def create_clean_mask(self, num_std_dev=1.5):
"""
Create a subject-refined version of the clustering mask.
"""
import os
from pynets.core import utils
from nilearn.masking import intersect_masks
from nilearn.image import index_img, math_img, resample_img
mask_name = os.path.basename(self.clust_mask).split('.nii')[0]
self.atlas = "%s%s%s%s%s" % (mask_name, '_', self.clust_type, '_k', str(self.k))
print("%s%s%s%s%s%s%s" % ('\nCreating atlas using ', self.clust_type, ' at cluster level ', str(self.k),
' for ', str(self.atlas), '...\n'))
self._dir_path = utils.do_dir_path(self.atlas, self.func_file)
self.uatlas = "%s%s%s%s%s%s%s%s" % (self._dir_path, '/', mask_name, '_clust-', self.clust_type, '_k',
str(self.k), '.nii.gz')
# Load clustering mask
self._func_img.set_data_dtype(np.float32)
func_vol_img = index_img(self._func_img, 1)
func_vol_img.set_data_dtype(np.uint16)
clust_mask_res_img = resample_img(nib.load(self.clust_mask), target_affine=func_vol_img.affine,
target_shape=func_vol_img.shape, interpolation='nearest')
clust_mask_res_img.set_data_dtype(np.uint16)
func_data = np.asarray(func_vol_img.dataobj).astype('float32')
func_int_thr = np.round(np.mean(func_data[func_data > 0]) - np.std(func_data[func_data > 0]) * num_std_dev, 3)
if self.mask is not None:
self._mask_img = nib.load(self.mask)
self._mask_img.set_data_dtype(np.uint16)
mask_res_img = resample_img(self._mask_img, target_affine=func_vol_img.affine,
target_shape=func_vol_img.shape, interpolation='nearest')
mask_res_img.set_data_dtype(np.uint16)
self._clust_mask_corr_img = intersect_masks([math_img('img > ' + str(func_int_thr), img=func_vol_img),
math_img('img > 0.01', img=clust_mask_res_img),
math_img('img > 0.01', img=mask_res_img)],
threshold=1, connected=False)
self._clust_mask_corr_img.set_data_dtype(np.uint16)
self._mask_img.uncache()
mask_res_img.uncache()
else:
self._clust_mask_corr_img = intersect_masks([math_img('img > ' + str(func_int_thr), img=func_vol_img),
math_img('img > 0.01', img=clust_mask_res_img)],
threshold=1, connected=False)
self._clust_mask_corr_img.set_data_dtype(np.uint16)
nib.save(self._clust_mask_corr_img, "%s%s%s%s" % (self._dir_path, '/', mask_name, '.nii.gz'))
del func_data
func_vol_img.uncache()
clust_mask_res_img.uncache()
return self.atlas
def test_do_dir_path():
"""
Test do_dir_path functionality
"""
base_dir = str(Path(__file__).parent/"examples")
func_path = base_dir + '/002/fmri'
dwi_path = base_dir + '/002/dmri'
in_func = func_path + '/002.nii.gz'
in_dwi = dwi_path + '/std_dmri/iso_eddy_corrected_data_denoised_pre_reor.nii.gz'
in_files = [in_func, in_dwi]
atlases = ['Power', 'Shirer', 'Shen', 'Smith']
for inputs in in_files:
for atlas in atlases:
dir_path = utils.do_dir_path(atlas, inputs)
assert dir_path is not None
def test_do_dir_path(atlas, input):
"""
Test do_dir_path functionality
"""
base_dir = os.path.abspath(
pkg_resources.resource_filename("pynets", "../data/examples"))
if input == 'fmri':
in_file = f"{base_dir}/003/func/sub-003_ses-01_task-rest_bold.nii.gz"
elif input == 'dmri':
in_file = f"{base_dir}/003/dmri/sub-003_dwi.nii.gz"
# Delete existing atlas dirs in in_file parent
dir_path = utils.do_dir_path(
atlas, f"{os.path.dirname(os.path.realpath(in_file))}")
assert dir_path is not None
def test_do_dir_path(atlas, input):
"""
Test do_dir_path functionality
"""
import tempfile
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path)
base_dir = str(Path(__file__).parent / "examples")
if input == 'fmri':
in_file = f"{base_dir}/BIDS/sub-25659/ses-1/func/sub-25659_ses-1_task-rest_space-T1w_desc-preproc_bold.nii.gz"
elif input == 'dmri':
in_file = f"{base_dir}/BIDS/sub-25659/ses-1/dwi/final_preprocessed_dwi.nii.gz"
# Delete existing atlas dirs in in_file parent
atlas_dir = os.path.dirname(os.path.realpath(in_file)) + '/' + str(atlas)
dir_path = utils.do_dir_path(atlas, atlas_dir)
assert dir_path is not None
def test_do_dir_path(atlas, input):
"""
Test do_dir_path functionality
"""
base_dir = str(Path(__file__).parent / "examples")
if input == 'fmri':
func_path = base_dir + '/002/fmri'
in_file = func_path + '/002.nii.gz'
elif input == 'dmri':
dwi_path = base_dir + '/002/dmri'
in_file = dwi_path + '/std_dmri/iso_eddy_corrected_data_denoised_pre_reor.nii.gz'
# Delete existing atlas dirs in in_file parent
atlas_dir = os.path.dirname(os.path.realpath(in_file)) + '/' + str(atlas)
if os.path.exists(atlas_dir):
shutil.move(atlas_dir, atlas_dir + '_tmp')
dir_path = utils.do_dir_path(atlas, in_file)
assert dir_path is not None
# Restore oringal atlas dir
if os.path.exists(atlas_dir + '_tmp'):
shutil.move(atlas_dir + '_tmp', atlas_dir)
def create_clean_mask(self, num_std_dev=1.5):
"""
Create a subject-refined version of the clustering mask.
