import sys
sys.path.append('/u1/work/hpc3230/ConWhAt_brainhack')
from ConWhAt.volumetric import VolTractAtlas
va = VolTractAtlas('JHU')
import nibabel as nib
img = nib.load(va.atlas_info['files'][0])
dat = img.get_data()
from nilearn.image import index_img
img0 = index_img(img, 0)
new_img = nib.Nifti1Image((img0.get_data() > 20).astype('float'), img0.affine)
new_img.to_filename('lesion_test_file_from_jhu.nii.gz')

72b4947c4ad03e304cc61fd36bd37ff508e90e14
<mask token>

<mask token>
sys.path.append('/u1/work/hpc3230/ConWhAt_brainhack')
<mask token>
new_img.to_filename('lesion_test_file_from_jhu.nii.gz')

<mask token>
sys.path.append('/u1/work/hpc3230/ConWhAt_brainhack')
<mask token>
va = VolTractAtlas('JHU')
<mask token>
img = nib.load(va.atlas_info['files'][0])
dat = img.get_data()
<mask token>
img0 = index_img(img, 0)
new_img = nib.Nifti1Image((img0.get_data() > 20).astype('float'), img0.affine)
new_img.to_filename('lesion_test_file_from_jhu.nii.gz')

import sys
sys.path.append('/u1/work/hpc3230/ConWhAt_brainhack')
from ConWhAt.volumetric import VolTractAtlas
va = VolTractAtlas('JHU')
import nibabel as nib
img = nib.load(va.atlas_info['files'][0])
dat = img.get_data()
from nilearn.image import index_img
img0 = index_img(img, 0)
new_img = nib.Nifti1Image((img0.get_data() > 20).astype('float'), img0.affine)
new_img.to_filename('lesion_test_file_from_jhu.nii.gz')
