def setUp(self):
self.img_model = ImgModel()
self.mask_model = MaskModel()
self.spectrum_model = PatternModel()
# setting up the calibration model but mocking the integration for speed
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.num_points = 1000
dummy_x = np.linspace(0, 25, 1000)
dummy_y = np.sin(dummy_x)
self.calibration_model.integrate_1d = mock.Mock(return_value=(dummy_x,
dummy_y))
self.phase_model = PhaseModel()
self.widget = IntegrationWidget()
self.integration_controller = IntegrationController(
{'spectrum': data_path},
widget=self.widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.image_controller = self.integration_controller.image_controller
self.calibration_model.load(
os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
def setUp(self):
self.img_model = ImgModel()
self.mask_model = MaskModel()
self.spectrum_model = PatternModel()
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.integrate_1d = MagicMock(
return_value=(self.calibration_model.tth,
self.calibration_model.int))
self.phase_model = PhaseModel()
self.integration_widget = IntegrationWidget()
self.integration_controller = IntegrationController(
{'spectrum': data_path},
widget=self.integration_widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.calibration_model.load(
os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.integration_spectrum_controller = self.integration_controller.spectrum_controller
self.integration_image_controller = self.integration_controller.image_controller
def main():
print("Let's train your model !")
args = parse()
model = ImgModel(args.arch, args.hidden_units)
if args.gpu:
device = 'cuda'
else:
device = 'cpu'
model.to(device)
data = get_data(args.data_directory)
criterion = torch.nn.NLLLoss()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.learning_rate)
train(model,
data['train'],
data['valid'],
criterion,
optimizer,
epochs=args.epochs,
device=torch.device(device))
if not (os.path.isdir(args.save_dir)):
os.mkdir(args.save_dir)
path = args.save_dir + '/model.pth'
model.save(path)
print("model finished!")
return None
def main():
args = parse()
model = ImgModel.load(args.model_checkpoint)
if args.gpu :
device = 'cuda'
else :
device = 'cpu'
model.to(device)
prediction = predict(args.image_input,model,torch.device(device),args.top_k)
display_prediction(prediction,args.category_names)
def setUp(self):
self.widget = IntegrationWidget()
self.spectrum_model = PatternModel()
self.img_model = ImgModel()
self.spectrum_controller = PatternController({}, self.widget,
self.img_model, None,
None, self.spectrum_model)
self.background_controller = BackgroundController({}, self.widget,
self.img_model,
self.spectrum_model)
self.overlay_tw = self.widget.overlay_tw
def setUp(self):
self.app = QtGui.QApplication(sys.argv)
self.img_model = ImgModel()
self.mask_model = MaskModel()
self.spectrum_model = SpectrumModel()
self.calibration_model = CalibrationModel(self.img_model)
self.phase_model = PhaseModel()
self.integration_widget = IntegrationWidget()
self.integration_controller = IntegrationController({'spectrum': data_path},
widget=self.integration_widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.calibration_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.integration_spectrum_controller = self.integration_controller.spectrum_controller
self.integration_image_controller = self.integration_controller.image_controller
def setUp(self):
self.img_model = ImgModel()
self.mask_model = MaskModel()
self.spectrum_model = PatternModel()
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.integrate_1d = MagicMock(return_value=(self.calibration_model.tth,
self.calibration_model.int))
self.phase_model = PhaseModel()
self.integration_widget = IntegrationWidget()
self.integration_controller = IntegrationController({'spectrum': data_path},
widget=self.integration_widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.calibration_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.integration_spectrum_controller = self.integration_controller.spectrum_controller
self.integration_image_controller = self.integration_controller.image_controller
def setUp(self):
self.img_model = ImgModel()
self.mask_model = MaskModel()
self.spectrum_model = PatternModel()
# setting up the calibration model but mocking the integration for speed
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.num_points = 1000
dummy_x = np.linspace(0, 25, 1000)
dummy_y = np.sin(dummy_x)
self.calibration_model.integrate_1d = mock.Mock(return_value=(dummy_x, dummy_y))