"""
import os
from pynets.core import utils
from nilearn.masking import intersect_masks
from nilearn.image import index_img, math_img, resample_img
mask_name = os.path.basename(self.clust_mask).split(".nii")[0]
self.atlas = f"{mask_name}{'_'}{self.clust_type}{'_k'}{str(self.k)}"
print(f"\nCreating atlas using {self.clust_type} at cluster level"
f" {str(self.k)} for {str(self.atlas)}...\n")
self._dir_path = utils.do_dir_path(self.atlas, self.outdir)
self.parcellation = f"{self._dir_path}/{mask_name}_" \
f"clust-{self.clust_type}" \
f"_k{str(self.k)}.nii.gz"
# Load clustering mask
self._func_img.set_data_dtype(np.float32)
func_vol_img = index_img(self._func_img, 1)
func_vol_img.set_data_dtype(np.uint16)
clust_mask_res_img = resample_img(
nib.load(self.clust_mask),
target_affine=func_vol_img.affine,
target_shape=func_vol_img.shape,
interpolation="nearest",
)
clust_mask_res_img.set_data_dtype(np.uint16)
func_data = np.asarray(func_vol_img.dataobj, dtype=np.float32)
func_int_thr = np.round(
np.mean(func_data[func_data > 0]) -
np.std(func_data[func_data > 0]) * num_std_dev,
3,
)
if self.mask is not None:
self._mask_img = nib.load(self.mask)
self._mask_img.set_data_dtype(np.uint16)
mask_res_img = resample_img(
self._mask_img,
target_affine=func_vol_img.affine,
target_shape=func_vol_img.shape,
interpolation="nearest",
)
mask_res_img.set_data_dtype(np.uint16)
self._clust_mask_corr_img = intersect_masks(
[
math_img(f"img > {func_int_thr}", img=func_vol_img),
math_img("img > 0.01", img=clust_mask_res_img),
math_img("img > 0.01", img=mask_res_img),
],
threshold=1,
connected=False,
)
self._clust_mask_corr_img.set_data_dtype(np.uint16)
self._mask_img.uncache()
mask_res_img.uncache()
else:
self._clust_mask_corr_img = intersect_masks(
[
math_img("img > " + str(func_int_thr), img=func_vol_img),
math_img("img > 0.01", img=clust_mask_res_img),
],
threshold=1,
connected=False,
)
self._clust_mask_corr_img.set_data_dtype(np.uint16)
nib.save(self._clust_mask_corr_img,
f"{self._dir_path}{'/'}{mask_name}{'.nii.gz'}")
del func_data
func_vol_img.uncache()
clust_mask_res_img.uncache()
return self.atlas
def _run_interface(self, runtime):
import gc
import os
import time
import os.path as op
from dipy.io import load_pickle
from colorama import Fore, Style
from dipy.data import get_sphere
from pynets.core import utils
from pynets.core.utils import load_runconfig
from pynets.dmri.estimation import reconstruction
from pynets.dmri.track import (
create_density_map,
track_ensemble,
)
from dipy.io.stateful_tractogram import Space, StatefulTractogram, \
Origin
from dipy.io.streamline import save_tractogram
from nipype.utils.filemanip import copyfile, fname_presuffix
hardcoded_params = load_runconfig()
use_life = hardcoded_params['tracking']["use_life"][0]
roi_neighborhood_tol = hardcoded_params['tracking'][
"roi_neighborhood_tol"][0]
sphere = hardcoded_params['tracking']["sphere"][0]
target_samples = hardcoded_params['tracking']["tracking_samples"][0]
dir_path = utils.do_dir_path(self.inputs.atlas,
os.path.dirname(self.inputs.dwi_file))
namer_dir = "{}/tractography".format(dir_path)
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
# Load diffusion data
dwi_file_tmp_path = fname_presuffix(self.inputs.dwi_file,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.dwi_file,
dwi_file_tmp_path,
copy=True,
use_hardlink=False)
dwi_img = nib.load(dwi_file_tmp_path, mmap=True)
dwi_data = dwi_img.get_fdata(dtype=np.float32)
# Load FA data
fa_file_tmp_path = fname_presuffix(self.inputs.fa_path,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.fa_path,
fa_file_tmp_path,
copy=True,
use_hardlink=False)
fa_img = nib.load(fa_file_tmp_path, mmap=True)
labels_im_file_tmp_path = fname_presuffix(self.inputs.labels_im_file,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.labels_im_file,
labels_im_file_tmp_path,
copy=True,
use_hardlink=False)
# Load B0 mask
B0_mask_tmp_path = fname_presuffix(self.inputs.B0_mask,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.B0_mask,
