def test_streams2graph(fa_wei):
from dipy.core.gradients import gradient_table
from dipy.io import save_pickle
base_dir = str(Path(__file__).parent/"examples")
dwi_file = f"{base_dir}/003/test_out/003/dwi/sub-003_dwi_reor-RAS_res-2mm.nii.gz"
conn_model = 'csd'
min_length = 10
error_margin = 2
directget = 'prob'
track_type = 'particle'
target_samples = 500
overlap_thr = 1
min_span_tree = True
prune = 3
norm = 6
binary = False
roi = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
network = 'Default'
ID = '003'
parc = True
disp_filt = False
node_size = None
dens_thresh = False
atlas = 'whole_brain_cluster_labels_PCA200'
uatlas = None
coord_file_path = f"{base_dir}/miscellaneous/Default_func_coords_wb.pkl"
coord_file = open(coord_file_path, 'rb')
coords = pickle.load(coord_file)
labels_file_path = f"{base_dir}/miscellaneous/Default_func_labelnames_wb.pkl"
labels_file = open(labels_file_path, 'rb')
labels = pickle.load(labels_file)
# Not actually normalized to mni-space in this test.
atlas_mni = f"{base_dir}/003/dmri/whole_brain_cluster_labels_PCA200_dwi_track.nii.gz"
streams = f"{base_dir}/miscellaneous/003_streamlines_est-csd_nodetype-parc_samples-1000streams_tt-particle_dg-prob_ml-10.trk"
B0_mask = f"{base_dir}/003/anat/mean_B0_bet_mask_tmp.nii.gz"
dir_path = f"{base_dir}/003/dmri"
bvals = f"{dir_path}/sub-003_dwi.bval"
bvecs = f"{base_dir}/003/test_out/003/dwi/bvecs_reor.bvec"
gtab_file = f"{base_dir}/gtab.pkl"
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = 50
gtab_bvals = gtab.bvals.copy()
b0_thr_ixs = np.where(gtab_bvals < gtab.b0_threshold)[0]
gtab_bvals[b0_thr_ixs] = 0
gtab.b0s_mask = gtab_bvals == 0
save_pickle(gtab_file, gtab)
# Not actually normalized to mni-space in this test.
warped_fa = tens_mod_fa_est(gtab_file, dwi_file, B0_mask)[0]
conn_matrix = streams2graph(atlas_mni, streams, overlap_thr, dir_path, track_type, target_samples,
conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary,
directget, warped_fa, error_margin, min_length, fa_wei)[2]
assert conn_matrix is not None
def test_streams2graph(fa_wei, dsn):
from dipy.core.gradients import gradient_table
from pynets.registration import register
from pynets.core import nodemaker
from dipy.io import save_pickle
import os
base_dir = str(Path(__file__).parent / "examples")
dwi_file = f"{base_dir}/003/test_out/003/dwi/sub-003_dwi_reor-RAS_res-2mm.nii.gz"
conn_model = 'csd'
min_length = 10
error_margin = 2
directget = 'prob'
track_type = 'particle'
target_samples = 500
overlap_thr = 1
min_span_tree = True
prune = 3
norm = 6
binary = False
roi = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
network = 'Default'
ID = '003'
parc = True
disp_filt = False
node_size = None
dens_thresh = False
atlas = 'whole_brain_cluster_labels_PCA200'
uatlas = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
t1_aligned_mni = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
atlas_dwi = f"{base_dir}/003/dmri/whole_brain_cluster_labels_PCA200_dwi_track.nii.gz"
streams = f"{base_dir}/miscellaneous/003_streamlines_est-csd_nodetype-parc_samples-1000streams_tt-particle_" \
f"dg-prob_ml-10.trk"
B0_mask = f"{base_dir}/003/anat/mean_B0_bet_mask_tmp.nii.gz"
dir_path = f"{base_dir}/003/dmri"
bvals = f"{dir_path}/sub-003_dwi.bval"
bvecs = f"{base_dir}/003/test_out/003/dwi/bvecs_reor.bvec"
gtab_file = f"{base_dir}/gtab.pkl"
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = 50
gtab_bvals = gtab.bvals.copy()
b0_thr_ixs = np.where(gtab_bvals < gtab.b0_threshold)[0]
gtab_bvals[b0_thr_ixs] = 0
gtab.b0s_mask = gtab_bvals == 0
save_pickle(gtab_file, gtab)
fa_path = tens_mod_fa_est(gtab_file, dwi_file, B0_mask)[0]
coords = nodemaker.get_names_and_coords_of_parcels(uatlas)[0]
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
