dpd.fetch_stanford_hardi()
img, gtab = dpd.read_stanford_hardi()
We initialize a DTI model class instance using the gradient table used in the
After Change
hardi_fname, hardi_bval, hardi_bvec = dpd.get_fnames("stanford_hardi")
data, affine = load_nifti(hardi_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval, hardi_bvec)
gtab = gradient_table(bvals, bvecs)
We initialize a DTI model class instance using the gradient table used in the
measurement. By default, ``dti.TensorModel`` will use a weighted least-squares
