qc[key] = float(value)
if isinstance(value, numpy.core.memmap):
if value.dtype == numpy.single or value.dtype == numpy.double:
qc[key] = float(value)
elif value.dtype == numpy.int:
qc[key] = int(value)
else:
raise ValueError("Unexpected value type '{0}:{1}'".format(key,
value))
with open(scores_json, "w") as open_file:
json.dump(qc, open_file, indent=4)
# Control FOV
mean_snap = os.path.join(subjectdir, "mean_epi.pdf")
figures.append(mean_snap)
fig = plot_mosaic(args.meanepi, title="Mean EPI")
fig.savefig(mean_snap, dpi=300)
# Control wrapping on template
mean_snap_norm = os.path.join(subjectdir, "mean_normalized_epi_masked.pdf")
figures.append(mean_snap_norm)
fig = plot_mosaic(args.wrappedmean,
title="Mean wrapped EPI",
overlay_mask=args.basemask)
fig.savefig(mean_snap_norm, dpi=300)
# compute overlapping score between template mask and mean mask
# load masks
subject_mask_data = nibabel.load(args.meanwrappedmask).get_data()
template_mask_data = nibabel.load(args.basemask).get_data()
"spm_funcnormalize_template": ["wau{}.nii", "pyscript_normalize.m"],
"smoothing": ["pyscript_smooth.m", "swau{}.nii"],
}
else:
raise NotImplementedError("Can't reorganize the data.")
image_basename = os.path.basename(funcfile).split(".")[0]
for step_name, step_fnames in processing_struct.items():
for basename in step_fnames:
basename = basename.format(image_basename)
srcpath = os.path.join(tmp_capsul, step_name, basename)
if srcpath.endswith(".nii"):
gzip_file(srcpath, prefix="", output_directory=outdir)
else:
if (step_name == "spm_funcnormalize_template") and (basename == "pyscript_normalize.m"):
destpath = os.path.join(outdir, "pyscript_apply_normalize.m")
elif step_name == "":
destpath = os.path.join(logdir, "{0}_{1}".format(sid, basename))
else:
destpath = os.path.join(outdir, basename)
shutil.move(srcpath, destpath)
shutil.rmtree(tmp_capsul)
"""
Create a snap to chack the registration result
"""
if args.template_mask is not None:
snap = os.path.join(outdir, image_basename + ".pdf")
image = os.path.join(outdir, "wmeanau{}.nii.gz".format(image_basename))
fig = plot_mosaic(image, overlay_mask=args.template_mask, title="SPM template registration")
fig.savefig(snap, dpi=300)
qc[key] = float(value)
if isinstance(value, numpy.core.memmap):
if value.dtype == numpy.single or value.dtype == numpy.double:
qc[key] = float(value)
elif value.dtype == numpy.int:
qc[key] = int(value)
else:
raise ValueError("Unexpected value type '{0}:{1}'".format(
key, value))
with open(scores_json, "w") as open_file:
json.dump(qc, open_file, indent=4)
# Control FOV
mean_snap = os.path.join(subjectdir, "mean_epi.pdf")
figures.append(mean_snap)
fig = plot_mosaic(args.meanepi, title="Mean EPI")
fig.savefig(mean_snap, dpi=300)
fov_coverage, fov_score = control_fov(args.maskrealign, threshold=0, verbose=0)
if args.slmask:
# measure signal loss in brain region (e.g. lower anterior quadrant)
sl_value = signal_loss(args.wrappedmean,
args.meanwrappedmask,
args.slmask,
verbose=0)
else:
sl_value = None
# Control wrapping on template
if args.wrappedmean:
mean_snap_norm = os.path.join(subjectdir, "mean_normalized_epi_masked.pdf")
