def _run_demo(func, anat): # fit coreg = Coregister().fit(anat, func) # apply coreg VFk = coreg.transform(func) # QA plot_registration(anat, VFk, title="before coreg") plot_registration(VFk, anat, title="after coreg") plt.show()
def _run_demo(func, anat): # fit coreg = Coregister().fit(anat, func) # apply coreg VFk = coreg.transform(func) # QA plot_registration(anat, VFk, title="before coreg") plot_registration(VFk, anat, title="after coreg") plt.show()
subject_data = do_preproc(funcfile, anatfile, args.subject, decimate=False) subject_data.func # #### Perform some quality checks for one subject # # First we can check the motion parameters: plot_spm_motion_parameters( subject_data.nipype_results["realign"].outputs.realignment_parameters, title="Realign: estimated motion.") plt.savefig(os.path.join(outdir, filename_prefix + '_motion_params.png')) plt.close() # We can also check the coregistration: plot_registration( anatfile, subject_data.nipype_results["coreg"].outputs.coregistered_files, close=False, cut_coords=(-10, 20, 5), title="Coregister outline.") plt.savefig(os.path.join(outdir, filename_prefix + '_coregistration.png')) plt.close() # And the normalization: from pypreprocess.nipype_preproc_spm_utils import SPM_T1_TEMPLATE plot_registration( SPM_T1_TEMPLATE, subject_data.nipype_results["normalize"].outputs.normalized_source, close=False, cut_coords=(-10, 0, 5), title="Normalization outline.") plt.savefig(os.path.join(outdir, filename_prefix + '_normalization.png'))
from pypreprocess import coreg BASE_DIR = os.path.join('/', 'shfj', 'Ppsypim', 'PSYDAT', 'Subjects') FIG_BASE_DIR = os.path.join('figures') subject_path_list = glob.glob(os.path.join(BASE_DIR, 'S*')) for sp in subject_path_list: root, subject_id = os.path.split(sp) # Plot Coreg mid_dir = os.path.join(sp, 'MRI', 'MID') fmri = glob.glob(os.path.join(mid_dir, '[se]*.nii')) t1_dir = os.path.join(sp, 'MRI', 'T1MRI') t1mri = glob.glob(os.path.join(t1_dir, '*.hdr')) coreg_fig = os.path.join(FIG_BASE_DIR, 'coregister', subject_id) if len(fmri)>0 and len(t1mri)>0: c = coreg.Coregister() c.fit(fmri[0], t1mri[0]) transf = c.transform(t1mri[0]) plot_registration(transf, fmri[0], title=subject_id, output_filename=coreg_fig)