def test_extract_k_space_center_3D(self):
""" This test ensures that the output of the non cartesian kspace
extraction is same a that of mimicked cartesian extraction in 3D
"""
_mask = np.ones((self.N, self.N, self.Nz))
_samples = convert_mask_to_locations(_mask)
Img = (np.random.randn(self.num_channel, self.N, self.N, self.Nz) +
1j * np.random.randn(self.num_channel, self.N, self.N, self.Nz))
Nby2_percent = self.N * self.percent / 2
Nzby2_percent = self.Nz * self.percent / 2
low = int(self.N / 2 - Nby2_percent)
high = int(self.N / 2 + Nby2_percent + 1)
lowz = int(self.Nz / 2 - Nzby2_percent)
highz = int(self.Nz / 2 + Nzby2_percent + 1)
center_Img = Img[:, low:high, low:high, lowz:highz]
thresh = self.percent * 0.5
data_thresholded, samples_thresholded = \
extract_k_space_center_and_locations(
data_values=np.reshape(Img, (self.num_channel,
self.N * self.N * self.Nz)),
samples_locations=_samples,
thr=(thresh, thresh, thresh),
img_shape=(self.N, self.N, self.Nz))
np.testing.assert_allclose(center_Img.reshape(data_thresholded.shape),
data_thresholded)
def test_extract_k_space_center_2D_fft(self):
""" Ensure that the extracted k-space center is right
for cartesian case"""
mask = np.random.randint(0, 2, (self.N, self.N))
samples = convert_mask_to_locations(mask)
Img = (np.random.randn(self.num_channel, self.N, self.N) +
1j * np.random.randn(self.num_channel, self.N, self.N))
Nby2_percent = self.N * self.percent / 2
low = int(self.N / 2 - Nby2_percent)
high = int(self.N / 2 + Nby2_percent + 1)
center_Img = Img[:, low:high, low:high]
cutoff_mask = mask[low:high, low:high]
locations = np.where(cutoff_mask.reshape(cutoff_mask.size))
center_Img = center_Img.reshape(
(center_Img.shape[0], cutoff_mask.size))[:, locations[0]]
thresh = self.percent * 0.5
data_thresholded, samples_thresholded = \
extract_k_space_center_and_locations(
data_values=Img,
samples_locations=samples,
thr=(thresh, thresh),
img_shape=(self.N, self.N))
np.testing.assert_allclose(
center_Img,
data_thresholded)
def test_extract_k_space_center_2D(self):
""" Ensure that the extracted k-space center is right"""
_mask = np.ones((self.N, self.N))
_samples = convert_mask_to_locations(_mask)
Img = (np.random.randn(self.num_channel, self.N, self.N) +
1j * np.random.randn(self.num_channel, self.N, self.N))
Nby2_percent = self.N * self.percent / 2
low = int(self.N / 2 - Nby2_percent)
high = int(self.N / 2 + Nby2_percent + 1)
center_Img = Img[:, low:high, low:high]
thresh = self.percent * 0.5
data_thresholded, samples_thresholded = \
extract_k_space_center_and_locations(
data_values=np.reshape(Img, (self.num_channel,
self.N * self.N)),
samples_locations=_samples,
thr=(thresh, thresh),
img_shape=(self.N, self.N))
np.testing.assert_allclose(center_Img.reshape(data_thresholded.shape),
data_thresholded)
