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()