B0_mask_tmp_path,
copy=True,
use_hardlink=False)
streams = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
runtime.cwd,
"/streamlines_",
"%s" % (self.inputs.subnet +
"_" if self.inputs.subnet is not None else ""),
"%s" % (op.basename(self.inputs.roi).split(".")[0] +
"_" if self.inputs.roi is not None else ""),
self.inputs.conn_model,
"_",
target_samples,
"_",
"%s" % ("%s%s" % (self.inputs.node_radius, "mm_") if
((self.inputs.node_radius != "parc") and
(self.inputs.node_radius is not None)) else "parc_"),
"curv-",
str(self.inputs.curv_thr_list).replace(", ", "_"),
"_step-",
str(self.inputs.step_list).replace(", ", "_"),
"_traversal-",
self.inputs.traversal,
"_minlength-",
self.inputs.min_length,
".trk",
)
if os.path.isfile(f"{namer_dir}/{op.basename(streams)}"):
from dipy.io.streamline import load_tractogram
copyfile(
f"{namer_dir}/{op.basename(streams)}",
streams,
copy=True,
use_hardlink=False,
)
tractogram = load_tractogram(
streams,
fa_img,
bbox_valid_check=False,
)
streamlines = tractogram.streamlines
# Create streamline density map
try:
[dir_path, dm_path] = create_density_map(
fa_img,
dir_path,
streamlines,
self.inputs.conn_model,
self.inputs.node_radius,
self.inputs.curv_thr_list,
self.inputs.step_list,
self.inputs.subnet,
self.inputs.roi,
self.inputs.traversal,
self.inputs.min_length,
namer_dir,
)
except BaseException:
print('Density map failed. Check tractography output.')
dm_path = None
del streamlines, tractogram
fa_img.uncache()
dwi_img.uncache()
gc.collect()
self._results["dm_path"] = dm_path
self._results["streams"] = streams
recon_path = None
else:
# Fit diffusion model
# Save reconstruction to .npy
recon_path = "%s%s%s%s%s%s%s%s" % (
runtime.cwd,
"/reconstruction_",
"%s" % (self.inputs.subnet +
"_" if self.inputs.subnet is not None else ""),
"%s" % (op.basename(self.inputs.roi).split(".")[0] +
"_" if self.inputs.roi is not None else ""),
self.inputs.conn_model,
"_",
"%s" % ("%s%s" % (self.inputs.node_radius, "mm") if
((self.inputs.node_radius != "parc") and
(self.inputs.node_radius is not None)) else "parc"),
".hdf5",
)
gtab_file_tmp_path = fname_presuffix(self.inputs.gtab_file,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.gtab_file,
gtab_file_tmp_path,
copy=True,
use_hardlink=False)
gtab = load_pickle(gtab_file_tmp_path)
# Only re-run the reconstruction if we have to
if not os.path.isfile(f"{namer_dir}/{op.basename(recon_path)}"):
import h5py
model = reconstruction(
self.inputs.conn_model,
gtab,
dwi_data,
B0_mask_tmp_path,
)[0]
with h5py.File(recon_path, 'w') as hf:
hf.create_dataset("reconstruction",
data=model.astype('float32'),
dtype='f4')
hf.close()
copyfile(
recon_path,
f"{namer_dir}/{op.basename(recon_path)}",
copy=True,
use_hardlink=False,
)
time.sleep(2)
del model
elif os.path.getsize(f"{namer_dir}/{op.basename(recon_path)}") > 0:
print(f"Found existing reconstruction with "
f"{self.inputs.conn_model}. Loading...")
copyfile(
f"{namer_dir}/{op.basename(recon_path)}",
recon_path,
copy=True,
use_hardlink=False,
)
time.sleep(5)
else:
import h5py
model = reconstruction(
self.inputs.conn_model,
gtab,
dwi_data,
B0_mask_tmp_path,
)[0]
with h5py.File(recon_path, 'w') as hf:
hf.create_dataset("reconstruction",
data=model.astype('float32'),
dtype='f4')
hf.close()
copyfile(
recon_path,
f"{namer_dir}/{op.basename(recon_path)}",
copy=True,
use_hardlink=False,
)
time.sleep(5)
del model
dwi_img.uncache()
del dwi_data
# Load atlas wm-gm interface reduced version for seeding
labels_im_file_tmp_path_wm_gm_int = fname_presuffix(
self.inputs.labels_im_file_wm_gm_int,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.labels_im_file_wm_gm_int,
labels_im_file_tmp_path_wm_gm_int,
copy=True,
use_hardlink=False)
t1w2dwi_tmp_path = fname_presuffix(self.inputs.t1w2dwi,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.t1w2dwi,
t1w2dwi_tmp_path,
copy=True,
use_hardlink=False)
gm_in_dwi_tmp_path = fname_presuffix(self.inputs.gm_in_dwi,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.gm_in_dwi,
