def getonefile(files, name):
merger = Merge()
merger.inputs.in_files = files
merger.inputs.dimension = "t"
merger.inputs.tr = 2.00
merger.inputs.output_type = "NIFTI_GZ"
merger.inputs.merged_file = "/media/phoenix/SeagateDrive/Dataset/Outputs/Belief_Updating/Higher_level_inputs/%s.nii.gz" % (
name)
merger.run()
def weird_convert_dti_dcm(in_dcm):
import os
import numpy as np
import re
subjid = re.search('R[0-9X]+', in_dcm).group()
year = re.search('_201[1234]', in_dcm).group()[1:]
visit_dict = {'2012': 1, '2013': 2, '2014': 3, '2011': 4}
visit = visit_dict[year]
scanid = re.search('S[0-9]+', in_dcm).group()
ton_dir = '/data1/cooked/TONf'
test_fn = os.path.join(ton_dir, subjid, 'visit_{}'.format(visit), 'DTI',
'_'.join([subjid, 'visit', str(visit), 'DTI',
scanid])) + '.bvals'
if os.path.exists(test_fn):
assert np.all(np.loadtxt(test_fn) != 0)
converter = Dcm2nii()
converter.inputs.source_names = in_dcm
converter.inputs.gzip_output = True
converter.inputs.output_dir = os.getcwd()
converter.run()
merger = Merge()
merger.inputs.in_files = converter.output_files
merger.inputs.dimension = 't'
merged_result = merger.run()
fn_base = os.path.basename(in_dcm).split('.')[0]
merged_file = os.path.join(os.getcwd(), fn_base + '.nii.gz')
os.rename(merged_result.outputs.merged_file, merged_file)
in_bval = converter.bvals[0]
in_bvec = converter.bvecs[0]
b0_idx = 0
assert np.all(np.loadtxt(in_bval) != 0)
# Load (and transpose!!)
bvec_arr = np.loadtxt(in_bvec).T
out_bvec = np.zeros((bvec_arr.shape[0] + 1,
bvec_arr.shape[1]))
out_bvec[:] = np.nan
out_bvec[b0_idx, :] = 0
out_bvec[np.where(np.isnan(out_bvec))] = bvec_arr.flatten()
bval_arr = np.loadtxt(in_bval)
out_bval = np.zeros((bval_arr.shape[0] + 1,))
out_bval[:] = np.nan
out_bval[b0_idx] = 0
out_bval[np.isnan(out_bval)] = bval_arr
out_bvec_fn = os.path.join(os.getcwd(), fn_base + '.bvecs')
np.savetxt(out_bvec_fn, out_bvec, fmt='%.8f')
out_bval_fn = os.path.join(os.getcwd(), fn_base + '.bvals')
np.savetxt(out_bval_fn, out_bval, fmt='%.6f')
return merged_file, out_bvec_fn, out_bval_fn
def __init__(self, in_files=['path'],
dimension="enumerate(('t','x','y','z','a'))", **options):
from nipype.interfaces.fsl import Merge
fu = Merge()
fu.inputs.in_files = in_files
fu.inputs.dimension = dimension
for ef in options:
setattr(fu.inputs, ef, options[ef])
self.res = fu.run()
def merge_files(output_dir, subject, file_list):
merger = Merge()
merger.inputs.in_files = [os.path.join(output_dir, subject, f) for f in file_list]
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.merged_file = os.path.join(output_dir, subject, 'DWI_concat.nii.gz')
result = merger.run()
print('Merging complete')
return None
def fslmath_Merge(filelist, output_prefix):
from nipype.interfaces.fsl import Merge
import os
mergeM = Merge()
mergeM.inputs.in_files = filelist
mergeM.inputs.dimension = 't'
mergeM.inputs.output_type = 'NIFTI_GZ'
print "Merge [" + os.path.basename(filelist[0]) + ".." + os.path.basename(filelist[len(filelist)-1]) + "]:" + mergeM.cmdline
res = mergeM.run()
outfile = d2s.move_to_results(res.outputs.merged_file, output_prefix)
return outfile
def coregistration_4D(source_file, ref, out_file=None, spm_path=None):
'''
Coregistration with spm + fsl for 4D files.
