/usr/lib/python2.7/dist-packages/dipy/direction/tests/test_peaks.py is in python-dipy 0.10.1-1.
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from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_almost_equal, run_module_suite,
assert_equal, assert_)
from dipy.reconst.odf import (OdfFit, OdfModel, gfa)
from dipy.direction.peaks import (peaks_from_model,
peak_directions,
peak_directions_nl,
reshape_peaks_for_visualization)
from dipy.core.subdivide_octahedron import create_unit_hemisphere
from dipy.core.sphere import unit_icosahedron
from dipy.sims.voxel import multi_tensor, all_tensor_evecs, multi_tensor_odf
from dipy.data import get_data, get_sphere
from dipy.core.gradients import gradient_table, GradientTable
from dipy.core.sphere_stats import angular_similarity
from dipy.core.sphere import HemiSphere
def test_peak_directions_nl():
def discrete_eval(sphere):
return abs(sphere.vertices).sum(-1)
directions, values = peak_directions_nl(discrete_eval)
assert_equal(directions.shape, (4, 3))
assert_array_almost_equal(abs(directions), 1 / np.sqrt(3))
assert_array_equal(values, abs(directions).sum(-1))
# Test using a different sphere
sphere = unit_icosahedron.subdivide(4)
directions, values = peak_directions_nl(discrete_eval, sphere=sphere)
assert_equal(directions.shape, (4, 3))
assert_array_almost_equal(abs(directions), 1 / np.sqrt(3))
assert_array_equal(values, abs(directions).sum(-1))
# Test the relative_peak_threshold
def discrete_eval(sphere):
A = abs(sphere.vertices).sum(-1)
x, y, z = sphere.vertices.T
B = 1 + (x * z > 0) + 2 * (y * z > 0)
return A * B
directions, values = peak_directions_nl(discrete_eval, .01)
assert_equal(directions.shape, (4, 3))
directions, values = peak_directions_nl(discrete_eval, .3)
assert_equal(directions.shape, (3, 3))
directions, values = peak_directions_nl(discrete_eval, .6)
assert_equal(directions.shape, (2, 3))
directions, values = peak_directions_nl(discrete_eval, .8)
assert_equal(directions.shape, (1, 3))
assert_almost_equal(values, 4 * 3 / np.sqrt(3))
# Test odfs with large areas of zero
def discrete_eval(sphere):
A = abs(sphere.vertices).sum(-1)
x, y, z = sphere.vertices.T
B = (x * z > 0) + 2 * (y * z > 0)
return A * B
directions, values = peak_directions_nl(discrete_eval, 0.)
assert_equal(directions.shape, (3, 3))
directions, values = peak_directions_nl(discrete_eval, .6)
assert_equal(directions.shape, (2, 3))
directions, values = peak_directions_nl(discrete_eval, .8)
assert_equal(directions.shape, (1, 3))
assert_almost_equal(values, 3 * 3 / np.sqrt(3))
_sphere = create_unit_hemisphere(4)
_odf = (_sphere.vertices * [1, 2, 3]).sum(-1)
_gtab = GradientTable(np.ones((64, 3)))
class SimpleOdfModel(OdfModel):
sphere = _sphere
def fit(self, data):
fit = SimpleOdfFit(self, data)
fit.model = self
return fit
class SimpleOdfFit(OdfFit):
def odf(self, sphere=None):
if sphere is None:
sphere = self.model.sphere
# Use ascontiguousarray to work around a bug in NumPy
return np.ascontiguousarray((sphere.vertices * [1, 2, 3]).sum(-1))
def test_OdfFit():
m = SimpleOdfModel(_gtab)
f = m.fit(None)
odf = f.odf(_sphere)
assert_equal(len(odf), len(_sphere.theta))
def test_peak_directions():
model = SimpleOdfModel(_gtab)
fit = model.fit(None)
odf = fit.odf()
argmax = odf.argmax()
mx = odf.max()
sphere = fit.model.sphere
# Only one peak
dir, val, ind = peak_directions(odf, sphere, .5, 45)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
assert_array_equal(dir, dir_e)
odf[0] = mx * .9
# Two peaks, relative_threshold
dir, val, ind = peak_directions(odf, sphere, 1., 0)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, .8, 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
# Two peaks, angle_sep
dir, val, ind = peak_directions(odf, sphere, 0., 90)
dir_e = sphere.vertices[[argmax]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax])
assert_array_equal(val, odf[ind])
dir, val, ind = peak_directions(odf, sphere, 0., 0)
dir_e = sphere.vertices[[argmax, 0]]
assert_array_equal(dir, dir_e)
assert_array_equal(ind, [argmax, 0])
assert_array_equal(val, odf[ind])
def _create_mt_sim(mevals, angles, fractions, S0, SNR, half_sphere=False):
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=fractions, snr=SNR)
sphere = get_sphere('symmetric724').subdivide(2)
if half_sphere:
sphere = HemiSphere.from_sphere(sphere)
odf_gt = multi_tensor_odf(sphere.vertices, mevals,
angles=angles, fractions=fractions)
return odf_gt, sticks, sphere
def test_peak_directions_thorough():
# two equal fibers (creating a very sharp odf)
mevals = np.array([[0.0025, 0.0003, 0.0003],
[0.0025, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0)]
fractions = [50, 50]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions,
100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# two unequal fibers
fractions = [75, 25]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions,
100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 1, 2)
directions, values, indices = peak_directions(odf_gt, sphere, .20, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# two equal fibers short angle (simulating very sharp ODF)
mevals = np.array(([0.0045, 0.0003, 0.0003],
[0.0045, 0.0003, 0.0003]))
fractions = [50, 50]
angles = [(0, 0), (20, 0)]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles,
fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 1, 2)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 15.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# 1 fiber
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
fractions = [50, 50]
angles = [(15, 0), (15, 0)]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles,
fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 15.)
