Exemple #1
0
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
Exemple #2
0
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]