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 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 CalibrationModelTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.app = QtGui.QApplication([])
@classmethod
def tearDownClass(cls):
cls.app.quit()
cls.app.deleteLater()
def setUp(self):
self.img_model = ImgModel()
self.calibration_model = CalibrationModel(self.img_model)
def tearDown(self):
del self.img_model
if hasattr(self.calibration_model, 'cake_geometry'):
del self.calibration_model.cake_geometry
del self.calibration_model.spectrum_geometry
del self.calibration_model
gc.collect()
def test_loading_calibration_gives_right_pixel_size(self):
self.calibration_model.spectrum_geometry.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.assertEqual(self.calibration_model.spectrum_geometry.pixel1, 0.000172)
self.calibration_model.load(os.path.join(data_path, 'LaB6_40keV_MarCCD.poni'))
self.assertEqual(self.calibration_model.spectrum_geometry.pixel1, 0.000079)
def test_find_peaks_automatic(self):
self.load_pilatus_1M_and_find_peaks()
self.assertEqual(len(self.calibration_model.points), 6)
for points in self.calibration_model.points:
self.assertGreater(len(points), 0)
def test_find_peak(self):
"""
Tests the find_peak function for several maxima and pick points
"""
points_and_pick_points = [
[[30, 50], [31, 49]],
[[30, 50], [34, 46]],
[[5, 5], [3, 3]],
[[298, 298], [299, 299]]
]
for data in points_and_pick_points:
self.img_model._img_data = np.zeros((300, 300))
point = data[0]
pick_point = data[1]
self.img_model._img_data[point[0], point[1]] = 100
peak_point = self.calibration_model.find_peak(pick_point[0], pick_point[1], 10, 0)
self.assertEqual(peak_point[0][0], point[0])
self.assertEqual(peak_point[0][1], point[1])
def test_search_peaks_on_ring(self):
"""
Tests to search on the first ring of the calibrant after an inital calibration
"""
pass
def load_pilatus_1M_and_find_peaks(self):
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.calibration_model.find_peaks_automatic(517.664434674, 647.529865592, 0)
self.calibration_model.find_peaks_automatic(667.380513299, 525.252854758, 1)
self.calibration_model.find_peaks_automatic(671.110095329, 473.571503774, 2)
self.calibration_model.find_peaks_automatic(592.788872703, 350.495296791, 3)
self.calibration_model.find_peaks_automatic(387.395462348, 390.987901686, 4)
self.calibration_model.find_peaks_automatic(367.94835605, 554.290314848, 5)
def test_calibration_with_supersampling(self):
self.load_pilatus_1M_and_find_peaks()
self.calibration_model.set_calibrant(os.path.join(calibrant_path, 'LaB6.D'))
self.calibration_model.calibrate()
normal_poni1 = self.calibration_model.spectrum_geometry.poni1
self.img_model.set_supersampling(2)
self.calibration_model.set_supersampling(2)
self.calibration_model.calibrate()
self.assertAlmostEqual(normal_poni1, self.calibration_model.spectrum_geometry.poni1, places=5)
def test_calibration1(self):
self.img_model.load(os.path.join(data_path, 'LaB6_40keV_MarCCD.tif'))
self.calibration_model.find_peaks_automatic(1179.6, 1129.4, 0)
self.calibration_model.find_peaks_automatic(1268.5, 1119.8, 1)
self.calibration_model.set_calibrant(os.path.join(calibrant_path, 'LaB6.D'))
self.calibration_model.calibrate()
self.assertGreater(self.calibration_model.spectrum_geometry.poni1, 0)
self.assertAlmostEqual(self.calibration_model.spectrum_geometry.dist, 0.18, delta=0.01)
self.assertGreater(self.calibration_model.cake_geometry.poni1, 0)
def test_calibration2(self):
self.img_model.load(os.path.join(data_path, 'LaB6_OffCenter_PE.tif'))
self.calibration_model.find_peaks_automatic(1245.2, 1919.3, 0)
self.calibration_model.find_peaks_automatic(1334.0, 1823.7, 1)
self.calibration_model.start_values['dist'] = 500e-3
self.calibration_model.start_values['pixel_height'] = 200e-6
self.calibration_model.start_values['pixel_width'] = 200e-6
self.calibration_model.set_calibrant(os.path.join(calibrant_path, 'LaB6.D'))
self.calibration_model.calibrate()
self.assertGreater(self.calibration_model.spectrum_geometry.poni1, 0)
self.assertAlmostEqual(self.calibration_model.spectrum_geometry.dist, 0.500, delta=0.01)
self.assertGreater(self.calibration_model.cake_geometry.poni1, 0)
def test_calibration3(self):
self.load_pilatus_1M_and_find_peaks()
self.calibration_model.start_values['wavelength'] = 0.406626e-10
self.calibration_model.start_values['pixel_height'] = 172e-6
self.calibration_model.start_values['pixel_width'] = 172e-6
self.calibration_model.set_calibrant(os.path.join(calibrant_path, 'LaB6.D'))
self.calibration_model.calibrate()
self.assertGreater(self.calibration_model.spectrum_geometry.poni1, 0)
self.assertAlmostEqual(self.calibration_model.spectrum_geometry.dist, 0.100, delta=0.02)
self.assertGreater(self.calibration_model.cake_geometry.poni1, 0)
def test_get_pixel_ind(self):
self.img_model.load(os.path.join(data_path, 'image_001.tif'))
self.calibration_model.load(os.path.join(data_path, 'LaB6_40keV_MarCCD.poni'))
self.calibration_model.integrate_1d(1000)
tth_array = self.calibration_model.spectrum_geometry.ttha
azi_array = self.calibration_model.spectrum_geometry.chia
for i in range(100):
ind1 = np.random.random_integers(0, 2023)
ind2 = np.random.random_integers(0, 2023)
tth = tth_array[ind1, ind2]
azi = azi_array[ind1, ind2]
result_ind1, result_ind2 = self.calibration_model.get_pixel_ind(tth, azi)
self.assertAlmostEqual(ind1, result_ind1, places=3)
self.assertAlmostEqual(ind2, result_ind2, places=3)
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))
