def test_create_anisopowermap(dmri_estimation_data):
""" Test creating an anisotropic power map."""
gtab_file = dmri_estimation_data['gtab_file']
dwi_file = dmri_estimation_data['dwi_file']
B0_mask_file = dmri_estimation_data['B0_mask']
assert os.path.isfile(
create_anisopowermap(gtab_file, dwi_file, B0_mask_file)[0])
def test_create_anisopowermap(dmri_estimation_data):
""" Test creating an anisotropic power map."""
gtab = dmri_estimation_data['gtab']
dwi_img = dmri_estimation_data['dwi_img']
B0_mask_img = dmri_estimation_data['B0_mask_img']
gtab_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.pkl')
dwi_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
B0_mask_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
save_pickle(gtab_file.name, gtab)
nib.save(dwi_img, dwi_file.name)
nib.save(B0_mask_img, B0_mask_file.name)
[anisopwr_path, _, _, _] = \
create_anisopowermap(gtab_file.name, dwi_file.name, B0_mask_file.name)
assert os.path.isfile(anisopwr_path)
gtab_file.close()
dwi_file.close()
B0_mask_file.close()
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