assert_almost_equal(angular_similarity(directions, sticks), 1, 2)
AE = np.rad2deg(np.arccos(np.dot(directions[0], sticks[0])))
assert_(abs(AE) < 2. or abs(AE - 180) < 2.)
# two equal fibers and one small noisy one
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (90, 0)]
fractions = [45, 45, 10]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# two equal fibers and one faulty
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (60, 0)]
fractions = [45, 45, 10]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# two equal fibers and one very very annoying one
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (60, 0)]
fractions = [40, 40, 20]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 2, 2)
# three peaks and one faulty
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (90, 0), (90, 45)]
fractions = [35, 35, 20, 10]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_almost_equal(angular_similarity(directions, sticks), 3, 2)
# four peaks
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (90, 0), (90, 45)]
fractions = [25, 25, 25, 25]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .15, 5.)
assert_almost_equal(angular_similarity(directions, sticks), 4, 2)
# four difficult peaks
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]])
angles = [(0, 0), (45, 0), (90, 0), (90, 45)]
fractions = [30, 30, 20, 20]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, 0, 0)
assert_almost_equal(angular_similarity(directions, sticks), 4, 1)
odf_gt, sticks, hsphere = _create_mt_sim(mevals, angles, fractions,
100, None, half_sphere=True)
directions, values, indices = peak_directions(odf_gt, hsphere, 0, 0)
assert_equal(angular_similarity(directions, sticks) < 4, True)
# four peaks and one them quite small
fractions = [35, 35, 20, 10]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions, 100, None)
directions, values, indices = peak_directions(odf_gt, sphere, 0, 0)
assert_equal(angular_similarity(directions, sticks) < 4, True)
odf_gt, sticks, hsphere = _create_mt_sim(mevals, angles, fractions,
100, None, half_sphere=True)
directions, values, indices = peak_directions(odf_gt, hsphere, 0, 0)
assert_equal(angular_similarity(directions, sticks) < 4, True)
# isotropic case
mevals = np.array([[0.0015, 0.0015, 0.0015]])
angles = [(0, 0)]
fractions = [100.]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions,
100, None)
directions, values, indices = peak_directions(odf_gt, sphere, .5, 25.)
assert_equal(len(values) > 10, True)
def test_difference_with_minmax():
# Show difference with and without minmax normalization
# we create an odf here with 3 main peaks, 1 small sharp unwanted peak
# (noise) and an isotropic compartment.
mevals = np.array([[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003],
[0.0015, 0.00005, 0.00005],
[0.0015, 0.0015, 0.0015]])
angles = [(0, 0), (45, 0), (90, 0), (90, 90), (0, 0)]
fractions = [20, 20, 10, 1, 100 - 20 - 20 - 10 - 1]
odf_gt, sticks, sphere = _create_mt_sim(mevals, angles, fractions,
100, None)
# We will show that when the minmax normalization is used we can remove
# the noisy peak using a lower threshold.
odf_gt_minmax = (odf_gt - odf_gt.min()) / (odf_gt.max() - odf_gt.min())
_, values_1, _ = peak_directions(odf_gt, sphere, .30, 25.)
assert_equal(len(values_1), 3)
_, values_2, _ = peak_directions(odf_gt_minmax, sphere, .30, 25.)
assert_equal(len(values_2), 3)
# Setting the smallest value of the odf to zero is like running
# peak_directions without the odf_min correction.
odf_gt[odf_gt.argmin()] = 0.