gm_in_dwi_tmp_path,
copy=True,
use_hardlink=False)
vent_csf_in_dwi_tmp_path = fname_presuffix(
self.inputs.vent_csf_in_dwi,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.vent_csf_in_dwi,
vent_csf_in_dwi_tmp_path,
copy=True,
use_hardlink=False)
wm_in_dwi_tmp_path = fname_presuffix(self.inputs.wm_in_dwi,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.wm_in_dwi,
wm_in_dwi_tmp_path,
copy=True,
use_hardlink=False)
if self.inputs.waymask:
waymask_tmp_path = fname_presuffix(self.inputs.waymask,
suffix="_tmp",
newpath=runtime.cwd)
copyfile(self.inputs.waymask,
waymask_tmp_path,
copy=True,
use_hardlink=False)
else:
waymask_tmp_path = None
# Iteratively build a list of streamlines for each ROI while
# tracking
print(f"{Fore.GREEN}Target streamlines per iteration: "
f"{Fore.BLUE} "
f"{target_samples}")
print(Style.RESET_ALL)
print(f"{Fore.GREEN}Curvature threshold(s): {Fore.BLUE} "
f"{self.inputs.curv_thr_list}")
print(Style.RESET_ALL)
print(f"{Fore.GREEN}Step size(s): {Fore.BLUE} "
f"{self.inputs.step_list}")
print(Style.RESET_ALL)
print(f"{Fore.GREEN}Tracking type: {Fore.BLUE} "
f"{self.inputs.track_type}")
print(Style.RESET_ALL)
if self.inputs.traversal == "prob":
print(f"{Fore.GREEN}Direction-getting type: {Fore.BLUE}"
f"Probabilistic")
elif self.inputs.traversal == "clos":
print(f"{Fore.GREEN}Direction-getting type: "
f"{Fore.BLUE}Closest Peak")
elif self.inputs.traversal == "det":
print(f"{Fore.GREEN}Direction-getting type: "
f"{Fore.BLUE}Deterministic Maximum")
else:
raise ValueError("Direction-getting type not recognized!")
print(Style.RESET_ALL)
# Commence Ensemble Tractography
try:
streamlines = track_ensemble(
target_samples, labels_im_file_tmp_path_wm_gm_int,
labels_im_file_tmp_path, recon_path, get_sphere(sphere),
self.inputs.traversal, self.inputs.curv_thr_list,
self.inputs.step_list,
self.inputs.track_type, self.inputs.maxcrossing,
int(roi_neighborhood_tol), self.inputs.min_length,
waymask_tmp_path, B0_mask_tmp_path, t1w2dwi_tmp_path,
gm_in_dwi_tmp_path, vent_csf_in_dwi_tmp_path,
wm_in_dwi_tmp_path, self.inputs.tiss_class)
gc.collect()
except BaseException as w:
print(f"\n{Fore.RED}Tractography failed: {w}")
print(Style.RESET_ALL)
streamlines = None
if streamlines is not None:
# import multiprocessing
# from pynets.core.utils import kill_process_family
# return kill_process_family(int(
# multiprocessing.current_process().pid))
# Linear Fascicle Evaluation (LiFE)
if use_life is True:
print('Using LiFE to evaluate streamline plausibility...')
from pynets.dmri.utils import \
evaluate_streamline_plausibility
dwi_img = nib.load(dwi_file_tmp_path)
dwi_data = dwi_img.get_fdata(dtype=np.float32)
orig_count = len(streamlines)
if self.inputs.waymask:
mask_data = nib.load(waymask_tmp_path).get_fdata(
).astype('bool').astype('int')
else:
mask_data = nib.load(wm_in_dwi_tmp_path).get_fdata(
).astype('bool').astype('int')
try:
streamlines = evaluate_streamline_plausibility(
dwi_data,
gtab,
mask_data,
streamlines,
sphere=sphere)
except BaseException:
print(f"Linear Fascicle Evaluation failed. "
f"Visually checking streamlines output "
f"{namer_dir}/{op.basename(streams)} is "
f"recommended.")
if len(streamlines) < 0.5 * orig_count:
raise ValueError('LiFE revealed no plausible '
'streamlines in the tractogram!')
del dwi_data, mask_data
# Save streamlines to trk
stf = StatefulTractogram(streamlines,
fa_img,
origin=Origin.NIFTI,
space=Space.VOXMM)
stf.remove_invalid_streamlines()
save_tractogram(
stf,
streams,
)
del stf
copyfile(
streams,
f"{namer_dir}/{op.basename(streams)}",
copy=True,
use_hardlink=False,
)
# Create streamline density map
try:
[dir_path, dm_path] = create_density_map(
dwi_img,
dir_path,
streamlines,
self.inputs.conn_model,
self.inputs.node_radius,
self.inputs.curv_thr_list,
self.inputs.step_list,
self.inputs.subnet,
self.inputs.roi,
self.inputs.traversal,
self.inputs.min_length,
namer_dir,
)
except BaseException:
print('Density map failed. Check tractography output.')
