from dipy.data import (read_stanford_labels, default_sphere,
read_stanford_pve_maps)
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.trackvis import save_trk
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
auto_response)
from dipy.tracking.local import LocalTracking
from dipy.tracking import utils
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors
ren = fvtk.ren()
hardi_img, gtab, labels_img = read_stanford_labels()
_, _, img_pve_wm = read_stanford_pve_maps()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = img_pve_wm.get_data()
seed_mask = np.logical_and(labels == 2, white_matter == 1)
seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine)
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=white_matter)
dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
from dipy.data import (read_stanford_labels,
default_sphere,
read_stanford_pve_maps)
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.trackvis import save_trk
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
auto_response)
from dipy.tracking.local import LocalTracking
from dipy.tracking import utils
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors
ren = fvtk.ren()
hardi_img, gtab, labels_img = read_stanford_labels()
_, _, img_pve_wm = read_stanford_pve_maps()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_affine()
white_matter = img_pve_wm.get_data()
seed_mask = np.logical_and(labels == 2, white_matter == 1)
seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine)
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=white_matter)
dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
default='',
help="path to the output file")
parser.add_argument("--chunk-size",
type=int,
required=True,
help="how many seeds to process per sweep")
parser.add_argument("--nseeds",
type=int,
default=None,
help="how many seeds to process in total")
args = parser.parse_args()
# Get Stanford HARDI data
hardi_nifti_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames(
'stanford_hardi')
csf, gm, wm = read_stanford_pve_maps()
wm_data = wm.get_fdata()
img = get_img(hardi_nifti_fname)
voxel_order = "".join(aff2axcodes(img.affine))
gtab = get_gtab(hardi_bval_fname, hardi_bvec_fname)
#roi = get_img(hardi_nifti_fname)
data = img.get_fdata()
roi_data = (wm_data > 0.5)
mask = roi_data
tenmodel = dti.TensorModel(gtab, fit_method='WLS')
print('Fitting Tensor')
tenfit = tenmodel.fit(data, mask)
