from load_data import get_train_dti, get_train_hardi, get_train_dsi, get_train_mask from dipy.reconst.dti import TensorModel 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
from dipy.data import get_sphere from dipy.viz.mayavi.spheres import show_odfs from dipy.reconst.shm import sf_to_sh from load_data import get_train_dsi, get_train_rois, get_train_mask, get_train_hardi, get_train_dti from show_streamlines import show_streamlines from conn_mat import connectivity_matrix from dipy.io.pickles import save_pickle, load_pickle from time import time if __name__ == '__main__': data, affine, gtab = get_train_hardi(10, denoised=None, Coupe=None) mask, affine = get_train_mask() tenmodel = TensorModel(gtab) tenfit = tenmodel.fit(data, mask) FA = fractional_anisotropy(tenfit.evals) FA[np.isnan(FA)] = 0 indices = np.where(FA > 0.7) lambdas = tenfit.evals[indices][:, :2] S0s = data[indices][:, 0]