dm_path = None
del streamlines
dwi_img.uncache()
gc.collect()
self._results["dm_path"] = dm_path
self._results["streams"] = streams
else:
self._results["streams"] = None
self._results["dm_path"] = None
tmp_files = [
gtab_file_tmp_path, wm_in_dwi_tmp_path, gm_in_dwi_tmp_path,
vent_csf_in_dwi_tmp_path, t1w2dwi_tmp_path
]
for j in tmp_files:
if j is not None:
if os.path.isfile(j):
os.system(f"rm -f {j} &")
self._results["track_type"] = self.inputs.track_type
self._results["conn_model"] = self.inputs.conn_model
self._results["dir_path"] = dir_path
self._results["subnet"] = self.inputs.subnet
self._results["node_radius"] = self.inputs.node_radius
self._results["dens_thresh"] = self.inputs.dens_thresh
self._results["ID"] = self.inputs.ID
self._results["roi"] = self.inputs.roi
self._results["min_span_tree"] = self.inputs.min_span_tree
self._results["disp_filt"] = self.inputs.disp_filt
self._results["parc"] = self.inputs.parc
self._results["prune"] = self.inputs.prune
self._results["atlas"] = self.inputs.atlas
self._results["parcellation"] = self.inputs.parcellation
self._results["labels"] = self.inputs.labels
self._results["coords"] = self.inputs.coords
self._results["norm"] = self.inputs.norm
self._results["binary"] = self.inputs.binary
self._results["atlas_t1w"] = self.inputs.atlas_t1w
self._results["curv_thr_list"] = self.inputs.curv_thr_list
self._results["step_list"] = self.inputs.step_list
self._results["fa_path"] = fa_file_tmp_path
self._results["traversal"] = self.inputs.traversal
self._results["labels_im_file"] = labels_im_file_tmp_path
self._results["min_length"] = self.inputs.min_length
tmp_files = [B0_mask_tmp_path, dwi_file_tmp_path]
for j in tmp_files:
if j is not None:
if os.path.isfile(j):
os.system(f"rm -f {j} &")
# Exercise caution when deleting copied recon_path
# if recon_path is not None:
# if os.path.isfile(recon_path):
# os.remove(recon_path)
return runtime
def _run_interface(self, runtime):
import gc
import numpy as np
import nibabel as nib
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.io import load_pickle
from colorama import Fore, Style
from dipy.data import get_sphere
from pynets.core import utils
from pynets.dmri.track import prep_tissues, reconstruction, create_density_map, track_ensemble
# Load diffusion data
dwi_img = nib.load(self.inputs.dwi_file)
# Fit diffusion model
mod_fit = reconstruction(self.inputs.conn_model, load_pickle(self.inputs.gtab_file),
np.asarray(dwi_img.dataobj), self.inputs.B0_mask)
# Load atlas parcellation (and its wm-gm interface reduced version for seeding)
atlas_data = np.array(nib.load(self.inputs.labels_im_file).dataobj).astype('uint16')
atlas_data_wm_gm_int = np.asarray(nib.load(self.inputs.labels_im_file_wm_gm_int).dataobj).astype('uint16')
# Build mask vector from atlas for later roi filtering
parcels = []
i = 0
for roi_val in np.unique(atlas_data)[1:]:
parcels.append(atlas_data == roi_val)
i = i + 1
if np.sum(atlas_data) == 0:
raise ValueError(
'ERROR: No non-zero voxels found in atlas. Check any roi masks and/or wm-gm interface images '
'to verify overlap with dwi-registered atlas.')
# Iteratively build a list of streamlines for each ROI while tracking
print("%s%s%s%s" % (Fore.GREEN, 'Target number of samples: ', Fore.BLUE, self.inputs.target_samples))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Using curvature threshold(s): ', Fore.BLUE, self.inputs.curv_thr_list))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Using step size(s): ', Fore.BLUE, self.inputs.step_list))
print(Style.RESET_ALL)
print("%s%s%s%s" % (Fore.GREEN, 'Tracking type: ', Fore.BLUE, self.inputs.track_type))
print(Style.RESET_ALL)
if self.inputs.directget == 'prob':
print("%s%s%s%s" % (Fore.GREEN, 'Direction-getting type: ', Fore.BLUE, 'Probabilistic'))
elif self.inputs.directget == 'boot':
print("%s%s%s%s" % (Fore.GREEN, 'Direction-getting type: ', Fore.BLUE, 'Bootstrapped'))
elif self.inputs.directget == 'closest':
print("%s%s%s%s" % (Fore.GREEN, 'Direction-getting type: ', Fore.BLUE, 'Closest Peak'))
elif self.inputs.directget == 'det':
print("%s%s%s%s" % (Fore.GREEN, 'Direction-getting type: ', Fore.BLUE, 'Deterministic Maximum'))
else:
raise ValueError('Direction-getting type not recognized!')
