def extract_region(atlas_file, label):
from nipype.interfaces.base import CommandLine
from nipype.pipeline.engine import Node
import os
from glob import glob
node = Node(CommandLine('fslmaths %s -thr %s -uthr %s region_%s.nii.gz' %
(atlas_file, label, label, label)),
name='extract_roi')
cwd = os.getcwd()
print("cwd = ", cwd)
node.base_dir = cwd
node.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
node.run()
single_region = os.path.realpath(
os.path.join(cwd, 'extract_roi', 'region_%s.nii.gz' % label))
print('single region mask file: ', single_region)
assert os.path.exists(single_region)
return single_region, label, atlas_file
node.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
node.run()
single_region = os.path.realpath(
os.path.join(cwd, 'extract_roi', 'region_%s.nii.gz' % label))
print('single region mask file: ', single_region)
assert os.path.exists(single_region)
return single_region, label, atlas_file
region_extracter = Node(Function(
input_names=['atlas_file', 'label'],
output_names=['single_region', 'label', 'atlas_file'],
function=extract_region),
name='region_extracter')
region_extracter.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
# filter streamlines by seed region of interest
def sl_filter(streamlines, target_mask, affine, label):
from dipy.tracking.utils import target
from nilearn.image import resample_img
import numpy as np
import os
import nibabel as nib
trk_file = nib.streamlines.load(streamlines)
streams = trk_file.streamlines
hdr = trk_file.header
# resample mask to resolution of input data & get data