class MaskModelTest(unittest.TestCase):
def setUp(self):
self.mask_model = MaskModel()
self.img = np.zeros((10, 10))
self.mask_model.set_dimension(self.img.shape)
def tearDown(self):
del self.mask_model
del self.img
gc.collect()
def test_growing_masks(self):
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[0, 0] = 1
self.mask_model._mask_data[0, 9] = 1
self.mask_model._mask_data[9, 9] = 1
self.mask_model._mask_data[9, 0] = 1
self.mask_model.grow()
# test corners
self.assertEqual(self.mask_model._mask_data[0, 1], 1)
self.assertEqual(self.mask_model._mask_data[1, 1], 1)
self.assertEqual(self.mask_model._mask_data[1, 0], 1)
self.assertEqual(self.mask_model._mask_data[0, 8], 1)
self.assertEqual(self.mask_model._mask_data[1, 8], 1)
self.assertEqual(self.mask_model._mask_data[1, 9], 1)
self.assertEqual(self.mask_model._mask_data[8, 0], 1)
self.assertEqual(self.mask_model._mask_data[8, 1], 1)
self.assertEqual(self.mask_model._mask_data[9, 1], 1)
self.assertEqual(self.mask_model._mask_data[8, 8], 1)
self.assertEqual(self.mask_model._mask_data[8, 9], 1)
self.assertEqual(self.mask_model._mask_data[9, 8], 1)
# test center
self.assertEqual(self.mask_model._mask_data[3, 3], 1)
self.assertEqual(self.mask_model._mask_data[4, 3], 1)
self.assertEqual(self.mask_model._mask_data[5, 3], 1)
self.assertEqual(self.mask_model._mask_data[3, 5], 1)
self.assertEqual(self.mask_model._mask_data[4, 5], 1)
self.assertEqual(self.mask_model._mask_data[5, 5], 1)
self.assertEqual(self.mask_model._mask_data[3, 4], 1)
self.assertEqual(self.mask_model._mask_data[5, 4], 1)
def test_shrink_mask(self):
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[0, 0] = 1
self.mask_model._mask_data[0, 9] = 1
self.mask_model._mask_data[9, 9] = 1
self.mask_model._mask_data[9, 0] = 1
self.before_mask = np.copy(self.mask_model._mask_data)
self.mask_model.grow()
self.mask_model.shrink()
self.assertTrue(np.array_equal(self.before_mask, self.mask_model._mask_data))
self.mask_model.clear_mask()
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[5, 4] = 1
self.mask_model._mask_data[5, 5] = 1
self.mask_model._mask_data[4, 5] = 1
self.mask_model.shrink()
self.assertEqual(np.sum(self.mask_model._mask_data), 0)
def test_saving_and_loading(self):
self.mask_model.mask_ellipse(1024, 1024, 100, 100)
self.mask_model.set_dimension((2048, 2048))
mask_array = np.copy(self.mask_model.get_img())
filename = os.path.join(data_path, "dummy.mask")
self.mask_model.save_mask(filename)
self.mask_model.load_mask(filename)
self.assertTrue(np.array_equal(mask_array, self.mask_model.get_img()))
os.remove(filename)
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 MaskModelTest(unittest.TestCase):
def setUp(self):
self.mask_model = MaskModel()
self.img = np.zeros((10, 10))
self.mask_model.set_dimension(self.img.shape)
def tearDown(self):
del self.mask_model
del self.img
gc.collect()
def test_growing_masks(self):
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[0, 0] = 1
self.mask_model._mask_data[0, 9] = 1
self.mask_model._mask_data[9, 9] = 1
self.mask_model._mask_data[9, 0] = 1
self.mask_model.grow()
# tests corners
self.assertEqual(self.mask_model._mask_data[0, 1], 1)
self.assertEqual(self.mask_model._mask_data[1, 1], 1)
self.assertEqual(self.mask_model._mask_data[1, 0], 1)
self.assertEqual(self.mask_model._mask_data[0, 8], 1)
self.assertEqual(self.mask_model._mask_data[1, 8], 1)
self.assertEqual(self.mask_model._mask_data[1, 9], 1)
self.assertEqual(self.mask_model._mask_data[8, 0], 1)
self.assertEqual(self.mask_model._mask_data[8, 1], 1)
self.assertEqual(self.mask_model._mask_data[9, 1], 1)
self.assertEqual(self.mask_model._mask_data[8, 8], 1)
self.assertEqual(self.mask_model._mask_data[8, 9], 1)
self.assertEqual(self.mask_model._mask_data[9, 8], 1)
# tests center
self.assertEqual(self.mask_model._mask_data[3, 3], 1)
self.assertEqual(self.mask_model._mask_data[4, 3], 1)
self.assertEqual(self.mask_model._mask_data[5, 3], 1)
self.assertEqual(self.mask_model._mask_data[3, 5], 1)
self.assertEqual(self.mask_model._mask_data[4, 5], 1)
self.assertEqual(self.mask_model._mask_data[5, 5], 1)
self.assertEqual(self.mask_model._mask_data[3, 4], 1)
self.assertEqual(self.mask_model._mask_data[5, 4], 1)
def test_shrink_mask(self):
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[0, 0] = 1
self.mask_model._mask_data[0, 9] = 1
self.mask_model._mask_data[9, 9] = 1
self.mask_model._mask_data[9, 0] = 1
self.before_mask = np.copy(self.mask_model._mask_data)
self.mask_model.grow()
self.mask_model.shrink()
self.assertTrue(np.array_equal(self.before_mask, self.mask_model._mask_data))
self.mask_model.clear_mask()
self.mask_model._mask_data[4, 4] = 1
self.mask_model._mask_data[5, 4] = 1
self.mask_model._mask_data[5, 5] = 1
self.mask_model._mask_data[4, 5] = 1
self.mask_model.shrink()
self.assertEqual(np.sum(self.mask_model._mask_data), 0)
def test_saving_and_loading(self):
self.mask_model.mask_ellipse(1024, 1024, 100, 100)
self.mask_model.set_dimension((2048, 2048))
mask_array = np.copy(self.mask_model.get_img())
filename = os.path.join(data_path, 'dummy.mask')
self.mask_model.save_mask(filename)
self.mask_model.load_mask(filename)
self.assertTrue(np.array_equal(mask_array, self.mask_model.get_img()))
os.remove(filename)
