def fg_from_trk(trk_file, affine=None):
"""
Read data from a trackvis .trk file and create a FiberGroup object
according to the information in it.
"""
# Generate right away, since we're going to do it anyway:
read_trk = tv.read(trk_file, as_generator=False)
fibers_trk = read_trk[0]
# Per default read from the affine from the file header:
if affine is not None:
aff = affine
else:
hdr = read_trk[1]
aff = tv.aff_from_hdr(hdr)
# If the header contains a bogus affine, we revert to np.eye(4), so we
# don't get into trouble later:
try:
np.matrix(aff).getI()
except np.linalg.LinAlgError:
e_s = "trk file contains bogus header, reverting to np.eye(4)"
warnings.warn(e_s)
aff = np.eye(4)
fibers = []
for f in fibers_trk:
fibers.append(ozf.Fiber(np.array(f[0]).T, affine=aff))
return ozf.FiberGroup(fibers, affine=aff)
def fg_from_trk(trk_file, affine=None):
"""
Read data from a trackvis .trk file and create a FiberGroup object
according to the information in it.
"""
# Generate right away, since we're going to do it anyway:
read_trk = tv.read(trk_file, as_generator=False)
fibers_trk = read_trk[0]
# Per default read from the affine from the file header:
if affine is not None:
aff = affine
else:
hdr = read_trk[1]
aff= tv.aff_from_hdr(hdr)
# If the header contains a bogus affine, we revert to np.eye(4), so we
# don't get into trouble later:
try:
np.matrix(aff).getI()
except np.linalg.LinAlgError:
e_s = "trk file contains bogus header, reverting to np.eye(4)"
warnings.warn(e_s)
aff = np.eye(4)
fibers = []
for f in fibers_trk:
fibers.append(ozf.Fiber(np.array(f[0]).T,affine=aff))
return ozf.FiberGroup(fibers, affine=aff)
def _run_interface(self, runtime):
# Loading the ROI file
import nibabel as nib
import numpy as np
from dipy.tracking import utils
img = nib.load(self.inputs.ROI_file)
data = img.get_data()
affine = img.get_affine()
# Getting the FA file
img = nib.load(self.inputs.FA_file)
FA_data = img.get_data()
FA_affine = img.get_affine()
# Loading the streamlines
from nibabel import trackvis
streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
streamlines = [s[0] for s in streams]
streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)
# Checking for negative values
from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
endpoints = [sl[0::len(sl)-1] for sl in streamlines]
lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
inds = np.dot(endpoints, lin_T)
inds += offset
negative_values = np.where(inds <0)[0]
for negative_value in sorted(negative_values, reverse=True):
del streamlines[negative_value]
# Constructing the streamlines matrix
matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
matrix[matrix < 10] = 0
# Constructing the FA matrix
dimensions = matrix.shape
FA_matrix = np.empty(shape=dimensions)
for i in range(0,dimensions[0]):
for j in range(0,dimensions[1]):
if matrix[i,j]:
dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
FA_matrix[i,j] = np.mean(FA_data[dm>0])
else:
FA_matrix[i,j] = 0
FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)
from nipype.utils.filemanip import split_filename
_, base, _ = split_filename(self.inputs.trackfile)
np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
return runtime
def _run_interface(self, runtime):
# Loading the ROI file
from dipy.tracking import utils
import nibabel as nib
import numpy as np
import os
img = nib.load(self.inputs.ROI_file)
data = img.get_data()
affine = img.get_affine()
# Getting ROI volumes if they haven't been generated
if not os.path.isfile('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv'):
import nibabel as nib
import numpy as np
import os
import pandas as pd
import subprocess
atlas_file = ROI_file
img = nib.load(atlas_file)
data = img.get_data()
affine = img.get_affine()
volumes = pd.DataFrame()
atlas_labels = np.unique(data)
for atlas_label in atlas_labels:
data = nib.load(atlas_file).get_data()
data[data != atlas_label] = 0
data[data == atlas_label] = 1
nib.save(nib.Nifti1Image(data, affine), 'temp.nii.gz')
volumes.set_value(atlas_label, 'volume', subprocess.check_output(os.environ['FSLDIR'] + '/bin/fslstats temp.nii.gz -V', shell=True).split(' ')[0])
os.remove('temp.nii.gz')
volumes.to_csv('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')
ROI_volumes = pd.read_csv('/home/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')
# Getting the FA file
img = nib.load(self.inputs.FA_file)
FA_data = img.get_data()
FA_affine = img.get_affine()
# Loading the streamlines
from nibabel import trackvis
streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
streamlines = [s[0] for s in streams]
streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)
# Checking for negative values
from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
endpoints = [sl[0::len(sl)-1] for sl in streamlines]
lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
inds = np.dot(endpoints, lin_T)
inds += offset
negative_values = np.where(inds <0)[0]
for negative_value in sorted(negative_values, reverse=True):
del streamlines[negative_value]
# Constructing the streamlines matrix
matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
matrix[matrix < 10] = 0
# Constructing the FA matrix
dimensions = matrix.shape
FA_matrix = np.empty(shape=dimensions)
density_matrix = np.empty(shape=dimensions)
density_corrected_matrix = np.empty(shape=dimensions)
for i in range(0,dimensions[0]):
for j in range(0,dimensions[1]):
if matrix[i,j]:
dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
FA_matrix[i,j] = np.mean(FA_data[dm>0])
if np.sum(dm > 0) > 0:
density_matrix[i,j] = np.sum(dm[dm > 0])
density_corrected_matrix[i,j] = np.sum(dm[dm > 0])/np.sum([ROI_volumes.iloc[i].values.astype('int'), ROI_volumes.iloc[j].values.astype('int')])
else:
density_matrix[i,j] = 0
density_corrected_matrix[i,j] = 0
else:
FA_matrix[i,j] = 0
density_matrix[i,j] = 0
density_corrected_matrix[i,j] = 0
FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)
density_matrix[np.tril_indices(n=len(density_matrix))] = 0
density_matrix = density_matrix.T + density_matrix - np.diagonal(density_matrix)
density_corrected_matrix[np.tril_indices(n=len(density_corrected_matrix))] = 0
density_corrected_matrix = density_corrected_matrix.T + density_corrected_matrix - np.diagonal(density_corrected_matrix)
from nipype.utils.filemanip import split_filename
_, base, _ = split_filename(self.inputs.trackfile)
np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
np.savetxt(base + '_density_matrix.txt',density_matrix,delimiter='\t')
np.savetxt(base + '_volume_corrected_density_matrix.txt',density_corrected_matrix,delimiter='\t')