self.phase_model = PhaseModel()
self.widget = IntegrationWidget()
self.integration_controller = IntegrationController({'spectrum': data_path},
widget=self.widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.image_controller = self.integration_controller.image_controller
self.calibration_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
class IntegrationControllerTest(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.mask_model = MaskModel()
self.spectrum_model = PatternModel()
# setting up the calibration model but mocking the integration for speed
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.num_points = 1000
dummy_x = np.linspace(0, 25, 1000)
dummy_y = np.sin(dummy_x)
self.calibration_model.integrate_1d = mock.Mock(return_value=(dummy_x,
dummy_y))
self.phase_model = PhaseModel()
self.widget = IntegrationWidget()
self.integration_controller = IntegrationController(
{'spectrum': data_path},
widget=self.widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.image_controller = self.integration_controller.image_controller
self.calibration_model.load(
os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
def tearDown(self):
del self.calibration_model
del self.img_model
del self.phase_model
del self.widget
del self.integration_controller
del self.image_controller
gc.collect()
def _setup_batch_integration(self):
# setting up filenames and working directories
filenames = ['image_001.tif', 'image_002.tif']
input_filenames = [os.path.join(data_path, f) for f in filenames]
working_dir = os.path.join(data_path, 'out')
if not os.path.exists(working_dir):
os.mkdir(working_dir)
self.image_controller.working_dir['spectrum'] = os.path.join(
working_dir)
self.widget.spec_autocreate_cb.setChecked(True)
return filenames, input_filenames, working_dir
def test_batch_integration_of_multiple_files(self):
filenames, input_filenames, working_dir = self._setup_batch_integration(
)
self.image_controller.load_file(input_filenames)
for filename in filenames:
filename = filename.split('.')[0] + '.xy'
filepath = os.path.join(working_dir, filename)
self.assertTrue(os.path.exists(filepath))
os.remove(filepath)
# cleaning up
os.rmdir(working_dir)
def test_batch_integration_with_automatic_background_subtraction(self):
filenames, input_filenames, working_dir = self._setup_batch_integration(
)
self.widget.bkg_spectrum_gb.setChecked(True)
self.image_controller.load_file(input_filenames)
self.assertTrue(
os.path.exists(os.path.join(working_dir, 'bkg_subtracted')))
# check if two kind of files have been saved
for filename in filenames:
filename = filename.split('.')[0] + '.xy'
orig_filepath = os.path.join(working_dir, filename)
self.assertTrue(os.path.exists(orig_filepath))
os.remove(orig_filepath)
bkg_subtracted_filepath = os.path.join(working_dir,
'bkg_subtracted', filename)
self.assertTrue(os.path.exists(bkg_subtracted_filepath))
os.remove(bkg_subtracted_filepath)
os.rmdir(os.path.join(working_dir, 'bkg_subtracted'))
os.rmdir(working_dir)
def test_switching_to_cake_mode_without_having_clicked_the_image_before(
self):
QTest.mouseClick(self.widget.img_mode_btn, QtCore.Qt.LeftButton)
QTest.mouseClick(self.widget.img_mode_btn, QtCore.Qt.LeftButton)
class IntegrationFunctionalTest(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.mask_model = MaskModel()
self.spectrum_model = PatternModel()
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.integrate_1d = MagicMock(return_value=(self.calibration_model.tth,
self.calibration_model.int))
self.phase_model = PhaseModel()
self.integration_widget = IntegrationWidget()
self.integration_controller = IntegrationController({'spectrum': data_path},
widget=self.integration_widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.calibration_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.integration_spectrum_controller = self.integration_controller.spectrum_controller
self.integration_image_controller = self.integration_controller.image_controller
def tearDown(self):
del self.integration_spectrum_controller
del self.mask_model
del self.img_model
del self.calibration_model.cake_geometry
del self.calibration_model.spectrum_geometry
del self.calibration_model
del self.integration_widget
del self.integration_controller
gc.collect()
def enter_value_into_text_field(self, text_field, value):
text_field.setText('')
QTest.keyClicks(text_field, str(value))
QTest.keyPress(text_field, QtCore.Qt.Key_Enter)
QtGui.QApplication.processEvents()
def test_changing_number_of_integration_bins(self):
