in_brain_img = make_mask_filtered_data(image_path[1],mask_path)
print("brain image filtered\n")
else:
print("Loading filtered brain image for: ")
print(str(name))
in_brain_img = nib.load(md)
print("brain image loaded\n")
data_int = in_brain_img.get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
template = nib.load(template_path)
template_data_int = template.get_data()
template_data = template_data_int.astype(float)
Transpose = False
in_brain_mask = (mean_data - 0.0) < 0.01
plt.imshow(plot_mosaic(template_data, transpose=Transpose),\
cmap='gray', alpha=1)
else:
img = nib.load(image_path[1])
data_int = img.get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
in_brain_mask = mean_data > thres
Transpose = True
plt.contour(plot_mosaic(in_brain_mask, transpose=Transpose), \
cmap='gray' , alpha=1)
# Smoothing with Gaussian filter
smooth_data = smoothing(data,1,range(data.shape[-1]))
# Selecting the voxels in the brain
in_brain_tcs = smooth_data[in_brain_mask, :]
else:
img = nib.load(image_path[1])
data_int = img.get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
in_brain_mask = mean_data > thres
Transpose = True
# Smoothing with Gaussian filter
smooth_data = smoothing(data,1,range(data.shape[-1]))
# Selecting the voxels in the brain
in_brain_tcs = smooth_data[in_brain_mask, :]
#in_brain_tcs = data[in_brain_mask, :]
vol_shape = data.shape[:-1]
# Plotting the voxels in the brain
plt.imshow(plot_mosaic(mean_data, transpose=Transpose), cmap='gray', alpha=1)
plt.colorbar()
plt.contour(plot_mosaic(in_brain_mask, transpose=Transpose),colors='blue')
plt.title('In brain voxel mean values' + '\n' + (d_path['type'] + str(name)))
plt.savefig(project_path+'fig/BOLD/%s_mean_voxels_countour.png'\
%(d_path['type'] + str(name)))
#plt.show()
plt.clf()
# Convolution with 1 to 4 conditions
convolved = np.zeros((240,5))
for i in range(1,5):
#convolved = np.loadtxt(\
# '../../../txt_output/conv_normal/%s_conv_00%s_canonical.txt'\
# %(str(name),str(i)))
convolved[:,i] = np.loadtxt(\
for mydict in [task, gain, loss]:
mydict['mean'] = sum([task[sub] for sub in subject_list])/(len(subject_list))
mydict['mean_reshape'] = mydict['mean'].reshape(91,109,91)
mydict['mean_mask'] = (mydict['mean_reshape'] - 0.0) < 0.01
mydict['mean_reshape_plot'] = mydict['mean_reshape']
mydict['mean_reshape_plot'][~mydict['mean_mask']] = np.nan
print("Creating plots for the voxel mean accross subjects analysis")
task_mean = task['mean']
gain_mean = gain['mean']
loss_mean = loss['mean']
in_brain_task = task['mean_mask']
in_brain_gain = gain['mean_mask']
in_brain_loss = loss['mean_mask']
plt.title('In brain activated voxels - \nmean across ' + str(len(subject_list)) +' subjects on TASK condition', fontsize=12)
plt.imshow(plot_mosaic(template_data, transpose=False), cmap='gray', alpha=1)
plt.imshow(plot_mosaic(task['mean_reshape_plot'], transpose=False), \
cmap='seismic', alpha=1, vmin=task['mean_reshape_plot'].min(), vmax= task['mean_reshape_plot'].max())
plt.colorbar()
plt.savefig(dirs[1]+'/mean_task.png')
#plt.show()
plt.clf()
print(" Plot for the TASK condition saved in " + dirs[1]+'/mean_task.png')
