def show_nifti(name, nifti_dir=None, save_fig=0, fig_dir=None):
"""
name is the name of the nifti file, without the nii.gz extension
nifti_dir is the path to the directory where the nifti is located. if not provided, looks in current directory.
save_fig is 1 for save nifti fig as png, 0 for don't save
fig_dir is the path to the directory for saving the png. if not provided, saves to nifti_dir
"""
if nifti_dir==None:
nifti_dir = os.getcwd()
im = nib.load(os.path.join(nifti_dir, '{}.nii.gz'.format(name)))
data = im.get_data()
min_val = data[~np.isnan(data)].min()
max_val = data[~np.isnan(data)].max()
fig = visualize.display_slices(data, min_val=min_val, max_val=max_val, cmap=plt.cm.RdBu_r)
fig.suptitle(name)
# plt.show()
if save_fig:
if fig_dir==None:
fig_dir = nifti_dir
fig.savefig(os.path.join(fig_dir,'{}.png'.format(name)))
def show_nifti(name, nifti_dir=None, save_fig=0, fig_dir=None):
"""
name is the name of the nifti file, without the nii.gz extension
nifti_dir is the path to the directory where the nifti is located. if not provided, looks in current directory.
save_fig is 1 for save nifti fig as png, 0 for don't save
fig_dir is the path to the directory for saving the png. if not provided, saves to nifti_dir
"""
if nifti_dir == None:
nifti_dir = os.getcwd()
im = nib.load(os.path.join(nifti_dir, '{}.nii.gz'.format(name)))
data = im.get_data()
min_val = data[~np.isnan(data)].min()
max_val = data[~np.isnan(data)].max()
fig = visualize.display_slices(data,
min_val=min_val,
max_val=max_val,
cmap=plt.cm.RdBu_r)
fig.suptitle(name)
# plt.show()
if save_fig:
if fig_dir == None:
fig_dir = nifti_dir
fig.savefig(os.path.join(fig_dir, '{}.png'.format(name)))
coh_im[coords_indices] = coh
cor_im[coords_indices] = cor
# save the images as niftis
if save_nii:
print 'Saving niftis'
coh_nii = nib.Nifti1Image(coh_im, fmri_data.get_affine())
cor_nii = nib.Nifti1Image(cor_im, fmri_data.get_affine())
coh_nii.to_filename(coh_nii_file)
cor_nii.to_filename(cor_nii_file)
# display the coh and coh maps
fig_coh = visualize.display_slices(coh_im,
min_val=0,
max_val=1,
cmap=plt.cm.RdBu_r)
fig_coh.suptitle('coherence, hemi {0} {1} seed'.format(hemi, roi_name))
fig_cor = visualize.display_slices(cor_im,
min_val=-1,
max_val=1,
cmap=plt.cm.RdBu_r)
fig_cor.suptitle('correlation hemi {0} {1} seed'.format(hemi, roi_name))
# save the figures
if save_fig:
print 'Saving figs'
fig_coh.savefig(coh_fig_file)
fig_cor.savefig(cor_fig_file)
coh_im = np.zeros(volume_shape)
cor_im = np.zeros(volume_shape)
coh_im[coords_indices] = coh
cor_im[coords_indices] = cor
# save the images as niftis
if save_nii:
print 'Saving niftis'
coh_nii = nib.Nifti1Image(coh_im, fmri_data.get_affine())
cor_nii = nib.Nifti1Image(cor_im, fmri_data.get_affine())
coh_nii.to_filename(coh_nii_file)
cor_nii.to_filename(cor_nii_file)
# display the coh and coh maps
fig_coh = visualize.display_slices(coh_im, 0, 1)
fig_coh.suptitle('coherence, {0} seed'.format(roi_name))
plt.show()
fig_cor = visualize.display_slices(cor_im, 0, 1)
fig_cor.suptitle('correlation {0} seed'.format(roi_name))
plt.show()
# save the figures
if save_fig:
print 'Saving figs'
fig_coh.savefig(coh_fig_file)
fig_cor.savefig(cor_fig_file)
cor_im = np.zeros(volume_shape)
coh_im[coords_indices] = coh
cor_im[coords_indices] = cor
# save the images as niftis
if save_nii:
print 'Saving niftis'
coh_nii = nib.Nifti1Image(coh_im, fmri_data.get_affine())
cor_nii = nib.Nifti1Image(cor_im, fmri_data.get_affine())
coh_nii.to_filename(coh_nii_file)
cor_nii.to_filename(cor_nii_file)
# display the coh and coh maps
fig_coh = visualize.display_slices(coh_im, 0, 1)
fig_coh.suptitle('coherence, {0} seed'.format(roi_name))
plt.show()
fig_cor = visualize.display_slices(cor_im, 0, 1)
fig_cor.suptitle('correlation {0} seed'.format(roi_name))
plt.show()
# save the figures
if save_fig:
print 'Saving figs'
fig_coh.savefig(coh_fig_file)
fig_cor.savefig(cor_fig_file)
cor_im = np.zeros(volume_shape)
coh_im[coords_indices] = coh
cor_im[coords_indices] = cor
# save the images as niftis
if save_nii:
print 'Saving niftis'
coh_nii = nib.Nifti1Image(coh_im, fmri_data.get_affine())
cor_nii = nib.Nifti1Image(cor_im, fmri_data.get_affine())
coh_nii.to_filename(coh_nii_file)
cor_nii.to_filename(cor_nii_file)
# display the coh and coh maps
fig_coh = visualize.display_slices(coh_im, min_val=0, max_val=1, cmap=plt.cm.RdBu_r)
fig_coh.suptitle('coherence, hemi {0} {1} seed'.format(hemi, roi_name))
fig_cor = visualize.display_slices(cor_im, min_val=-1, max_val=1, cmap=plt.cm.RdBu_r)
fig_cor.suptitle('correlation hemi {0} {1} seed'.format(hemi, roi_name))
# save the figures
if save_fig:
print 'Saving figs'
fig_coh.savefig(coh_fig_file)
fig_cor.savefig(cor_fig_file)
plt.show()