# Edith wants to change the number of integration bins in order to see the effect of binning onto her line
# shape. She sees that there is an option in the X tab and deselects automatic and sees that the sbinbox
# becomes editable.
self.assertFalse(self.integration_widget.bin_count_txt.isEnabled())
self.integration_widget.automatic_binning_cb.setChecked(False)
self.assertTrue(self.integration_widget.bin_count_txt.isEnabled())
# she sees that the current value and wants to double it and notices that the spectrum looks a little bit
# smoother
previous_number_of_points = len(self.spectrum_model.pattern.x)
self.enter_value_into_text_field(self.integration_widget.bin_count_txt, 2 * previous_number_of_points)
self.calibration_model.integrate_1d.assert_called_with(num_points=2 * previous_number_of_points,
mask=None, unit='2th_deg')
# then she decides that having an automatic estimation may probably be better and changes back to automatic.
# immediately the number is restored and the image looks like when she started
self.integration_widget.automatic_binning_cb.setChecked(True)
self.calibration_model.integrate_1d.assert_called_with(num_points=None,
mask=None, unit='2th_deg')
def test_changing_supersampling_amount_integrating_to_cake_with_mask(self):
# Edith opens the program, calibrates everything and looks in to the options menu. She sees that there is a
# miraculous parameter called supersampling. It is currently set to 1 which seems to be normal
self.assertEqual(self.integration_widget.supersampling_sb.value(), 1)
# then she sets it to two and she sees that the number of spectrum bin changes and that the spectrum looks
# smoother
# values before:
px1 = self.calibration_model.spectrum_geometry.pixel1
px2 = self.calibration_model.spectrum_geometry.pixel2
img_shape = self.img_model.img_data.shape
self.integration_widget.supersampling_sb.setValue(2)
self.assertEqual(self.calibration_model.spectrum_geometry.pixel1, 0.5 * px1)
self.assertEqual(self.calibration_model.spectrum_geometry.pixel2, 0.5 * px2)
self.assertEqual(self.calibration_model.cake_geometry.pixel1, px1)
self.assertEqual(self.calibration_model.cake_geometry.pixel2, px2)
self.assertEqual(self.img_model.img_data.shape[0], 2 * img_shape[0])
self.assertEqual(self.img_model.img_data.shape[1], 2 * img_shape[1])
self.mask_model.load_mask(os.path.join(data_path, 'test.mask'))
QTest.mouseClick(self.integration_widget.img_mask_btn, QtCore.Qt.LeftButton)
QTest.mouseClick(self.integration_widget.img_mode_btn, QtCore.Qt.LeftButton)
def test_saving_image(self):
# the widget has to be shown to be able to save the image:
self.integration_widget.show()
# Tests if the image save procedures are working for the different possible file endings
self.integration_image_controller.save_img(os.path.join(data_path, 'Test_img.png'))
self.integration_image_controller.save_img(os.path.join(data_path, 'Test_img.tiff'))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_img.png')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_img.tiff')))
os.remove(os.path.join(data_path, 'Test_img.png'))
os.remove(os.path.join(data_path, 'Test_img.tiff'))
def test_saving_spectrum(self):
# the widget has to be shown to be able to save the image:
self.integration_widget.show()
# Tests if the spectrum save procedures is are working for all fileendings
def save_spectra_test_for_size_and_delete(self):
self.integration_spectrum_controller.save_pattern(os.path.join(data_path, 'Test_spec.xy'))
self.integration_spectrum_controller.save_pattern(os.path.join(data_path, 'Test_spec.chi'))
self.integration_spectrum_controller.save_pattern(os.path.join(data_path, 'Test_spec.dat'))
self.integration_spectrum_controller.save_pattern(os.path.join(data_path, 'Test_spec.png'))
self.integration_spectrum_controller.save_pattern(os.path.join(data_path, 'Test_spec.svg'))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_spec.xy')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_spec.chi')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_spec.dat')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_spec.png')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'Test_spec.svg')))
