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()