def test_inverted_mirror_ar2rectangular(self):
data_invMirror = ensure2DImage(self.data_invMir[0])
result_invMirror = angleres.AngleResolved2Rectangular(
data_invMirror, (90, 360))
# get the inverted image of the one that corresponds to the flipped mirror
data = data_invMirror[::-1, :]
data.metadata[model.MD_AR_FOCUS_DISTANCE] *= -1
arpole = data.metadata[model.MD_AR_POLE]
data.metadata[model.MD_AR_POLE] = (arpole[0], data_invMirror.shape[0] -
1 - arpole[1])
result_standardMirror = angleres.AngleResolved2Rectangular(
data, (90, 360))
numpy.testing.assert_allclose(result_invMirror,
result_standardMirror,
atol=1e-7)
def test_uint16_input_rect(self):
"""
Tests for input of DataArray with uint16 ndarray to rectangular projection
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Rectangular(data[0], (100, 400))
self.assertEqual(result.shape, (100, 400))
def test_uint16_input_rect2polar(self):
"""
Tests for input of DataArray with uint16 ndarray from rectangular projection to polar projection.
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
data[0].metadata[MD_POL_MODE] = MD_POL_S1 # add necessary metadata
# convert to rectangular
data_rect = angleres.AngleResolved2Rectangular(data[0], (100, 400))
# convert from rectangular to polar
result = angleres.Rectangular2Polar(data_rect, 201)
result_polar_1 = RGB2Greyscale(result)
# convert input data directly to polar representation
result_polar_2 = angleres.AngleResolved2Polar(data[0], 201)
self.assertEqual(result.shape, (201, 201, 3))
self.assertEqual(result_polar_2.shape, result_polar_1.shape)
def test_uint16_input_rect_intensity(self):
"""
Tests for input of DataArray with uint16 ndarray to rectangular projection checking that the
expected intensity values are at the correct theta/phi position in the rectangular and polar representation.
This test case is optimized for the current input image. If ever changed, this test case might fail!
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
data[0] = data[0][0:256, 0:241]
result_rectangular = angleres.AngleResolved2Rectangular(
data[0], (90, 360))
self.assertEqual(result_rectangular.shape, (90, 360))
# get theta/phi corresponding to x/y in data
theta_data, phi_data, intensity_data, circle_mask_dilated = \
angleres._ExtractAngleInformation(data[0], hole=False)
# find the position in the raw data with max intensity
pos_max = numpy.where(data[0] == numpy.max(data[0]))
x = pos_max[0][0]
y = pos_max[1][0]
# find the intensity value for the same position but corrected for photon collection efficiency
intensity_data_I = intensity_data[x][y]
# find the corresponding theta/phi angles
phi = phi_data[x][y]
theta = theta_data[x][y]
# find the position in the rectangular representation
px_phi = int(
numpy.round(result_rectangular.shape[1] / (2 * numpy.pi) * phi))
px_theta = int(
numpy.round(result_rectangular.shape[0] / (numpy.pi / 2) * theta))
result_I_rectangular = result_rectangular[px_theta][px_phi]
# test values are close due to rounding the px pos and interpolation
numpy.testing.assert_allclose(intensity_data_I,
result_I_rectangular,
atol=10e6)
# find a second characteristic position in raw image
x1 = 97
y1 = 77
# find the intensity value for the same position but corrected for photon collection efficiency
intensity_data_I_rectangular_2 = intensity_data[x1][y1]
# find the corresponding theta/phi angles
phi2 = phi_data[x1][y1]
theta2 = theta_data[x1][y1]
px_phi2 = int(
numpy.round(result_rectangular.shape[1] / (2 * numpy.pi) * phi2))
px_theta2 = int(
numpy.round(result_rectangular.shape[0] / (numpy.pi / 2) * theta2))
result_I_rectangular_2 = result_rectangular[px_theta2 - 1][
px_phi2 - 1] # Note rounding problems
# test values are close due to rounding the px pos and interpolation
numpy.testing.assert_allclose(intensity_data_I_rectangular_2,
result_I_rectangular_2,
atol=10e6)