self.assertGreater(os.stat(os.path.join(data_path, 'Test_spec.xy')).st_size, 1)
self.assertGreater(os.stat(os.path.join(data_path, 'Test_spec.chi')).st_size, 1)
self.assertGreater(os.stat(os.path.join(data_path, 'Test_spec.dat')).st_size, 1)
self.assertGreater(os.stat(os.path.join(data_path, 'Test_spec.png')).st_size, 1)
self.assertGreater(os.stat(os.path.join(data_path, 'Test_spec.svg')).st_size, 1)
os.remove(os.path.join(data_path, 'Test_spec.xy'))
os.remove(os.path.join(data_path, 'Test_spec.chi'))
os.remove(os.path.join(data_path, 'Test_spec.dat'))
os.remove(os.path.join(data_path, 'Test_spec.png'))
os.remove(os.path.join(data_path, 'Test_spec.svg'))
save_spectra_test_for_size_and_delete(self)
QTest.mouseClick(self.integration_spectrum_controller.widget.spec_q_btn, QtCore.Qt.LeftButton)
save_spectra_test_for_size_and_delete(self)
QTest.mouseClick(self.integration_spectrum_controller.widget.spec_d_btn, QtCore.Qt.LeftButton)
save_spectra_test_for_size_and_delete(self)
def test_undocking_and_docking_img_frame(self):
QTest.mouseClick(self.integration_widget.img_dock_btn, QtCore.Qt.LeftButton)
QTest.mouseClick(self.integration_widget.img_dock_btn, QtCore.Qt.LeftButton)
def test_loading_multiple_images_and_batch_integrate_them(self):
self.integration_widget.spec_autocreate_cb.setChecked(True)
self.assertTrue(self.integration_widget.spec_autocreate_cb.isChecked())
self.integration_image_controller.load_file([os.path.join(data_path, 'image_001.tif'),
os.path.join(data_path, 'image_002.tif')])
self.assertTrue(os.path.exists(os.path.join(data_path, 'image_001.xy')))
self.assertTrue(os.path.exists(os.path.join(data_path, 'image_002.xy')))
os.remove(os.path.join(data_path, 'image_001.xy'))
os.remove(os.path.join(data_path, 'image_002.xy'))
class IntegrationControllerTest(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.mask_model = MaskModel()
self.spectrum_model = PatternModel()
# setting up the calibration model but mocking the integration for speed
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.num_points = 1000
dummy_x = np.linspace(0, 25, 1000)
dummy_y = np.sin(dummy_x)
self.calibration_model.integrate_1d = mock.Mock(return_value=(dummy_x, dummy_y))
self.phase_model = PhaseModel()
self.widget = IntegrationWidget()
self.integration_controller = IntegrationController({'spectrum': data_path},
widget=self.widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.image_controller = self.integration_controller.image_controller
self.calibration_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
def tearDown(self):
del self.calibration_model
del self.img_model
del self.phase_model
del self.widget
del self.integration_controller
del self.image_controller
gc.collect()
def _setup_batch_integration(self):
# setting up filenames and working directories
filenames = ['image_001.tif', 'image_002.tif']
input_filenames = [os.path.join(data_path, f) for f in filenames]
working_dir = os.path.join(data_path, 'out')
if not os.path.exists(working_dir):
os.mkdir(working_dir)
self.image_controller.working_dir['spectrum'] = os.path.join(working_dir)
self.widget.spec_autocreate_cb.setChecked(True)
return filenames, input_filenames, working_dir
def test_batch_integration_of_multiple_files(self):
filenames, input_filenames, working_dir = self._setup_batch_integration()
self.image_controller.load_file(input_filenames)
for filename in filenames:
filename = filename.split('.')[0] + '.xy'
filepath = os.path.join(working_dir, filename)
self.assertTrue(os.path.exists(filepath))
os.remove(filepath)
# cleaning up
os.rmdir(working_dir)
def test_batch_integration_with_automatic_background_subtraction(self):
filenames, input_filenames, working_dir = self._setup_batch_integration()
self.widget.bkg_spectrum_gb.setChecked(True)
self.image_controller.load_file(input_filenames)
self.assertTrue(os.path.exists(os.path.join(working_dir, 'bkg_subtracted')))
# check if two kind of files have been saved
for filename in filenames:
