue(np.array_equal(np.flipud(original_img_background_subtracted), flipped_img_background_subtracted)) self.assertEqual(np.sum(np.flipud(original_img_background_subtracted)-flipped_img_background_subtracted), 0) self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.perform_transformations_tests() def test_background_subtraction_with_supersampling_and_image_transformation(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.img_model.set_supersampling(2) self.assertEqual(self.img_model.get_img_data().shape, (4096, 4096)) self.perform_transformations_tests() self.img_model.set_supersampling(3) self.assertEqual(self.img_model.get_img_data().shape, (6144, 6144)) self.perform_transformations_tests() self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.assertEqual(self.img_model.get_img_data().shape, (6144, 6144)) self.perform_transformations_tests() def test_background_scaling_and_offset(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) #assure that everything is correct before self.assertTrue(np.array_equal(self.img_model.get_img_data(), self.img_model._img_data-self.img_model._background_data)) #set scaling and see difference self.img_model.set_background_scaling(2.4) self.assertTrue(np.array_equal(self.img_model.get_img_data(), self.img_model._img_data-2.4*self.img_model._background_data)) #set offset and see the difference self.img_model.set_background_scaling(1.0) self.img_model.set_background_offset(100.0) self.assertTrue(np.array_equal(self.img_model.img_data, self.img_model._img_data-(self.img_model._background_data+100.0))) #use offset and scaling combined self.img_model.set_background_scaling(2.3) self.img_model.set_background_offset(100.0) self.assertTrue(np.array_equal(self.img_model.img_data, self.img_model._img_data-(2.3*self.img_model._background_data+100))) def test_background_with_different_shape(self): self.img_model.load_background(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertEqual(self.img_model._background_data, None) self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.assertTrue(self.img_model._background_data is not None) self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertEqual(self.img_model._background_data, None) def test_absorption_correction_with_supersampling(self): original_image = np.copy(self.img_model.get_img_data()) dummy_correction = DummyCorrection(self.img_model.get_img_data().shape, 0.6) self.img_model.add_img_correction(dummy_correction, "Dummy 1") self.assertAlmostEqual(np.sum(original_image)/0.6, np.sum(self.img_model.get_img_data()), places=4) self.img_model.set_supersampling(2) self.img_model.get_img_data() def test_absorption_correction_with_different_image_sizes(self): dummy_correction = DummyCorrection(self.img_model.get_img_data().shape, 0.4) # self.img_data.set_absorption_correction(np.ones(self.img_data._img_data.shape)*0.4) self.img_model.add_img_correction(dummy_correction, "Dummy 1") self.assertTrue(self.img_model._img_corrections.has_items()) self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertFalse(self.img_model.has_corrections()) def test_adding_several_absorption_corrections(self): original_image = np.copy(self.img_model.get_img_data()) img_shape = original_image.shape self.img_model.add_img_correction(DummyCorrection(img_shape, 0.4)) self.img_model.add_img_correction(DummyCorrection(img_shape, 3)) self.img_model.add_img_correction(DummyCorrection(img_shape, 5)) self.assertTrue(np.sum(original_image)/(0.5*3*5), np.sum(self.img_model.get_img_data())) self.img_model.delete_img_correction(1) self.assertTrue(np.sum(original_image)/(0.5*5), np.sum(self.img_model.get_img_data())) def test_saving_data(self): self.img_model.load(os.path.join(data_path, 'image_001.tif')) filename = os.path.join(data_path, 'test.tif') self.img_model.save(filename) first_img_array = np.copy(self.img_model._img_data) self.img_model.load(filename) self.assertTrue(np.array_equal(first_img_array, self.img_model._img_data)) self.assertTrue(os.path.exists(filename)) os.remove(filename) def test_rotation(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) self.img_model.reset_img_transformations() pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) self.img_model.reset_img_transformations() pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_horizontally() self.img_model.flip_img_horizontally() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) self.img_model.reset_img_transformations() pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.flip_img_vertically() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) self.img_model.reset_img_transformations() self.img_model.flip_img_vertically() self.img_model.flip_img_horizontally() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.flip_img_horizontally() transformed_data = self.img_model.get_img_data() self.img_model.load(os.path.join(data_path, 'image_001.tif')) self.assertTrue(np.array_equal(self.img_model.get_img_data(), transformed_data)) self.img_model.reset_img_transformations() pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_p90() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_horizontally() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.flip_img_horizontally() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.flip_img_horizontally() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data))
class CbnAbsorptionCorrectionOptimizationTest(unittest.TestCase): def setUp(self): # creating Data objects self.img_data = ImgModel() self.img_data.load("Data/CbnCorrectionOptimization/Mg2SiO4_091.tif") self.calibration_data = CalibrationModel(self.img_data) self.calibration_data.load("Data/CbnCorrectionOptimization/LaB6_40keV side.poni") self.mask_data = MaskModel() self.mask_data.load_mask("Data/CbnCorrectionOptimization/Mg2SiO4_91_combined.mask") # creating the ObliqueAngleDetectorAbsorptionCorrection _, fit2d_parameter = self.calibration_data.get_calibration_parameter() detector_tilt = fit2d_parameter['tilt'] detector_tilt_rotation = fit2d_parameter['tiltPlanRotation'] self.tth_array = self.calibration_data.spectrum_geometry.twoThetaArray((2048, 2048)) self.azi_array = self.calibration_data.spectrum_geometry.chiArray((2048, 2048)) self.oiadac_correction = ObliqueAngleDetectorAbsorptionCorrection( self.tth_array, self.azi_array, detector_thickness=40, absorption_length=465.5, tilt=detector_tilt, rotation=detector_tilt_rotation, ) self.img_data.add_img_correction(self.oiadac_correction, "oiadac") def tearDown(self): del self.calibration_data.cake_geometry del self.calibration_data.spectrum_geometry def test_the_world(self): params = Parameters() params.add("diamond_thickness", value=2, min=1.9, max=2.3) params.add("seat_thickness", value=5.3, min=4.0, max=6.6, vary=False) params.add("small_cbn_seat_radius", value=0.2, min=0.10, max=0.5, vary=True) params.add("large_cbn_seat_radius", value=1.95, min=1.85, max=2.05, vary=False) params.add("tilt", value=3.3, min=0, max=8) params.add("tilt_rotation", value=0, min=-15, max=+15) params.add("cbn_abs_length", value=14.05, min=12, max=16) region = [8, 26] self.tth_array = 180.0 / np.pi * self.tth_array self.azi_array = 180.0 / np.pi * self.azi_array def fcn2min(params): cbn_correction = CbnCorrection( tth_array=self.tth_array, azi_array=self.azi_array, diamond_thickness=params['diamond_thickness'].value, seat_thickness=params['seat_thickness'].value, small_cbn_seat_radius=params['small_cbn_seat_radius'].value, large_cbn_seat_radius=params['large_cbn_seat_radius'].value, tilt=params['tilt'].value, tilt_rotation=params['tilt_rotation'].value, cbn_abs_length=params["cbn_abs_length"].value ) self.img_data.add_img_correction(cbn_correction, "cbn") tth, int = self.calibration_data.integrate_1d(mask=self.mask_data.get_mask()) self.img_data.delete_img_correction("cbn") ind = np.where((tth > region[0]) & (tth < region[1])) int = gaussian_filter1d(int, 20) return (np.diff(int[ind])) ** 2 def output_values(param1, iteration, residual): report_fit(param1) result = minimize(fcn2min, params, iter_cb=output_values) report_fit(params) # plotting result: cbn_correction = CbnCorrection( tth_array=self.tth_array, azi_array=self.azi_array, diamond_thickness=params['diamond_thickness'].value, seat_thickness=params['seat_thickness'].value, small_cbn_seat_radius=params['small_cbn_seat_radius'].value, large_cbn_seat_radius=params['large_cbn_seat_radius'].value, tilt=params['tilt'].value, tilt_rotation=params['tilt_rotation'].value, cbn_abs_length=params['cbn_abs_length'].value ) self.img_data.add_img_correction(cbn_correction, "cbn") tth, int = self.calibration_data.integrate_1d(mask=self.mask_data.get_mask()) ind = np.where((tth > region[0]) & (tth < region[1])) tth = tth[ind] int = int[ind] int_smooth = gaussian_filter1d(int, 10) int_diff1 = np.diff(int) int_diff1_smooth = np.diff(int_smooth) int_diff2 = np.diff(int_diff1) int_diff2_smooth = np.diff(int_diff1_smooth) plt.figure() plt.subplot(3, 1, 1) plt.plot(tth, int) plt.plot(tth, int_smooth) plt.subplot(3, 1, 2) plt.plot(int_diff1) plt.plot(int_diff1_smooth) plt.subplot(3, 1, 3) plt.plot(int_diff2) plt.plot(int_diff2_smooth) plt.savefig("Results/optimize_cbn_absorption.png", dpi=300) os.system("open " + "Results/optimize_cbn_absorption.png")
class ImgModelTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.app = QtGui.QApplication([]) @classmethod def tearDownClass(cls): cls.app.quit() def setUp(self): self.img_model = ImgModel() self.img_model.load(os.path.join(data_path, 'image_001.tif')) def tearDown(self): del self.img_model def perform_transformations_tests(self): self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.rotate_img_m90() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.flip_img_horizontally() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.rotate_img_p90() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.flip_img_vertically() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) self.img_model.reset_img_transformations() self.assertEqual(np.sum(np.absolute(self.img_model.get_img_data())), 0) def test_load_emits_signal(self): callback_fcn = MagicMock() self.img_model.img_changed.connect(callback_fcn) self.img_model.load(os.path.join(data_path, 'image_001.tif')) callback_fcn.assert_called_once_with() def test_flipping_images(self): original_image = np.copy(self.img_model._img_data) self.img_model.flip_img_vertically() self.assertTrue(np.array_equal(self.img_model._img_data, np.flipud(original_image))) def test_simple_background_subtraction(self): self.first_image = np.copy(self.img_model.get_img_data()) self.img_model.load_next_file() self.second_image = np.copy(self.img_model.get_img_data()) self.img_model.load(os.path.join(data_path, 'image_001.tif')) self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.assertFalse(np.array_equal(self.first_image, self.img_model.get_img_data())) self.img_model.load_next_file() self.assertEqual(np.sum(self.img_model.get_img_data()), 0) def test_background_subtraction_with_supersampling(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.img_model.set_supersampling(2) self.img_model.get_img_data() self.img_model.set_supersampling(3) self.img_model.get_img_data() self.img_model.load_next_file() self.img_model.get_img_data() def test_background_subtraction_with_transformation(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) original_img = np.copy(self.img_model._img_data) original_background = np.copy(self.img_model._background_data) self.assertNotEqual(self.img_model._background_data, None) self.assertFalse(np.array_equal(self.img_model.img_data, self.img_model._img_data)) original_img_background_subtracted = np.copy(self.img_model.get_img_data()) self.assertTrue(np.array_equal(original_img_background_subtracted, original_img - original_background)) ### now comes the main process - flipping the image self.img_model.flip_img_vertically() flipped_img = np.copy(self.img_model._img_data) self.assertTrue(np.array_equal(np.flipud(original_img), flipped_img)) flipped_background = np.copy(self.img_model._background_data) self.assertTrue(np.array_equal(np.flipud(original_background), flipped_background)) flipped_img_background_subtracted = np.copy(self.img_model.get_img_data()) self.assertTrue(np.array_equal(flipped_img_background_subtracted, flipped_img - flipped_background)) self.assertTrue(np.array_equal(np.flipud(original_img_background_subtracted), flipped_img_background_subtracted)) self.assertEqual(np.sum(np.flipud(original_img_background_subtracted) - flipped_img_background_subtracted), 0) self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.perform_transformations_tests() def test_background_subtraction_with_supersampling_and_image_transformation(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.img_model.set_supersampling(2) self.assertEqual(self.img_model.get_img_data().shape, (4096, 4096)) self.perform_transformations_tests() self.img_model.set_supersampling(3) self.assertEqual(self.img_model.get_img_data().shape, (6144, 6144)) self.perform_transformations_tests() self.img_model.load(os.path.join(data_path, 'image_002.tif')) self.assertEqual(self.img_model.get_img_data().shape, (6144, 6144)) self.perform_transformations_tests() def test_background_scaling_and_offset(self): self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) # assure that everything is correct before self.assertTrue(np.array_equal(self.img_model.get_img_data(), self.img_model._img_data - self.img_model._background_data)) # set scaling and see difference self.img_model.set_background_scaling(2.4) self.assertTrue(np.array_equal(self.img_model.get_img_data(), self.img_model._img_data - 2.4 * self.img_model._background_data)) # set offset and see the difference self.img_model.set_background_scaling(1.0) self.img_model.set_background_offset(100.0) self.assertTrue(np.array_equal(self.img_model.img_data, self.img_model._img_data - (self.img_model._background_data + 100.0))) # use offset and scaling combined self.img_model.set_background_scaling(2.3) self.img_model.set_background_offset(100.0) self.assertTrue(np.array_equal(self.img_model.img_data, self.img_model._img_data - (2.3 * self.img_model._background_data + 100))) def test_background_with_different_shape(self): self.img_model.load_background(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertEqual(self.img_model._background_data, None) self.img_model.load_background(os.path.join(data_path, 'image_002.tif')) self.assertTrue(self.img_model._background_data is not None) self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertEqual(self.img_model._background_data, None) def test_absorption_correction_with_supersampling(self): original_image = np.copy(self.img_model._img_data) dummy_correction = DummyCorrection(self.img_model.get_img_data().shape, 0.6) self.img_model.add_img_correction(dummy_correction, "Dummy 1") self.assertAlmostEqual(np.sum(original_image / 0.6), np.sum(self.img_model.get_img_data()), places=4) self.img_model.set_supersampling(2) self.img_model.get_img_data() def test_absorption_correction_with_different_image_sizes(self): dummy_correction = DummyCorrection(self.img_model.get_img_data().shape, 0.4) # self.img_data.set_absorption_correction(np.ones(self.img_data._img_data.shape)*0.4) self.img_model.add_img_correction(dummy_correction, "Dummy 1") self.assertTrue(self.img_model._img_corrections.has_items()) self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif')) self.assertFalse(self.img_model.has_corrections()) def test_adding_several_absorption_corrections(self): original_image = np.copy(self.img_model.get_img_data()) img_shape = original_image.shape self.img_model.add_img_correction(DummyCorrection(img_shape, 0.4)) self.img_model.add_img_correction(DummyCorrection(img_shape, 3)) self.img_model.add_img_correction(DummyCorrection(img_shape, 5)) self.assertTrue(np.sum(original_image) / (0.5 * 3 * 5), np.sum(self.img_model.get_img_data())) self.img_model.delete_img_correction(1) self.assertTrue(np.sum(original_image) / (0.5 * 5), np.sum(self.img_model.get_img_data())) def test_saving_data(self): self.img_model.load(os.path.join(data_path, 'image_001.tif')) filename = os.path.join(data_path, 'test.tif') self.img_model.save(filename) first_img_array = np.copy(self.img_model._img_data) self.img_model.load(filename) self.assertTrue(np.array_equal(first_img_array, self.img_model._img_data)) self.assertTrue(os.path.exists(filename)) os.remove(filename) def test_negative_rotation(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) def test_combined_rotation(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) def test_flip_img_horizontally(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_horizontally() self.img_model.flip_img_horizontally() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) def test_flip_img_vertically(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.flip_img_vertically() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) def test_combined_rotation_and_flipping(self): self.img_model.flip_img_vertically() self.img_model.flip_img_horizontally() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.flip_img_horizontally() transformed_data = self.img_model.get_img_data() self.img_model.load(os.path.join(data_path, 'image_001.tif')) self.assertTrue(np.array_equal(self.img_model.get_img_data(), transformed_data)) def test_reset_img_transformation(self): pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_m90() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.rotate_img_p90() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_horizontally() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) pre_transformed_data = self.img_model.get_img_data() self.img_model.flip_img_vertically() self.img_model.flip_img_horizontally() self.img_model.rotate_img_m90() self.img_model.rotate_img_p90() self.img_model.rotate_img_m90() self.img_model.rotate_img_m90() self.img_model.flip_img_horizontally() self.img_model.reset_img_transformations() self.assertTrue(np.array_equal(self.img_model.get_img_data(), pre_transformed_data)) def test_loading_a_tagged_tif_file_and_retrieving_info_string(self): self.img_model.load(os.path.join(data_path, "attrib.tif")) self.assertIn("areaDetector", self.img_model.file_info) def test_loading_spe_file(self): self.img_model.load(os.path.join(spe_path, 'CeO2_PI_CCD_Mo.SPE')) self.assertEqual(self.img_model.img_data.shape, (1042, 1042))