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)