filename = filename.split('.')[0] + '.xy'
orig_filepath = os.path.join(working_dir, filename)
self.assertTrue(os.path.exists(orig_filepath))
os.remove(orig_filepath)
bkg_subtracted_filepath = os.path.join(working_dir, 'bkg_subtracted', filename)
self.assertTrue(os.path.exists(bkg_subtracted_filepath))
os.remove(bkg_subtracted_filepath)
os.rmdir(os.path.join(working_dir, 'bkg_subtracted'))
os.rmdir(working_dir)
def test_switching_to_cake_mode_without_having_clicked_the_image_before(self):
QTest.mouseClick(self.widget.img_mode_btn, QtCore.Qt.LeftButton)
QTest.mouseClick(self.widget.img_mode_btn, QtCore.Qt.LeftButton)
class IntegrationFunctionalTest(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.mask_model = MaskModel()
self.spectrum_model = PatternModel()
self.calibration_model = CalibrationModel(self.img_model)
self.calibration_model.integrate_1d = MagicMock(
return_value=(self.calibration_model.tth,
self.calibration_model.int))
self.phase_model = PhaseModel()
self.integration_widget = IntegrationWidget()
self.integration_controller = IntegrationController(
{'spectrum': data_path},
widget=self.integration_widget,
img_model=self.img_model,
mask_model=self.mask_model,
calibration_model=self.calibration_model,
spectrum_model=self.spectrum_model,
phase_model=self.phase_model)
self.calibration_model.load(
os.path.join(data_path, 'CeO2_Pilatus1M.poni'))
self.img_model.load(os.path.join(data_path, 'CeO2_Pilatus1M.tif'))
self.integration_spectrum_controller = self.integration_controller.spectrum_controller
self.integration_image_controller = self.integration_controller.image_controller
def tearDown(self):
del self.integration_spectrum_controller
del self.mask_model
del self.img_model
del self.calibration_model.cake_geometry
del self.calibration_model.spectrum_geometry
del self.calibration_model
del self.integration_widget
del self.integration_controller
gc.collect()
def enter_value_into_text_field(self, text_field, value):
text_field.setText('')
QTest.keyClicks(text_field, str(value))
QTest.keyPress(text_field, QtCore.Qt.Key_Enter)
QtGui.QApplication.processEvents()
def test_changing_number_of_integration_bins(self):
# Edith wants to change the number of integration bins in order to see the effect of binning onto her line
# shape. She sees that there is an option in the X tab and deselects automatic and sees that the sbinbox
# becomes editable.
self.assertFalse(self.integration_widget.bin_count_txt.isEnabled())
self.integration_widget.automatic_binning_cb.setChecked(False)
self.assertTrue(self.integration_widget.bin_count_txt.isEnabled())
# she sees that the current value and wants to double it and notices that the spectrum looks a little bit
# smoother
previous_number_of_points = len(self.spectrum_model.pattern.x)
self.enter_value_into_text_field(self.integration_widget.bin_count_txt,
2 * previous_number_of_points)
self.calibration_model.integrate_1d.assert_called_with(
num_points=2 * previous_number_of_points,
mask=None,
unit='2th_deg')
# then she decides that having an automatic estimation may probably be better and changes back to automatic.
# immediately the number is restored and the image looks like when she started
self.integration_widget.automatic_binning_cb.setChecked(True)
self.calibration_model.integrate_1d.assert_called_with(num_points=None,
mask=None,
unit='2th_deg')
def test_changing_supersampling_amount_integrating_to_cake_with_mask(self):
# Edith opens the program, calibrates everything and looks in to the options menu. She sees that there is a
# miraculous parameter called supersampling. It is currently set to 1 which seems to be normal
self.assertEqual(self.integration_widget.supersampling_sb.value(), 1)
# then she sets it to two and she sees that the number of spectrum bin changes and that the spectrum looks
# smoother
# values before:
px1 = self.calibration_model.spectrum_geometry.pixel1
px2 = self.calibration_model.spectrum_geometry.pixel2
img_shape = self.img_model.img_data.shape
self.integration_widget.supersampling_sb.setValue(2)