print(Style.RESET_ALL)
# Commence Ensemble Tractography
streamlines = track_ensemble(np.asarray(dwi_img.dataobj), self.inputs.target_samples, atlas_data_wm_gm_int,
parcels, mod_fit,
prep_tissues(self.inputs.t1w2dwi, self.inputs.gm_in_dwi,
self.inputs.vent_csf_in_dwi, self.inputs.wm_in_dwi,
self.inputs.tiss_class),
get_sphere(self.inputs.sphere), self.inputs.directget, self.inputs.curv_thr_list,
self.inputs.step_list, self.inputs.track_type, self.inputs.maxcrossing,
int(self.inputs.roi_neighborhood_tol), self.inputs.min_length, self.inputs.waymask)
# Create streamline density map
[streams, dir_path, dm_path] = create_density_map(dwi_img, utils.do_dir_path(self.inputs.atlas,
self.inputs.dwi_file), streamlines,
self.inputs.conn_model, self.inputs.target_samples,
self.inputs.node_size, self.inputs.curv_thr_list,
self.inputs.step_list, self.inputs.network, self.inputs.roi,
self.inputs.directget, self.inputs.min_length)
self._results['streams'] = streams
self._results['track_type'] = self.inputs.track_type
self._results['target_samples'] = self.inputs.target_samples
self._results['conn_model'] = self.inputs.conn_model
self._results['dir_path'] = dir_path
self._results['network'] = self.inputs.network
self._results['node_size'] = self.inputs.node_size
self._results['dens_thresh'] = self.inputs.dens_thresh
self._results['ID'] = self.inputs.ID
self._results['roi'] = self.inputs.roi
self._results['min_span_tree'] = self.inputs.min_span_tree
self._results['disp_filt'] = self.inputs.disp_filt
self._results['parc'] = self.inputs.parc
self._results['prune'] = self.inputs.prune
self._results['atlas'] = self.inputs.atlas
self._results['uatlas'] = self.inputs.uatlas
self._results['labels'] = self.inputs.labels
self._results['coords'] = self.inputs.coords
self._results['norm'] = self.inputs.norm
self._results['binary'] = self.inputs.binary
self._results['atlas_mni'] = self.inputs.atlas_mni
self._results['curv_thr_list'] = self.inputs.curv_thr_list
self._results['step_list'] = self.inputs.step_list
self._results['fa_path'] = self.inputs.fa_path
self._results['dm_path'] = dm_path
self._results['directget'] = self.inputs.directget
self._results['labels_im_file'] = self.inputs.labels_im_file
self._results['roi_neighborhood_tol'] = self.inputs.roi_neighborhood_tol
self._results['min_length'] = self.inputs.min_length
del streamlines, atlas_data_wm_gm_int, atlas_data, mod_fit, parcels
dwi_img.uncache()
gc.collect()
return runtime
def _run_interface(self, runtime):
from pynets.core import utils, nodemaker
from nipype.utils.filemanip import fname_presuffix, copyfile
from nilearn.image import concat_imgs
import pandas as pd
import time
import textwrap
from pathlib import Path
import os.path as op
import glob
base_path = utils.get_file()
# Test if atlas is a nilearn atlas. If so, fetch coords, labels, and/or
# networks.
nilearn_parc_atlases = [
"atlas_harvard_oxford",
"atlas_aal",
"atlas_destrieux_2009",
"atlas_talairach_gyrus",
"atlas_talairach_ba",
"atlas_talairach_lobe",
]
nilearn_coords_atlases = ["coords_power_2011", "coords_dosenbach_2010"]
nilearn_prob_atlases = ["atlas_msdl", "atlas_pauli_2017"]
local_atlases = [
op.basename(i).split(".nii")[0]
for i in glob.glob(f"{str(Path(base_path).parent.parent)}"
f"/templates/atlases/*.nii.gz")
if "_4d" not in i
]
if self.inputs.parcellation is None and self.inputs.atlas in \
nilearn_parc_atlases:
[labels, networks_list, parcellation
] = nodemaker.nilearn_atlas_helper(self.inputs.atlas,
self.inputs.parc)
if parcellation:
if not isinstance(parcellation, str):
nib.save(
parcellation, f"{runtime.cwd}"
f"{self.inputs.atlas}{'.nii.gz'}")
parcellation = f"{runtime.cwd}" \
f"{self.inputs.atlas}{'.nii.gz'}"
if self.inputs.clustering is False:
[parcellation,
labels] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation, label_names=labels)
[coords, atlas, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
else:
raise FileNotFoundError(
f"\nAtlas file for {self.inputs.atlas} not found!")
atlas = self.inputs.atlas
elif (self.inputs.parcellation is None and self.inputs.parc is False
and self.inputs.atlas in nilearn_coords_atlases):
print("Fetching coords and labels from nilearn coordinate-based"
" atlas library...")
# Fetch nilearn atlas coords
[coords, _, networks_list,
labels] = nodemaker.fetch_nilearn_atlas_coords(self.inputs.atlas)
parcels_4d = None
par_max = None
atlas = self.inputs.atlas
parcellation = None
label_intensities = None
elif (self.inputs.parcellation is None and self.inputs.parc is False
and self.inputs.atlas in nilearn_prob_atlases):
import matplotlib
matplotlib.use("agg")
from nilearn.plotting import find_probabilistic_atlas_cut_coords
print("Fetching coords and labels from nilearn probabilistic atlas"
" library...")