Why? Nor SPM, nor fsl are able to do this by default
:param source_file: path to input 4D file
:param ref: reference file to co-register the source-file to
:param out_file: output file
:param spm_path: path to spm
:return: path to coregistered file
'''
if spm_path is not None:
mlab.MatlabCommand.set_default_paths(spm_path)
if spm.SPMCommand().version is None:
raise Exception('SPM path not set correctly:', spm_path,
spm.SPMCommand().version)
main_dir, source_file_name = os.path.split(source_file)
if out_file is None:
out_file = os.path.join(main_dir, 'r' + source_file_name)
split_folder = os.path.join(main_dir, '4D_split')
if not os.path.exists(os.path.join(main_dir, '4D_split')):
os.mkdir(split_folder)
split = Split(in_file=source_file, dimension='t')
split.inputs.in_file = source_file
split.inputs.dimension = 't'
split.inputs.out_base_name = os.path.join(split_folder, '4D_vol_')
split.inputs.output_type = 'NIFTI'
split = split.run()
split_files = split.outputs.out_files
index_file = split_files.pop(0)
coreg = spm.Coregister()
coreg.inputs.target = ref
coreg.inputs.source = index_file
coreg.inputs.apply_to_files = split_files
coreg = coreg.run()
merger = Merge()
merger.inputs.in_files = coreg.outputs.coregistered_files
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.merged_file = out_file
merger = merger.run()
shutil.rmtree(split_folder)
return merger.outputs.merged_file
if run1Sample:
for fb in [0,1]:
x=[]
for subj in subject_list:
fbLoc=subjectinfo(subj,fb)
fname= '/home/jmuraskin/Projects/CCD/CPAC-out/pipeline_CCD_v1/%s_data_/dr_tempreg_maps_files_to_standard_smooth/_scan_feedback_%d/_csf_threshold_0.96/_gm_threshold_0.7/_wm_threshold_0.96/_compcor_ncomponents_5_selector_pc10.linear1.wm0.global0.motion1.quadratic1.gm0.compcor1.csf1/_spatial_map_PNAS_Smith09_rsn10/_fwhm_6/_dr_tempreg_maps_files_smooth_03/temp_reg_map_0003_antswarp_maths.nii.gz' % (subj,fbLoc)
# fname = '/home/jmuraskin/Projects/CCD/CPAC-out/pipeline_CCD_v1/%s_data_/dr_tempreg_maps_files_to_standard_smooth/_scan_feedback_%d/%s%d.nii.gz' % (fbLoc,subj,t,i)
x.append(fname)
subjs = len(x)
merger = Merge()
merger.inputs.in_files = x
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.merged_file = './DMN_merged_%s.nii.gz' % fbNames[fb]
merger.run()
#get meanFD values for each subject and add as covariate
meanFD=zscore(motionTest[motionTest.FB==fbNames[fb]][motionTest.Subject_ID.isin(subject_list)]['meanFD'])
model = MultipleRegressDesign()
model.inputs.contrasts = [['group mean', 'T',['reg1'],[1]],['group neg mean', 'T',['reg1'],[-1]]]
model.inputs.regressors = dict(reg1=list(motionTest[motionTest.FB==fbNames[fb]][motionTest.Subject_ID.isin(subject_list)]['scanorder']-1.5),FD=list(meanFD))
model.run()
if runWithRandomise:
os.mkdir(fbNames[fb])
randomiseCommand='./randomise_forpython.sh -i %s -o ./%s/fb -D -d design.mat -t design.con -e design.grp -m %s -T -n %d' % ('DMN_merged_%s.nii.gz' % fbNames[fb],fbNames[fb],'/usr/share/fsl/5.0/data/standard/MNI152_T1_3mm_brain_mask.nii.gz',nperms)
os.system(randomiseCommand)
shutil.move(fbNames[fb],pairedFolder)
shutil.move('DMN_merged_%s.nii.gz' % fbNames[fb],pairedFolder)
import os
tr = int(
subprocess.check_output(['cat', subject_dir + '/rest/Rest_TR.txt'
]).strip('\n').strip(' ').split('.')[0])
print(nvols, tr)
print(len(outlier_vols))
print('Length outliers: ' + str(len(outlier_vols)))
proc = subprocess.Popen(['fslsplit', str(bptf)])
proc.wait()
s = []
for i in range(nvols):
if i not in outlier_vols:
s += ['vol' + str(i).zfill(4) + '.nii.gz']
merger = Merge()
merger.inputs.in_files = s
merger.inputs.dimension = 't'
merger.inputs.tr = tr
merger.run()
wait10()
for j in s:
os.remove(j)
temp_ofile = os.path.join(subject_dir,
s[0].replace('.nii.gz', '_merged.nii.gz'))
if os.path.exists(temp_ofile):
os.rename(temp_ofile, 'RestVW_bptf_good_vols.nii.gz')
def gnl_correction(input,
file_bash,
file_coeff,
python3_env,
python2_env,
path_output,
cleanup=True):
"""
The purpose of the following function is to correct for gradient nonlinearities. A corrected
file is written using spline interpolation. The function needs FSL to be included in the search
path.
Inputs:
*input: filename of input image.