self.assertEqual(self.calibration_model.spectrum_geometry.pixel1,
0.5 * px1)
self.assertEqual(self.calibration_model.spectrum_geometry.pixel2,
0.5 * px2)
self.assertEqual(self.calibration_model.cake_geometry.pixel1, px1)
self.assertEqual(self.calibration_model.cake_geometry.pixel2, px2)
self.assertEqual(self.img_model.img_data.shape[0], 2 * img_shape[0])
self.assertEqual(self.img_model.img_data.shape[1], 2 * img_shape[1])
self.mask_model.load_mask(os.path.join(data_path, 'test.mask'))
QTest.mouseClick(self.integration_widget.img_mask_btn,
QtCore.Qt.LeftButton)
QTest.mouseClick(self.integration_widget.img_mode_btn,
QtCore.Qt.LeftButton)
def test_saving_image(self):
# the widget has to be shown to be able to save the image:
self.integration_widget.show()
# Tests if the image save procedures are working for the different possible file endings
self.integration_image_controller.save_img(
os.path.join(data_path, 'Test_img.png'))
self.integration_image_controller.save_img(
os.path.join(data_path, 'Test_img.tiff'))
self.assertTrue(os.path.exists(os.path.join(data_path,
'Test_img.png')))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_img.tiff')))
os.remove(os.path.join(data_path, 'Test_img.png'))
os.remove(os.path.join(data_path, 'Test_img.tiff'))
def test_saving_spectrum(self):
# the widget has to be shown to be able to save the image:
self.integration_widget.show()
# Tests if the spectrum save procedures is are working for all fileendings
def save_spectra_test_for_size_and_delete(self):
self.integration_spectrum_controller.save_pattern(
os.path.join(data_path, 'Test_spec.xy'))
self.integration_spectrum_controller.save_pattern(
os.path.join(data_path, 'Test_spec.chi'))
self.integration_spectrum_controller.save_pattern(
os.path.join(data_path, 'Test_spec.dat'))
self.integration_spectrum_controller.save_pattern(
os.path.join(data_path, 'Test_spec.png'))
self.integration_spectrum_controller.save_pattern(
os.path.join(data_path, 'Test_spec.svg'))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_spec.xy')))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_spec.chi')))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_spec.dat')))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_spec.png')))
self.assertTrue(
os.path.exists(os.path.join(data_path, 'Test_spec.svg')))
self.assertGreater(
os.stat(os.path.join(data_path, 'Test_spec.xy')).st_size, 1)
self.assertGreater(
os.stat(os.path.join(data_path, 'Test_spec.chi')).st_size, 1)
self.assertGreater(
os.stat(os.path.join(data_path, 'Test_spec.dat')).st_size, 1)
self.assertGreater(
os.stat(os.path.join(data_path, 'Test_spec.png')).st_size, 1)
self.assertGreater(
os.stat(os.path.join(data_path, 'Test_spec.svg')).st_size, 1)
os.remove(os.path.join(data_path, 'Test_spec.xy'))
os.remove(os.path.join(data_path, 'Test_spec.chi'))
os.remove(os.path.join(data_path, 'Test_spec.dat'))
os.remove(os.path.join(data_path, 'Test_spec.png'))
os.remove(os.path.join(data_path, 'Test_spec.svg'))
save_spectra_test_for_size_and_delete(self)
QTest.mouseClick(
self.integration_spectrum_controller.widget.spec_q_btn,
QtCore.Qt.LeftButton)
save_spectra_test_for_size_and_delete(self)
QTest.mouseClick(
self.integration_spectrum_controller.widget.spec_d_btn,
QtCore.Qt.LeftButton)
save_spectra_test_for_size_and_delete(self)
def test_undocking_and_docking_img_frame(self):
QTest.mouseClick(self.integration_widget.img_dock_btn,
QtCore.Qt.LeftButton)
QTest.mouseClick(self.integration_widget.img_dock_btn,
QtCore.Qt.LeftButton)
def test_loading_multiple_images_and_batch_integrate_them(self):
self.integration_widget.spec_autocreate_cb.setChecked(True)
self.assertTrue(self.integration_widget.spec_autocreate_cb.isChecked())
self.integration_image_controller.load_file([
os.path.join(data_path, 'image_001.tif'),
os.path.join(data_path, 'image_002.tif')
])
self.assertTrue(os.path.exists(os.path.join(data_path,
'image_001.xy')))
self.assertTrue(os.path.exists(os.path.join(data_path,
'image_002.xy')))
os.remove(os.path.join(data_path, 'image_001.xy'))
os.remove(os.path.join(data_path, 'image_002.xy'))