# if dsn is True:
# os.makedirs(f"{dir_path}/dmri_reg/DSN", exist_ok=True)
# (streams_mni, dir_path, track_type, target_samples, conn_model, network, node_size, dens_thresh, ID, roi,
# min_span_tree, disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary, atlas_mni, directget,
# warped_fa, min_length, error_margin) = register.direct_streamline_norm(streams, fa_path, fa_path, dir_path,
# track_type, target_samples, conn_model,
# network, node_size, dens_thresh, ID,
# roi, min_span_tree, disp_filt, parc,
# prune, atlas, atlas_dwi, uatlas,
# labels, coords, norm, binary, uatlas,
# dir_path, [0.1, 0.2], [40, 30],
# directget, min_length, t1_aligned_mni,
# error_margin)
#
# conn_matrix = streams2graph(atlas_mni, streams_mni, overlap_thr, dir_path, track_type, target_samples,
# conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
# disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary,
# directget, warped_fa, error_margin, min_length, fa_wei)[2]
# else:
# conn_matrix = streams2graph(atlas_dwi, streams, overlap_thr, dir_path, track_type, target_samples,
# conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
# disp_filt, parc, prune, atlas, atlas_dwi, labels, coords, norm, binary,
# directget, fa_path, error_margin, min_length, fa_wei)[2]
conn_matrix = streams2graph(
atlas_dwi, streams, overlap_thr, dir_path, track_type, target_samples,
conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
disp_filt, parc, prune, atlas, atlas_dwi, labels, coords, norm, binary,
directget, fa_path, error_margin, min_length, fa_wei)[2]
assert conn_matrix is not None
def test_streams2graph(dmri_estimation_data,
tractography_estimation_data,
random_mni_roi_data,
dsn=False):
from pynets.registration.register import direct_streamline_norm
from dipy.core.gradients import gradient_table
from dipy.io import save_pickle
import random
import pkg_resources
tmp = tempfile.TemporaryDirectory()
dir_path = str(tmp.name)
os.makedirs(dir_path, exist_ok=True)
dwi_file = dmri_estimation_data['dwi_file']
conn_model = 'csd'
min_length = 10
error_margin = 5
traversal = 'prob'
track_type = 'local'
min_span_tree = False
prune = 3
norm = 6
binary = False
roi = random_mni_roi_data['roi_file']
subnet = 'Default'
ID = '003'
parc = True
disp_filt = False
node_radius = None
dens_thresh = False
atlas = 'whole_brain_cluster_labels_PCA200'
parcellation = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
streams = tractography_estimation_data['trk']
B0_mask = dmri_estimation_data['B0_mask']
bvals = dmri_estimation_data['fbvals']
bvecs = dmri_estimation_data['fbvecs']
gtab_file = dmri_estimation_data['gtab_file']
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = 50
gtab_bvals = gtab.bvals.copy()
b0_thr_ixs = np.where(gtab_bvals < gtab.b0_threshold)[0]
gtab_bvals[b0_thr_ixs] = 0
gtab.b0s_mask = gtab_bvals == 0
save_pickle(gtab_file, gtab)
fa_path = tens_mod_fa_est(gtab_file, dwi_file, B0_mask)[0]
ap_path = create_anisopowermap(gtab_file, dwi_file, B0_mask)[0]
t1w_brain = dmri_estimation_data['t1w_file']
t1w_gm = dmri_estimation_data['f_pve_gm']
atlas_in_dwi = fname_presuffix(ap_path,
suffix="atlas_in_dwi",
use_ext=True)
resample_to_img(nib.load(parcellation),
nib.load(fa_path),
interpolation="nearest").to_filename(atlas_in_dwi)
coords = [
(random.random() * 2.0, random.random() * 2.0, random.random() * 2.0)
for _ in range(len(np.unique(nib.load(atlas_in_dwi).get_fdata())) - 1)
]
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
if dsn is True:
os.makedirs(f"{dir_path}/dmri_reg/DSN", exist_ok=True)
(streams, dir_path, track_type, conn_model, subnet, node_radius,
dens_thresh, ID, roi, min_span_tree, disp_filt, parc, prune, atlas,
parcellation, labels, coords, norm, binary, atlas_for_streams,
traversal, fa_path,
min_length) = direct_streamline_norm(streams,
fa_path,
ap_path,
dir_path,
track_type,
conn_model,
subnet,
node_radius,
dens_thresh,
ID,
roi,
min_span_tree,
disp_filt,
parc,
prune,
atlas,
atlas_in_dwi,
parcellation,
labels,
coords,
norm,
binary,
t1w_gm,
dir_path, [0.1, 0.2], [40, 30],
traversal,
min_length,
t1w_brain,
run_dsn=True)
conn_matrix = streams2graph(
atlas_in_dwi, streams, os.path.dirname(streams), track_type,
conn_model, subnet, node_radius, dens_thresh, ID, roi, min_span_tree,
disp_filt, parc, prune, atlas, atlas_in_dwi, labels, coords, norm,
binary, traversal, fa_path, min_length, error_margin)[2]
assert conn_matrix is not None