def test_smooth(): # Read in the image data. img = nib.load(pathtoclassdata + "ds114_sub009_t2r1.nii") data = img.get_data()[..., 4:] # Run the smoothvoxels function with fwhm = 0 (No smoothing) at time 7 non_smoothed_data = smoothvoxels(data, 0, 7) # assert that data at time 7 and non_smoothed_data are equal since fwhm = 0 assert_almost_equal(data[..., 7], non_smoothed_data) # Run the smoothvoxels function with fwhm = 5 at time 7 smoothed_data = smoothvoxels(data, 5, 7) # assert that data at time 7 and smoothed_data are not equal assert_not_equals(data[..., 7].all(), smoothed_data.all())
def test_smooth():
# Read in the image data.
img = nib.load(pathtoclassdata + "ds114_sub009_t2r1.nii")
data = img.get_data()[..., 4:]
# Run the smoothvoxels function with fwhm = 0 (No smoothing) at time 7
non_smoothed_data = smoothvoxels(data, 0, 7)
# assert that data at time 7 and non_smoothed_data are equal since fwhm = 0
assert_almost_equal(data[..., 7], non_smoothed_data)
# Run the smoothvoxels function with fwhm = 5 at time 7
smoothed_data = smoothvoxels(data, 5, 7)
# assert that data at time 7 and smoothed_data are not equal
assert_not_equals(data[..., 7].all(), smoothed_data.all())
# Load the image data for subject 1.
img = nib.load(pathtodata+"BOLD/task001_run001/bold.nii.gz")
data = img.get_data()
data = data[...,6:] # Knock off the first 6 observations.
#######################
# a. (my) smoothing #
#######################
# Kind of arbitrary chosen time
time = 7
original_slice = data[..., 7]
# full width at half maximum (FWHM)
fwhm = 1.5
smoothed_slice = smoothvoxels(data, fwhm, time)
# visually compare original_slice to smoothed_slice
plt.imshow(present_3d(smoothed_slice))
plt.colorbar()
plt.title('Smoothed Slice')
plt.clim(0,1600)
plt.savefig(location_of_images+"smoothed_slice.png")
plt.close()
plt.imshow(present_3d(original_slice))
plt.colorbar()
plt.title('Original Slice')
plt.clim(0,1600)
plt.savefig(location_of_images+"original_slice.png")
data = data[...,num_TR_cut:]
#########################
# smoothing per slice #
#########################
smoothed_data =np.zeros(data.shape)
for time in np.arange(data.shape[-1]):
# Kind of arbitrary chosen time
sigma = 1.5
fwhm = (2*np.sqrt(2 *np.log(2))) * sigma
smoothed_data[...,time]= smoothvoxels(data, sigma, time)
smoothed_data
img = nib.Nifti1Image(smoothed_data, affine)
nib.save(img,os.path.join(final_data + "smooth/",str(name)+"_bold_smoothed.nii"))
### 266.3 MB for first one
sys.stdout.write("-")
sys.stdout.flush()
sys.stdout.write("\n")
# Load the image data for subject 1.
img = nib.load(pathtodata + "BOLD/task001_run001/bold.nii.gz")
data = img.get_data()
data = data[..., 6:] # Knock off the first 6 observations.
#######################
# a. (my) smoothing #
#######################
# Kind of arbitrary chosen time
time = 7
original_slice = data[..., 7]
# full width at half maximum (FWHM)
fwhm = 1.5
smoothed_slice = smoothvoxels(data, fwhm, time)
# visually compare original_slice to smoothed_slice
plt.imshow(present_3d(smoothed_slice))
plt.colorbar()
plt.title('Smoothed Slice')
plt.clim(0, 1600)
plt.savefig(location_of_images + "smoothed_slice.png")
plt.close()
plt.imshow(present_3d(original_slice))
plt.colorbar()
plt.title('Original Slice')
plt.clim(0, 1600)
plt.savefig(location_of_images + "original_slice.png")
data = data[...,num_TR_cut:]
#########################
# Smoothing per slice #
#########################
smoothed_data = np.zeros(data.shape)
for time in np.arange(data.shape[-1]):
# Kind of arbitrary chosen time
sigma = 1
smoothed_data[..., time] = smoothvoxels(data, sigma, time)
smoothed_data
img = nib.Nifti1Image(smoothed_data, affine)
nib.save(img,os.path.join(final_data + "smooth/", str(name) + "_bold_smoothed.nii"))
### 266.3 MB for first one
sys.stdout.write("-")
sys.stdout.flush()
sys.stdout.write("\n")