*file_bash: filename of bash script which calls the gradient unwarping toolbox.
*file_coeff: filename of siemens coefficient file.
*python3_env: name of python3 virtual environment.
*python2_env: name of python2 virtual environment.
*path_output: path where output is written.
*cleanup: delete intermediate files.
created by Daniel Haenelt
Date created: 10-01-2020
Last modified: 10-01-2020
"""
import os
import shutil as sh
import numpy as np
import nibabel as nb
from nipype.interfaces.fsl import ConvertWarp, Merge
from nipype.interfaces.fsl.maths import MeanImage
from nipype.interfaces.fsl.preprocess import ApplyWarp
from lib.io.get_filename import get_filename
from lib.cmap.generate_coordinate_mapping import generate_coordinate_mapping
# get fileparts
path, name, ext = get_filename(input)
# make subfolders
path_grad = os.path.join(path_output, "grad")
if not os.path.exists(path_grad):
os.makedirs(path_grad)
# parse arguments
file_output = os.path.join(path_output, name + "_gnlcorr" + ext)
file_warp = os.path.join(path_grad, "warp.nii.gz")
file_jacobian = os.path.join(path_grad, "warp_jacobian.nii.gz")
# run gradient unwarp
os.system("bash " + file_bash + \
" " + python3_env + \
" " + python2_env + \
" " + path_grad + \
" " + input + \
" trilinear.nii.gz" + \
" " + file_coeff)
# now create an appropriate warpfield output (relative convention)
convertwarp = ConvertWarp()
convertwarp.inputs.reference = os.path.join(path_grad, "trilinear.nii.gz")
convertwarp.inputs.warp1 = os.path.join(path_grad, "fullWarp_abs.nii.gz")
convertwarp.inputs.abswarp = True
convertwarp.inputs.out_relwarp = True
convertwarp.inputs.out_file = file_warp
convertwarp.inputs.args = "--jacobian=" + file_jacobian
convertwarp.run()
# convertwarp's jacobian output has 8 frames, each combination of one-sided differences, so average them
calcmean = MeanImage()
calcmean.inputs.in_file = file_jacobian
calcmean.inputs.dimension = "T"
calcmean.inputs.out_file = file_jacobian
calcmean.run()
# apply warp to first volume
applywarp = ApplyWarp()
applywarp.inputs.in_file = input
applywarp.inputs.ref_file = input
applywarp.inputs.relwarp = True
applywarp.inputs.field_file = file_warp
applywarp.inputs.output_type = "NIFTI"
applywarp.inputs.out_file = file_output
applywarp.inputs.interp = "spline"
applywarp.run()
# normalise warped output image to initial intensity range
data_img = nb.load(input)
data_array = data_img.get_fdata()
max_data = np.max(data_array)
min_data = np.min(data_array)
data_img = nb.load(file_output)
data_array = data_img.get_fdata()
data_array[data_array < min_data] = 0
data_array[data_array > max_data] = max_data
output = nb.Nifti1Image(data_array, data_img.affine, data_img.header)
nb.save(output, file_output)
# calculate gradient deviations
os.system("calc_grad_perc_dev" + \
" --fullwarp=" + file_warp + \
" -o " + os.path.join(path_grad,"grad_dev"))
# merge directions
merger = Merge()
merger.inputs.in_files = [
os.path.join(path_grad, "grad_dev_x.nii.gz"),
os.path.join(path_grad, "grad_dev_y.nii.gz"),
os.path.join(path_grad, "grad_dev_z.nii.gz")
]
merger.inputs.dimension = 't'
merger.inputs.merged_file = os.path.join(path_grad, "grad_dev.nii.gz")
merger.run()
# convert from % deviation to absolute
data_img = nb.load(os.path.join(path_grad, "grad_dev.nii.gz"))
data_array = data_img.get_fdata()
data_array = data_array / 100
output = nb.Nifti1Image(data_array, data_img.affine, data_img.header)
nb.save(output, os.path.join(path_grad, "grad_dev.nii.gz"))
# warp coordinate mapping
generate_coordinate_mapping(input,
0,
path_grad,
suffix="gnl",
time=False,
write_output=True)
applywarp = ApplyWarp()
applywarp.inputs.in_file = os.path.join(path_grad, "cmap_gnl.nii")
applywarp.inputs.ref_file = input
applywarp.inputs.relwarp = True
applywarp.inputs.field_file = file_warp
applywarp.inputs.out_file = os.path.join(path_grad, "cmap_gnl.nii")
applywarp.inputs.interp = "trilinear"
applywarp.inputs.output_type = "NIFTI"
applywarp.run()
# clean intermediate files
if cleanup:
sh.rmtree(path_grad, ignore_errors=True)