def test_can_get_surface_areas(self):
# the following compares images and test images that are known
# to satisfy the condition that an ROI w/ label n will have n
# voxels that border white matter... labels in these images
# are defined for 1:10
roiImage = images.TNImage(
filename='test_data/images/ROI_50x50x50.nii.gz')
maskImage = images.TNImage(
filename='test_data/images/ROI_WM_MASK_50x50x50.nii.gz')
result = images.surface_area_for_rois(roiImage, maskImage)
for label in range(1, 11): # 1:10
assert result[label] == label
def test_can_get_wm_neighbors_for_value(self):
# for a test we will provide a 3dim white-matter mask with one
# zero value in the xyz-center of the cube... this
# configuration should yield 26 neighbors (entire cube
# centered around center voxel, less the voxel itself). The
# ROI image will consist of all ones, being of the same shape
# as the mask.
roiImage = images.TNImage(
filename='test_data/images/3dim_images/ones.nii.gz')
maskImage = images.TNImage(
filename='test_data/images/3dim_images/wm_mask_only_center.nii.gz')
assert len(images.get_wm_neighbors_for_value(roiImage, maskImage,
1)) == 26
def test_can_get_number_of_b_zeros(self):
subject_dir = 'test_data/images/subject_dir/SUB_0001/'
my_obj = images.TNImage(filename=subject_dir + 'rdata.nii.gz',
bvecs_file=subject_dir + 'bvecs',
bvals_file=subject_dir + 'bvals')
# this particular subject has 2 b0's
assert my_obj.number_of_b_zeros() == 2
def test_delta_z_dz_is_accurate(self):
from os.path import join
test_img = join(SUPPORT_DIR, 'noise.nii.gz')
my_obj = images.TNImage(filename=test_img)
mean_z = my_obj.z_mean()
delta_z_dz = my_obj.delta_z_dz()
assert (mean_z[1, :] - mean_z[0, :] == delta_z_dz[1, :]).all()
assert (mean_z[0, :] - mean_z[-1, :] == delta_z_dz[0, :]).all()
def test_delta_z_dv_is_accurate(self):
from os.path import join
test_img = join(SUPPORT_DIR, 'noise.nii.gz')
my_obj = images.TNImage(filename=test_img)
mean_z = my_obj.z_mean()
delta_z_dv = my_obj.delta_z_dv()
assert (mean_z[:, 1] - mean_z[:, 0] == delta_z_dv[:, 1]).all()
assert (mean_z[:, 0] - mean_z[:, -1] == delta_z_dv[:, 0]).all()
def test_access_number_of_b_zeros_without_set_no_error(self):
my_obj = images.TNImage()
error = False
try:
my_obj.number_of_b_zeros()
except Exception as e:
print e
error = True
assert not error
def test_v_mean_is_accurate(self):
from nibabel import load
from os.path import join
import numpy as np
test_img = join(SUPPORT_DIR, 'noise.nii.gz')
my_obj = images.TNImage(filename=test_img)
data = load(test_img).get_data()
flat = data[:, :, :, 0].ravel()
assert np.mean(flat) == my_obj.v_mean()[0]
def test_can_set_number_of_b_zeros(self):
my_obj = images.TNImage()
my_obj.number_of_b_zeros = 5
assert my_obj.number_of_b_zeros == 5
def test_can_get_delta_z_dv(self):
from os.path import join
my_image = images.TNImage(join(SUPPORT_DIR, 'one.nii.gz'))
assert my_image.delta_z_dv() is not None
def test_can_get_z_mean_for_3dim(self):
from os.path import join
my_image = images.TNImage(join(SUPPORT_DIR, '3dim_images/3dim.nii.gz'))
assert my_image.z_mean() is not None
def test_can_get_delta_z_dz(self):
from os.path import join
my_image = images.TNImage(join(SUPPORT_DIR, 'one.nii.gz'))
assert (my_image.delta_z_dz() == 0).all()
def test_can_instantiate_class_from_filename(self):
from os.path import join
assert images.TNImage(join(SUPPORT_DIR, 'noise.nii.gz')) is not None
def test_can_instantiate_class(self):
assert images.TNImage() is not None
def setUp(self):
self._i = images.TNImage()