def test_normality():
# Generate some 4-d random uniform data.
# The first 3 dimensions are like voxels, the last like time.
np.random.seed(159)
sim_resids = np.random.rand(2, 2, 2, 200)
# Force one of the time courses to be standard normal.
sim_resids[0,0,0] = np.random.randn(200)
# Do Shaprio-Wilk and Kruskal-Wallis
sw_3d = check_sw(sim_resids)
kw_3d = check_kw(sim_resids)
assert(sw_3d[0,0,0] > 0.05)
assert(sw_3d[1,0,0] < 0.05)
assert(kw_3d[0,0,0] > 0.05)
def test_normality():
# Generate some 4-d random uniform data.
# The first 3 dimensions are like voxels, the last like time.
np.random.seed(159)
sim_resids = np.random.rand(2, 2, 2, 200)
# Force one of the time courses to be standard normal.
sim_resids[0, 0, 0] = np.random.randn(200)
# Do Shaprio-Wilk and Kruskal-Wallis
sw_3d = check_sw(sim_resids)
kw_3d = check_kw(sim_resids)
assert (sw_3d[0, 0, 0] > 0.05)
assert (sw_3d[1, 0, 0] < 0.05)
assert (kw_3d[0, 0, 0] > 0.05)
def test_normality():
# Generate some 4-d random uniform data.
# The first 3 dimensions are like voxels, the last like time.
np.random.seed(159)
sim_resids = np.random.rand(2, 2, 2, 200)
# Force one of the time courses to be standard normal.
sim_resids[0,0,0] = np.random.randn(200)
# Do Shaprio-Wilk.
sw_3d = check_sw(sim_resids) # 4-d residuals, 3-d p-values
sw_1d = check_sw_masked(sim_resids.reshape((-1, sim_resids.shape[-1]))) # 2-d residuals, 1-d p-values
# Do Kruskal-Wallis.
kw_3d = check_kw(sim_resids)
assert(sw_3d[0,0,0] > 0.05)
assert(sw_3d[1,0,0] < 0.05)
# Two Shaprio-Wilk functions should do the same thing over arrays of different dimensions.
assert(sw_3d[0,0,0] == sw_1d[0])
assert(kw_3d[0,0,0] > 0.05)
def test_normality():
# Generate some 4-d random uniform data.
# The first 3 dimensions are like voxels, the last like time.
np.random.seed(159)
sim_resids = np.random.rand(2, 2, 2, 200)
# Force one of the time courses to be standard normal.
sim_resids[0, 0, 0] = np.random.randn(200)
# Do Shaprio-Wilk.
sw_3d = check_sw(sim_resids) # 4-d residuals, 3-d p-values
sw_1d = check_sw_masked(sim_resids.reshape(
(-1, sim_resids.shape[-1]))) # 2-d residuals, 1-d p-values
# Do Kruskal-Wallis.
kw_3d = check_kw(sim_resids)
assert (sw_3d[0, 0, 0] > 0.05)
assert (sw_3d[1, 0, 0] < 0.05)
# Two Shaprio-Wilk functions should do the same thing over arrays of different dimensions.
assert (sw_3d[0, 0, 0] == sw_1d[0])
assert (kw_3d[0, 0, 0] > 0.05)
# Load our normality functions.
from normality import check_sw
# Load masking and visualization functions.
from Image_Visualizing import make_mask, present_3d
# Progress bar
toolbar_width=len(sub_list)
sys.stdout.write("GLM, : ")
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" * (toolbar_width+1)) # return to start of line, after '['
for i in sub_list:
residuals = np.load(residual_data+i+"_residual.npy")
sw_pvals = check_sw(residuals)
print(i+" proportion of voxels with p-value above 0.05 (unmasked): "+str(np.mean(sw_pvals > 0.05)))
mask = nib.load(path_to_data+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
masked_pvals = make_mask(sw_pvals, mask_data, fit=True)
pvals_in_brain = sw_pvals.ravel()[masked_pvals.ravel() != 0]
print(i+" proportion of voxels with p-value above 0.05 (masked): "+str(np.mean(pvals_in_brain > 0.05)))
sys.stdout.write("-")
sys.stdout.flush()
# Save image plots of masked and unmasked p-values for a single subject.
plt.imshow(present_3d(sw_pvals), cmap=plt.get_cmap('gray'))
plt.savefig(location_of_images+i+'sw.png')
np.savetxt(condition_location + "cond_all.txt", cond_all)
neural_prediction = events2neural(condition_location + "cond_all.txt", TR,
n_vols)
convolved = np.convolve(neural_prediction,
hrf_at_trs) # hrf_at_trs sample data
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
np_hrf = convolved[:N]
###################
# From GLM function
###################
np_B, np_X = glm(data, np_hrf)
####################################
# GLM Diagnostics (to get residuals)
###################################
np_MRSS, np_fitted, np_residuals = glm_diagnostics(np_B, np_X, data)
###########################
#Shapiro-Wilks on Residuals
###########################
#Shapiro-Wilks: tests the null hypothesis that the data was
#drawn from a normal distribution.
sw_pvals = check_sw(np_residuals)
print(np.mean(sw_pvals > 0.05))
# Progress bar
toolbar_width = len(sub_list)
sys.stdout.write("Shaprio-Wilk test for normality, : ")
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" *
(toolbar_width + 1)) # return to start of line, after '['
# Set up lists to store proportion of p-values above 0.05.
unmasked_prop = [] # Unmasked (all voxels)
masked_prop = [] # Masked.
for i in sub_list:
residuals = np.load(residual_data + i + "_residual.npy")
sw_pvals = check_sw(residuals)
unmasked_prop.append(np.mean(sw_pvals > 0.05))
mask = nib.load(path_to_data + i + '/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
masked_pvals = make_mask(sw_pvals, mask_data, fit=True)
masked_pvals[masked_pvals > 1] = 1
pvals_in_brain = sw_pvals.ravel()[masked_pvals.ravel() != 0]
masked_prop.append(np.mean(pvals_in_brain > 0.05))
if (i[-3:] == "010"):
# Save image plots of unmasked p-values for subject 10.
plt.imshow(present_3d(sw_pvals), cmap=plt.get_cmap('gray'))
plt.colorbar()
plt.xticks([])
####################################
# GLM Diagnostics (to get residuals)
###################################
np_MRSS, np_fitted, np_residuals = glm_diagnostics(np_B, np_X, data)
###########################
#Shapiro-Wilks on Residuals
###########################
# Shapiro-Wilks: tests the null hypothesis that the data was
# drawn from a normal distribution.
# Using 4-d residuals.
sw_pvals = check_sw(np_residuals)
print("Proportion of voxels with p-value above 0.05 (unmasked): "+str(np.mean(sw_pvals > 0.05)))
# Load mask.
mask = nib.load(pathtodata+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
masked_pvals = make_mask(sw_pvals, mask_data, fit=True)
pvals_in_brain = sw_pvals.ravel()[masked_pvals.ravel() != 0]
print("Proportion of voxels with p-value above 0.05 (masked): "+str(np.mean(pvals_in_brain > 0.05)))
plt.imshow(present_3d(sw_pvals), cmap=plt.get_cmap('gray'))
plt.savefig(location_of_images+'sub001sw.png')
plt.close()
plt.imshow(present_3d(masked_pvals), cmap=plt.get_cmap('gray'))
