def _demo_runner(subjects, dataset_id, **spm_realign_kwargs):
"""Demo runner.
Parameters
----------
subjects: iterable for subject data
each subject data can be anything, with a func (string or list
of strings; existing file path(s)) and an output_dir (string,
existing dir path) field
dataset_id: string
a short string describing the data being processed (e.g. "HAXBY!")
Notes
-----
Don't invoke this directly!
"""
# loop over subjects
for subject_data in subjects:
print("%sMotion correction for %s (%s)" % ('\t' * 2,
subject_data.subject_id,
dataset_id))
# instantiate realigner
mrimc = MRIMotionCorrection(**spm_realign_kwargs)
# fit realigner
mrimc.fit(subject_data.func)
# write realigned files to disk
mrimc.transform(subject_data.output_dir, reslice=True, concat=True)
# plot results
for sess, rp_filename in zip(xrange(len(mrimc._rp_filenames_)),
mrimc._rp_filenames_):
plot_spm_motion_parameters(
rp_filename,
title="Estimated motion for %s (session %i) of '%s'" % (
subject_data.subject_id, sess, dataset_id))
plt.show()
def _demo_runner(subjects, dataset_id, **spm_realign_kwargs):
"""Demo runner.
Parameters
----------
subjects: iterable for subject data
each subject data can be anything, with a func (string or list
of strings; existing file path(s)) and an output_dir (string,
existing dir path) field
dataset_id: string
a short string describing the data being processed (e.g. "HAXBY!")
Notes
-----
Don't invoke this directly!
"""
# loop over subjects
for subject_data in subjects:
print("%sMotion correction for %s (%s)" % ('\t' * 2,
subject_data.subject_id,
dataset_id))
# instantiate realigner
mrimc = MRIMotionCorrection(**spm_realign_kwargs)
# fit realigner
mrimc = mem.cache(mrimc.fit)(subject_data.func)
# write realigned files to disk
mem.cache(mrimc.transform)(subject_data.output_dir, reslice=False,
concat=False)
# plot results
for sess, rp_filename in zip(
xrange(len(mrimc.realignment_parameters_)),
mrimc.realignment_parameters_):
plot_spm_motion_parameters(
rp_filename,
title="Estimated motion for %s (session %i) of '%s'" % (
subject_data.subject_id, sess, dataset_id))
prepreproc_undergone="",
caching=True)
return subject_data
##### Start preprocessing for one subject
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:
"""
Plotting PSYDAT alignment results
"""
import os, glob
from pypreprocess.reporting.check_preprocessing import plot_spm_motion_parameters
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)
mid_dir = os.path.join(sp, 'MRI', 'MID')
if os.path.isdir(mid_dir):
# Plot Realign
mvt = glob.glob(os.path.join(mid_dir, '*.txt'))
if len(mvt)>0:
realign_fig = os.path.join(FIG_BASE_DIR, 'realign', subject_id)
plot_spm_motion_parameters(mvt[0],
title=subject_id,
output_filename=realign_fig)