def test_qap_functional_temporal():
import os
import pkg_resources as p
from qap.qap_workflows_utils import qap_functional_temporal
mask_path = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"functional_brain_mask", \
"rest_calc_tshift_resample_volreg" \
"_mask.nii.gz"))
func_path = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"func_motion_correct", \
"rest_calc_tshift_resample_" \
"volreg.nii.gz"))
matrix = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"coordinate_transformation", \
"rest_calc_tshift_resample.aff12.1D"))
subject = "1019436"
session = "session_1"
scan = "rest_1"
qc = qap_functional_temporal(func_path, mask_path, matrix, subject, \
session, scan)
assert (len(qc.keys()) == 10) and (None not in qc.values())
def test_qap_functional_temporal():
import os
import pkg_resources as p
from qap.qap_workflows_utils import qap_functional_temporal
mask_path = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"functional_brain_mask", \
"rest_calc_tshift_resample_volreg" \
"_mask.nii.gz"))
func_path = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"func_motion_correct", \
"rest_calc_tshift_resample_" \
"volreg.nii.gz"))
matrix = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"coordinate_transformation", \
"rest_calc_tshift_resample.aff12.1D"))
subject = "1019436"
session = "session_1"
scan = "rest_1"
qc = qap_functional_temporal(func_path, mask_path, matrix, subject, \
session, scan)
assert (len(qc.keys()) == 10) and (None not in qc.values())
tsnr = tsnr.aggregate_outputs()
os.chdir(cwd)
for out_name in ["tsnr_file", "mean_file", "stddev_file"]:
path = getattr(tsnr, out_name)
tmp_path = os.path.join(subjectdir, "tmp_" + os.path.basename(path))
shutil.copyfile(path, tmp_path)
im = nibabel.load(tmp_path)
data_array = im.get_data()
data_array[numpy.isnan(data_array)] = 0
nibabel.save(im, path)
os.remove(tmp_path)
# > compute functional temporal scores from QAP library
qc = qap_functional_temporal(funcrealign_file, args.maskrealign,
tsnr.tsnr_file,
fd_file, args.subjectid, "mysession", "myscan",
site_name="mysite", motion_threshold=1.0)
# > compute snaps
tsnr_snap = os.path.join(subjectdir, "tsnr_volume.pdf")
figures.append(tsnr_snap)
fig = plot_mosaic(getattr(tsnr, "tsnr_file"), title="tSNR volume")
fig.savefig(tsnr_snap, dpi=300)
fd_snap = os.path.join(subjectdir, "plot_fd.pdf")
figures.append(fd_snap)
fig = plot_fd(fd_file, title="FD plot")
fig.savefig(fd_snap, dpi=300)
# > save scores as a CSV file
scores_json = os.path.join(subjectdir, "qap_functional_temporal.json")
qc.pop("session")
qc.pop("scan")
for out_name in ["tsnr_file", "mean_file", "stddev_file"]:
path = getattr(tsnr, out_name)
tmp_path = os.path.join(subjectdir, "tmp_" + os.path.basename(path))
shutil.copyfile(path, tmp_path)
im = nibabel.load(tmp_path)
data_array = im.get_data()
data_array[numpy.isnan(data_array)] = 0
nibabel.save(im, path)
os.remove(tmp_path)
# > compute functional temporal scores from QAP library
qc = qap_functional_temporal(funcrealign_file,
args.maskrealign,
tsnr.tsnr_file,
fd_file,
args.subjectid,
"mysession",
"myscan",
site_name="mysite",
motion_threshold=args.motion_threshold)
# > compute snaps
tsnr_snap = os.path.join(subjectdir, "tsnr_volume.pdf")
figures.append(tsnr_snap)
fig = plot_mosaic(getattr(tsnr, "tsnr_file"), title="tSNR volume")
fig.savefig(tsnr_snap, dpi=300)
fd_snap = os.path.join(subjectdir, "plot_fd.pdf")
figures.append(fd_snap)
fig = plot_fd(fd_file, title="FD plot")
fig.savefig(fd_snap, dpi=300)
# > save scores as a CSV file