plt.title('In brain activated voxels - \nmean across ' + str(len(subject_list)) + ' subjects on GAIN condition', fontsize=12)
plt.imshow(plot_mosaic(template_data, transpose=False), cmap='gray', alpha=1)
plt.imshow(plot_mosaic(gain['mean_reshape_plot'], transpose=False), \
cmap='seismic', alpha=1, vmin=gain['mean_reshape_plot'].min(), vmax= gain['mean_reshape_plot'].max())
plt.colorbar()
plt.savefig(dirs[1]+'/mean_gain.png')
#plt.show()
+ 'task001_run%s%s/%s' %(r.zfill(3),d_path['feat'],\
d_path['bold_img_name'])) \
for r in run_list \
for s in subject_list]
for image_path in images_paths:
name = image_path[0]
data_int = nib.load(image_path[1]).get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
# Plot
if d_path['type'] == 'filtered':
Transpose = False
template_data_int = nib.load(template_path).get_data()
template_data = template_data_int.astype(float)
plt.imshow(\
plot_mosaic(template_data, transpose=Transpose), \
cmap='gray', alpha=1)
else:
in_brain_mask = mean_data > 375
Transpose = True
plt.contour(\
plot_mosaic(in_brain_mask, transpose=Transpose), \
cmap='gray' , alpha=1)
plt.imshow(\
plot_mosaic(mean_data, transpose=Transpose), cmap='gray', alpha=1)
plt.colorbar()
plt.title('Voxels mean values' + '\n' + (d_path['type'] + str(name)))
plt.savefig(project_path+'fig/BOLD/%s_mean_voxels.png'\
%(d_path['type'] + str(name)))
#plt.show()
plt.clf()
data_path + 'sub%s/'%(s.zfill(3)) + d_path['run_path'] \
+ 'task001_run%s%s/%s' %(r.zfill(3),d_path['feat'],\
d_path['bold_img_name'])) \
for r in run_list \
for s in subject_list]
for image_path in images_paths:
name = image_path[0]
data_int = nib.load(image_path[1]).get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
# Plot
if d_path['type']=='filtered':
Transpose=False
template_data = nib.load(template_path).get_data()
plt.imshow(\
plot_mosaic(template_data, transpose=Transpose), \
cmap='gray', alpha=1)
else:
in_brain_mask = mean_data > 375
Transpose=True
plt.contour(\
plot_mosaic(in_brain_mask, transpose=Transpose), \
cmap='gray' , alpha=1)
plt.imshow(\
plot_mosaic(mean_data, transpose=Transpose), cmap='gray', alpha=1)
plt.colorbar()
plt.title('Voxels mean values' + '\n' + (d_path['type'] + str(name)))
plt.savefig(project_path+'fig/BOLD/%s_mean_voxels.png'\
%(d_path['type'] + str(name)))
#plt.show()
plt.clf()
X_matrix = np.ones((data.shape[-1], p))
#build our design matrix
for cond in range(1,5):
convolved = np.loadtxt(txt_path + name + '_conv_' + str(cond).zfill(3) + '_high_res.txt')
#convolved = np.loadtxt(txt_path + name + '_conv_' + str(cond).zfill(3) + '_canonical.txt')
X_matrix[:,cond] = convolved
linear_drift = np.linspace(-1, 1, n_trs)
X_matrix[:,5] = linear_drift
quadratic_drift = linear_drift ** 2
quadratic_drift -= np.mean(quadratic_drift)
X_matrix[:,6] = quadratic_drift
beta, t, df, p = t_stat(smooth_data, X_matrix)
for cond in range(0,4):
print("Starting test for condition " + str(cond+1))
t_newshape = np.reshape(t[cond,:],vol_shape)
t_newshape[~in_brain_mask]=np.nan
t_T = np.zeros(vol_shape)
for z in range(vol_shape[2]):
t_T[:, :, z] = t_newshape[:,:, z].T
t_plot = plot_mosaic(t_T)
plt.imshow(t_plot,interpolation='nearest', cmap='seismic')
