from pylab import imshow, show, colorbar, subplot, title, figure import nibabel as nib import numpy as np for datat in range(2): if datat == 0: print 'fitting with dti' data, affine, gtab = get_train_dti(30) elif datat == 1: print 'fitting with hardi' data, affine, gtab = get_train_hardi(30) elif datat == 2: print 'fitting with dsi' data, affine, gtab = get_train_dsi(30) mask, affine = get_train_mask() data.shape mask.shape model = TensorModel(gtab) fit = model.fit(data, mask) print 'done!' fa = fit.fa slice_z = 25
from dipy.core.sphere_stats import angular_similarity from copy import deepcopy from dipy.reconst.gqi import GeneralizedQSamplingModel from dipy.reconst.dsi import DiffusionSpectrumDeconvModel, DiffusionSpectrumModel #from dipy.core.subdivide_octahedron import create_unit_sphere from dipy.viz.mayavi.spheres import show_odfs from dipy.reconst.odf import peak_directions sphere = get_sphere('symmetric724') sphere = sphere.subdivide(1) #print(sphere.vertices.shape) #sphere2 = create_unit_sphere(5) data, affine, gtab_full = get_train_dsi(30) gtab = deepcopy(gtab_full) #subset of dsi gtab bmin = 1500 bmax = 4000 gtab.b0s_mask = gtab.b0s_mask[(gtab.bvals >= bmin) & (gtab.bvals <= bmax)] gtab.bvecs = gtab.bvecs[(gtab.bvals >= bmin) & (gtab.bvals <= bmax)] gtab.bvals = gtab.bvals[(gtab.bvals >= bmin) & (gtab.bvals <= bmax)] NN = gtab.bvals.shape[0] SNR = 30. print('SNR = {} with {} gradients direction ({}-{})'.format(SNR, gtab.bvals.shape[0], bmin, bmax))