# Fetch nilearn atlas coords
[labels, networks_list, parcellation
] = nodemaker.nilearn_atlas_helper(self.inputs.atlas,
self.inputs.parc)
coords = find_probabilistic_atlas_cut_coords(maps_img=parcellation)
if parcellation:
if not isinstance(parcellation, str):
nib.save(
parcellation, f"{runtime.cwd}"
f"{self.inputs.atlas}{'.nii.gz'}")
parcellation = f"{runtime.cwd}" \
f"{self.inputs.atlas}{'.nii.gz'}"
if self.inputs.clustering is False:
[parcellation,
labels] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation, label_names=labels)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
else:
raise FileNotFoundError(
f"\nAtlas file for {self.inputs.atlas} not found!")
par_max = None
atlas = self.inputs.atlas
label_intensities = None
elif self.inputs.parcellation is None and self.inputs.atlas in \
local_atlases:
parcellation_pre = (
f"{str(Path(base_path).parent.parent)}/templates/atlases/"
f"{self.inputs.atlas}.nii.gz")
parcellation = fname_presuffix(parcellation_pre,
newpath=runtime.cwd)
copyfile(parcellation_pre,
parcellation,
copy=True,
use_hardlink=False)
try:
par_img = nib.load(parcellation)
except indexed_gzip.ZranError as e:
print(
e, "\nCannot load subnetwork reference image. "
"Do you have git-lfs installed?")
try:
if self.inputs.clustering is False:
[parcellation, _] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation)
# Fetch user-specified atlas coords
[coords, _, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
# Describe user atlas coords
print(f"\n{self.inputs.atlas} comes with {par_max} parcels\n")
except ValueError as e:
print(
e, "Either you have specified the name of an atlas that "
"does not exist in the nilearn or local repository or "
"you have not supplied a 3d atlas parcellation image!")
labels = None
networks_list = None
atlas = self.inputs.atlas
elif self.inputs.parcellation:
if self.inputs.clustering is True:
while True:
if op.isfile(self.inputs.parcellation):
break
else:
print("Waiting for atlas file...")
time.sleep(5)
try:
parcellation_tmp_path = fname_presuffix(
self.inputs.parcellation, newpath=runtime.cwd)
copyfile(self.inputs.parcellation,
parcellation_tmp_path,
copy=True,
use_hardlink=False)
# Fetch user-specified atlas coords
if self.inputs.clustering is False:
[parcellation,
_] = nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation_tmp_path)
else:
parcellation = parcellation_tmp_path
[coords, atlas, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
atlas = utils.prune_suffices(atlas)
# Describe user atlas coords
print(f"\n{atlas} comes with {par_max} parcels\n")
except ValueError as e:
print(
e, "Either you have specified the name of an atlas that "
"does not exist in the nilearn or local repository or "
"you have not supplied a 3d atlas parcellation image!")
labels = None
networks_list = None
else:
raise ValueError(
"Either you have specified the name of an atlas that does"
" not exist in the nilearn or local repository or you have"
" not supplied a 3d atlas parcellation image!")
# Labels prep
if atlas and not labels:
if (self.inputs.ref_txt is not None) and (op.exists(
self.inputs.ref_txt)):
labels = pd.read_csv(self.inputs.ref_txt,
sep=" ",
header=None,
names=["Index",
"Region"])["Region"].tolist()
else:
if atlas in local_atlases:
ref_txt = (
f"{str(Path(base_path).parent.parent)}/templates/"
f"labels/"
f"{atlas}.txt")
else:
ref_txt = self.inputs.ref_txt
if ref_txt is not None:
try:
labels = pd.read_csv(ref_txt,
sep=" ",
header=None,
names=["Index", "Region"
])["Region"].tolist()
except BaseException:
if self.inputs.use_parcel_naming is True:
try:
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("AAL reference labeling failed!")
labels = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
print("Using generic index labels...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
if self.inputs.use_parcel_naming is True:
try:
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("AAL reference labeling failed!")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print("Using generic index labels...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
dir_path = utils.do_dir_path(atlas, self.inputs.outdir)
if len(coords) != len(labels):
labels = [
i for i in labels if (i != 'Unknown' and i != 'Background')
]
if len(coords) != len(labels):
print("Length of coordinates is not equal to length of "
"label names...")
if self.inputs.use_parcel_naming is True:
try:
print("Attempting consensus parcel naming instead...")
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("Reverting to integer labels instead...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print("Reverting to integer labels instead...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
print(f"Coordinates:\n{coords}")
print(f"Labels:\n"
f"{textwrap.shorten(str(labels), width=1000, placeholder='...')}"
f"")
assert len(coords) == len(labels)
if label_intensities is not None:
self._results["labels"] = list(zip(labels, label_intensities))
else:
self._results["labels"] = labels
self._results["coords"] = coords
self._results["atlas"] = atlas
self._results["networks_list"] = networks_list
# TODO: Optimize this with 4d array concatenation and .npyz
out_path = f"{runtime.cwd}/parcels_4d.nii.gz"
nib.save(parcels_4d_img, out_path)
self._results["parcels_4d"] = out_path
self._results["par_max"] = par_max
self._results["parcellation"] = parcellation
self._results["dir_path"] = dir_path
return runtime
def fetch_nodes_and_labels(atlas, uatlas, ref_txt, parc, in_file, use_AAL_naming, clustering=False):
"""
General API for fetching, identifying, and defining atlas nodes based on coordinates and/or labels.
Parameters
----------
atlas : str
Name of a Nilearn-hosted coordinate or parcellation/label-based atlas supported for fetching.
See Nilearn's datasets.atlas module for more detailed reference.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
ref_txt : str
Path to an atlas reference .txt file that maps labels to intensities corresponding to uatlas.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
in_file : str
File path to Nifti1Image object whose affine will provide sampling reference for fetching.
use_AAL_naming : bool
Indicates whether to perform Automated-Anatomical Labeling of each coordinate from a list of a voxel
coordinates.
clustering : bool
Indicates whether clustering was performed. Default is False.
