def test_fd_jenkinson():
import os
import pickle
import pkg_resources as p
from qap.temporal_qc import fd_jenkinson
coord_xfm = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"coordinate_transformation", \
"rest_calc_tshift_resample.aff12.1D"))
ref_out = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"fd_jenkinson", \
"FD_J.1D"))
meanfd = fd_jenkinson(coord_xfm)
# do da check
with open(meanfd, "r") as f:
test_fd_lines = f.readlines()
with open(ref_out, "r") as f:
ref_fd_lines = f.readlines()
os.system("rm FD_J.1D")
assert test_fd_lines == ref_fd_lines
def test_fd_jenkinson():
import os
import pickle
import pkg_resources as p
from qap.temporal_qc import fd_jenkinson
coord_xfm = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"coordinate_transformation", \
"rest_calc_tshift_resample.aff12.1D"))
ref_out = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"fd_jenkinson", \
"FD_J.1D"))
meanfd = fd_jenkinson(coord_xfm)
# do da check
with open(meanfd,"r") as f:
test_fd_lines = f.readlines()
with open(ref_out,"r") as f:
ref_fd_lines = f.readlines()
os.system("rm FD_J.1D")
assert test_fd_lines == ref_fd_lines
def test_fd_jenkinson():
import os
import numpy as np
import pkg_resources as p
from qap.temporal_qc import fd_jenkinson
coord_xfm = p.resource_filename("qap", os.path.join(test_sub_dir, \
"coordinate_transformation.aff12.1D"))
ref_meanfd = p.resource_filename("qap", os.path.join(test_sub_dir, \
"meanFD.1D"))
meanfd = fd_jenkinson(coord_xfm, out_array=True)
ref_meanfd_arr = np.genfromtxt(ref_meanfd)
np.testing.assert_array_equal(ref_meanfd_arr, meanfd)
100 * float(intersect), 2))}
"""
QAP temporal
"""
# > Jenkinson Frame Displacement (FD) (Jenkinson et al., 2002)
fd_file = os.path.join(subjectdir, "fd.txt")
r12_file = os.path.join(
subjectdir, "r12_" + os.path.basename(args.transformations))
rparams = numpy.loadtxt(args.transformations)
r12 = []
for rigid_params in rparams:
r12.append(get_rigid_matrix(rigid_params, "SPM")[:-1].ravel())
r12 = numpy.asarray(r12)
numpy.savetxt(r12_file, r12)
fd_jenkinson(r12_file, rmax=80., out_file=fd_file)
# > computes the time-course SNR for a time series,
# typically you want to run this on a realigned time-series.
funcrealign_file = os.path.join(
subjectdir, "nonan_" + os.path.basename(args.funcrealign))
im = nibabel.load(args.funcrealign)
data_array = im.get_data()
data_array[numpy.isnan(data_array)] = 0
nibabel.save(im, funcrealign_file)
cwd = os.getcwd()
os.chdir(subjectdir)
tsnr = nam.TSNR()
tsnr.inputs.in_file = funcrealign_file
tsnr.run()
tsnr = tsnr.aggregate_outputs()
"signal_loss": sl_value
}
"""
QAP temporal
"""
# > Jenkinson Frame Displacement (FD) (Jenkinson et al., 2002)
fd_file = os.path.join(subjectdir, "fd.txt")
r12_file = os.path.join(subjectdir,
"r12_" + os.path.basename(args.transformations))
rparams = numpy.loadtxt(args.transformations)
r12 = []
for rigid_params in rparams:
r12.append(get_rigid_matrix(rigid_params, args.package)[:-1].ravel())
r12 = numpy.asarray(r12)
numpy.savetxt(r12_file, r12)
fd_jenkinson(r12_file, rmax=80., out_file=fd_file)
# > computes the time-course SNR for a time series,
# typically you want to run this on a realigned time-series.
funcrealign_file = os.path.join(subjectdir,
"nonan_" + os.path.basename(args.funcrealign))
im = nibabel.load(args.funcrealign)
data_array = im.get_data()
data_array[numpy.isnan(data_array)] = 0
nibabel.save(im, funcrealign_file)
cwd = os.getcwd()
os.chdir(subjectdir)
tsnr = nam.TSNR()
tsnr.inputs.in_file = funcrealign_file
tsnr.run()
tsnr = tsnr.aggregate_outputs()
