def test_shore_metrics():
fetch_taiwan_ntu_dsi()
img, gtab = read_taiwan_ntu_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100, angles=angl,
fractions=[50, 50], snr=None)
S = S / S[0, None].astype(np.float)
radial_order = 8
zeta = 800
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore= asmfit.shore_coeff
cmat = SHOREmatrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 4)
mevecs2 = np.zeros((2, 3, 3))
angl = np.array(angl)
for i in range(2):
mevecs2[i] = all_tensor_evecs(sticks[i]).T
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, [.5, .5], mevals=mevals, mevecs=mevecs2)
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
assert_equal(rtop_mt/rtop_shore_signal < 1.12 and rtop_mt/rtop_shore_signal > 0.9 , True)
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
assert_equal(msd_mt/msd_shore < 1.05 and msd_mt/msd_shore > 0.95 , True)
[Wu2007]_, [Wu2008]_ on your dataset.
First import the necessary modules:
"""
import numpy as np
import matplotlib.pyplot as plt
from dipy.data import fetch_taiwan_ntu_dsi, read_taiwan_ntu_dsi
from dipy.reconst.dsi import DiffusionSpectrumModel
"""
Download and read the data for this tutorial.
"""
fetch_taiwan_ntu_dsi()
img, gtab = read_taiwan_ntu_dsi()
"""
img contains a nibabel Nifti1Image object (data) and gtab contains a GradientTable
object (gradient information e.g. b-values). For example to read the b-values
it is possible to write print(gtab.bvals).
Load the raw diffusion data and the affine.
"""
data = img.get_data()
affine = img.affine
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
Instantiate the Model and apply it to the data.
First import the necessary modules:
"""
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
from dipy.data import fetch_taiwan_ntu_dsi, read_taiwan_ntu_dsi, get_sphere
from dipy.data import get_data, dsi_voxels
from dipy.reconst.shore import ShoreModel
"""
Download and read the data for this tutorial.
"""
fetch_taiwan_ntu_dsi()
img, gtab = read_taiwan_ntu_dsi()
"""
img contains a nibabel Nifti1Image object (data) and gtab contains a GradientTable
object (gradient information e.g. b-values). For example, to read the b-values
it is possible to write print(gtab.bvals).
Load the raw diffusion data and the affine.
"""
data = img.get_data()
affine = img.affine
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
Instantiate the Model.
"""