Returns
-------
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples in mm-space corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
atlas : str
Name of atlas parcellation (can differ slightly from fetch API string).
networks_list : list
List of RSN's and their associated cooordinates, if predefined uniquely for a given atlas.
parcel_list : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks.
par_max : int
The maximum label intensity in the parcellation image.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
dir_path : str
Path to directory containing subject derivative data for given run.
"""
from pynets.core import utils, nodemaker
import pandas as pd
import time
from pathlib import Path
import os.path as op
base_path = utils.get_file()
# Test if atlas is a nilearn atlas. If so, fetch coords, labels, and/or networks.
nilearn_parc_atlases = ['atlas_harvard_oxford', 'atlas_aal', 'atlas_destrieux_2009', 'atlas_talairach_gyrus',
'atlas_talairach_ba', 'atlas_talairach_lobe']
nilearn_coords_atlases = ['coords_power_2011', 'coords_dosenbach_2010']
nilearn_prob_atlases = ['atlas_msdl', 'atlas_pauli_2017']
if uatlas is None and atlas in nilearn_parc_atlases:
[labels, networks_list, uatlas] = nodemaker.nilearn_atlas_helper(atlas, parc)
if uatlas:
if not isinstance(uatlas, str):
nib.save(uatlas, "%s%s%s" % ('/tmp/', atlas, '.nii.gz'))
uatlas = "%s%s%s" % ('/tmp/', atlas, '.nii.gz')
[coords, _, par_max] = nodemaker.get_names_and_coords_of_parcels(uatlas)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
else:
raise ValueError("%s%s%s" % ('\nERROR: Atlas file for ', atlas, ' not found!'))
elif uatlas is None and parc is False and atlas in nilearn_coords_atlases:
print('Fetching coords and labels from nilearn coordinate-based atlas library...')
# Fetch nilearn atlas coords
[coords, _, networks_list, labels] = nodemaker.fetch_nilearn_atlas_coords(atlas)
parcel_list = None
par_max = None
elif uatlas is None and parc is False and atlas in nilearn_prob_atlases:
from nilearn.plotting import find_probabilistic_atlas_cut_coords
print('Fetching coords and labels from nilearn probabilistic atlas library...')
# Fetch nilearn atlas coords
[labels, networks_list, uatlas] = nodemaker.nilearn_atlas_helper(atlas, parc)
coords = find_probabilistic_atlas_cut_coords(maps_img=uatlas)
if uatlas:
if not isinstance(uatlas, str):
nib.save(uatlas, "%s%s%s" % ('/tmp/', atlas, '.nii.gz'))
uatlas = "%s%s%s" % ('/tmp/', atlas, '.nii.gz')
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
else:
raise ValueError("%s%s%s" % ('\nERROR: Atlas file for ', atlas, ' not found!'))
par_max = None
elif uatlas:
if clustering is True:
while True:
if op.isfile(uatlas):
break
else:
print('Waiting for atlas file...')
time.sleep(15)
atlas = uatlas.split('/')[-1].split('.')[0]
try:
# Fetch user-specified atlas coords
[coords, atlas, par_max] = nodemaker.get_names_and_coords_of_parcels(uatlas)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
# Describe user atlas coords
print("%s%s%s%s" % ('\n', atlas, ' comes with {0} '.format(par_max), 'parcels\n'))
except ValueError:
print('\n\nError: Either you have specified the name of a nilearn atlas that does not exist or '
'you have not supplied a 3d atlas parcellation image!\n\n')
parcel_list = None
par_max = None
coords = None
labels = None
networks_list = None
else:
networks_list = None
labels = None
parcel_list = None
par_max = None
coords = None
# Labels prep
if atlas:
if labels:
pass
else:
if (ref_txt is not None) and (op.exists(ref_txt)) and (use_AAL_naming is False):
labels = pd.read_csv(ref_txt, sep=" ", header=None, names=["Index", "Region"])['Region'].tolist()
else:
try:
ref_txt = "%s%s%s%s" % (str(Path(base_path).parent), '/labelcharts/', atlas, '.txt')
if op.exists(ref_txt) and (use_AAL_naming is False):
try:
labels = pd.read_csv(ref_txt,
sep="\t", header=None, names=["Index", "Region"])['Region'].tolist()
except:
labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
else:
if use_AAL_naming is True:
try:
labels = nodemaker.AAL_naming(coords)
except:
print('AAL reference labeling failed!')
labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
else:
print('Using generic index labels...')
labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
except:
print("Label reference file not found. Attempting AAL naming...")
if use_AAL_naming is True:
try:
labels = nodemaker.AAL_naming(coords)
except:
print('AAL reference labeling failed!')
labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
else:
print('Using generic index labels...')
labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
else:
print('WARNING: No labels available since atlas name is not specified!')
print("%s%s" % ('Labels:\n', labels))
dir_path = utils.do_dir_path(atlas, in_file)
if len(coords) != len(labels):
labels = len(coords) * [np.nan]
if len(coords) != len(labels):
raise ValueError('ERROR: length of coordinates is not equal to length of label names')
return labels, coords, atlas, networks_list, parcel_list, par_max, uatlas, dir_path