zero_out=max(abs(np.nanmin(t_T)),np.nanmax(t_T))
plt.title(name+'_t_statistics'+'_cond_'+'_%s'%(cond+1))
plt.clim(-zero_out,zero_out)
plt.colorbar()
plt.savefig(dirs[1]+'/'+ name +'_t-test_'+'cond'+str(cond+1)+'.png')
plt.close()
print("\nT-test analysis and plots done for selected subjects")
print("See mosaic plots in project-epsilon/fig/t-test/")
else:
img = nib.load(image_path[1])
data_int = img.get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
in_brain_mask = mean_data > thres
Transpose = True
# Smoothing with Gaussian filter
smooth_data = smoothing(data, 1, range(data.shape[-1]))
# Selecting the voxels in the brain
in_brain_tcs = smooth_data[in_brain_mask, :]
#in_brain_tcs = data[in_brain_mask, :]
vol_shape = data.shape[:-1]
# Plotting the voxels in the brain
plt.imshow(plot_mosaic(mean_data, transpose=Transpose),
cmap='gray',
alpha=1)
plt.colorbar()
plt.contour(plot_mosaic(in_brain_mask, transpose=Transpose), colors='blue')
plt.title('In brain voxel mean values' + '\n' +
(d_path['type'] + str(name)))
plt.savefig(project_path+'fig/BOLD/%s_mean_voxels_countour.png'\
%(d_path['type'] + str(name)))
#plt.show()
plt.clf()
# Convolution with 1 to 4 conditions
convolved = np.zeros((240, 5))
for i in range(1, 5):
#convolved = np.loadtxt(\
gain_sum += gain[x]
gain_mean = gain_sum / 16
gain_mean_reshape = gain_mean.reshape(91, 109, 91)
loss_sum = gain[1]
for x in range(2, 17):
loss_sum += loss[x]
loss_mean = loss_sum / 16
loss_mean_reshape = loss_mean.reshape(91, 109, 91)
plt.title(
'In brain activated voxels - \nmean across 16 subjects on TASK condition',
fontsize=12)
plt.imshow(plot_mosaic(template_data, transpose=False), cmap='gray', alpha=1)
plt.imshow(plot_mosaic(task_mean_reshape, transpose=False),
cmap='seismic',
alpha=1,
vmin=task_mean_reshape.min(),
vmax=task_mean_reshape.max())
plt.colorbar()
plt.savefig(dirs[1] + '/mean_task.png')
plt.clf()
plt.title(
'In brain activated voxels - \nmean across 16 subjects on GAIN condition',
fontsize=12)
plt.imshow(plot_mosaic(template_data, transpose=False), cmap='gray', alpha=1)
plt.imshow(plot_mosaic(gain_mean_reshape, transpose=False),
cmap='seismic',
in_brain_img = make_mask_filtered_data(image_path[1], mask_path)
print("brain image filtered\n")
else:
print("Loading filtered brain image for: ")
print(str(name))
in_brain_img = nib.load(md)
print("brain image loaded\n")
data_int = in_brain_img.get_data()
data = data_int.astype(float)
mean_data = np.mean(data, axis=-1)
template = nib.load(template_path)
template_data_int = template.get_data()
template_data = template_data_int.astype(float)
Transpose = False
in_brain_mask = (mean_data - 0.0) < 0.01
plt.imshow(plot_mosaic(template_data, transpose=Transpose),\
cmap='gray', alpha=1)
else:
img = nib.load(image_path[1])
data = img.get_data()
mean_data = np.mean(data, axis=-1)
in_brain_mask = mean_data > thres
Transpose = True
plt.contour(plot_mosaic(in_brain_mask, transpose=Transpose), \
cmap='gray' , alpha=1)
# Smoothing with Gaussian filter
smooth_data = smoothing(data, 1, range(data.shape[-1]))
# Selecting the voxels in the brain
in_brain_tcs = smooth_data[in_brain_mask, :]
#in_brain_tcs = data[in_brain_mask, :]