_, values_3, _ = peak_directions(odf_gt, sphere, .30, 25.,)
assert_equal(len(values_3), 4)
# we show here that to actually get that noisy peak out we need to
# increase the peak threshold considerably
directions, values_4, indices = peak_directions(odf_gt, sphere, .60, 25.,)
assert_equal(len(values_4), 3)
assert_almost_equal(values_1, values_4)
def test_degenerative_cases():
sphere = get_sphere('symmetric724')
# completely isotropic and degencase
odf = np.zeros(sphere.vertices.shape[0])
directions, values, indices = peak_directions(odf, sphere, .5, 25)
print(directions, values, indices)
assert_equal(len(values), 0)
assert_equal(len(directions), 0)
assert_equal(len(indices), 0)
odf = np.zeros(sphere.vertices.shape[0])
odf[0] = 0.020
odf[1] = 0.018
directions, values, indices = peak_directions(odf, sphere, .5, 25)
print(directions, values, indices)
assert_equal(values[0], 0.02)
odf = - np.ones(sphere.vertices.shape[0])
directions, values, indices = peak_directions(odf, sphere, .5, 25)
print(directions, values, indices)
assert_equal(len(values), 0)
odf = np.zeros(sphere.vertices.shape[0])
odf[0] = 0.020
odf[1] = 0.018
odf[2] = - 0.018
directions, values, indices = peak_directions(odf, sphere, .5, 25)
assert_equal(values[0], 0.02)
odf = np.ones(sphere.vertices.shape[0])
odf += 0.1 * np.random.rand(odf.shape[0])
directions, values, indices = peak_directions(odf, sphere, .5, 25)
assert_(all(values > values[0] * .5))
assert_array_equal(values, odf[indices])
odf = np.ones(sphere.vertices.shape[0])
odf[1:] = np.finfo(np.float).eps * np.random.rand(odf.shape[0] - 1)
directions, values, indices = peak_directions(odf, sphere, .5, 25)
assert_equal(values[0], 1)
assert_equal(len(values), 1)
def test_peaksFromModel():
data = np.zeros((10, 2))
# Test basic case
model = SimpleOdfModel(_gtab)
odf_argmax = _odf.argmax()
pam = peaks_from_model(model, data, _sphere, .5, 45, normalize_peaks=True)
assert_array_equal(pam.gfa, gfa(_odf))
assert_array_equal(pam.peak_values[:, 0], 1.)
assert_array_equal(pam.peak_values[:, 1:], 0.)
mn, mx = _odf.min(), _odf.max()
assert_array_equal(pam.qa[:, 0], (mx - mn) / mx)
assert_array_equal(pam.qa[:, 1:], 0.)
assert_array_equal(pam.peak_indices[:, 0], odf_argmax)
assert_array_equal(pam.peak_indices[:, 1:], -1)
# Test that odf array matches and is right shape
pam = peaks_from_model(model, data, _sphere, .5, 45, return_odf=True)
expected_shape = (len(data), len(_odf))
assert_equal(pam.odf.shape, expected_shape)
assert_((_odf == pam.odf).all())
assert_array_equal(pam.peak_values[:, 0], _odf.max())
# Test mask
mask = (np.arange(10) % 2) == 1
pam = peaks_from_model(model, data, _sphere, .5, 45, mask=mask,
normalize_peaks=True)
assert_array_equal(pam.gfa[~mask], 0)
assert_array_equal(pam.qa[~mask], 0)
assert_array_equal(pam.peak_values[~mask], 0)
assert_array_equal(pam.peak_indices[~mask], -1)
assert_array_equal(pam.gfa[mask], gfa(_odf))
assert_array_equal(pam.peak_values[mask, 0], 1.)
assert_array_equal(pam.peak_values[mask, 1:], 0.)
mn, mx = _odf.min(), _odf.max()
assert_array_equal(pam.qa[mask, 0], (mx - mn) / mx)
assert_array_equal(pam.qa[mask, 1:], 0.)
assert_array_equal(pam.peak_indices[mask, 0], odf_argmax)
assert_array_equal(pam.peak_indices[mask, 1:], -1)
def test_peaksFromModelParallel():
SNR = 100
S0 = 100
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
data, _ = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (60, 0)],
fractions=[50, 50], snr=SNR)
# test equality with/without multiprocessing
model = SimpleOdfModel(gtab)
pam_multi = peaks_from_model(model, data, _sphere, .5, 45,
normalize_peaks=True, return_odf=True,
return_sh=True, parallel=True)
pam_single = peaks_from_model(model, data, _sphere, .5, 45,
normalize_peaks=True, return_odf=True,
return_sh=True, parallel=False)
assert_equal(pam_multi.gfa.dtype, pam_single.gfa.dtype)
assert_equal(pam_multi.gfa.shape, pam_single.gfa.shape)
assert_array_almost_equal(pam_multi.gfa, pam_single.gfa)
assert_equal(pam_multi.qa.dtype, pam_single.qa.dtype)
assert_equal(pam_multi.qa.shape, pam_single.qa.shape)
assert_array_almost_equal(pam_multi.qa, pam_single.qa)
assert_equal(pam_multi.peak_values.dtype, pam_single.peak_values.dtype)
assert_equal(pam_multi.peak_values.shape, pam_single.peak_values.shape)
assert_array_almost_equal(pam_multi.peak_values, pam_single.peak_values)
assert_equal(pam_multi.peak_indices.dtype, pam_single.peak_indices.dtype)
assert_equal(pam_multi.peak_indices.shape, pam_single.peak_indices.shape)
assert_array_equal(pam_multi.peak_indices, pam_single.peak_indices)
assert_equal(pam_multi.peak_dirs.dtype, pam_single.peak_dirs.dtype)
assert_equal(pam_multi.peak_dirs.shape, pam_single.peak_dirs.shape)
assert_array_almost_equal(pam_multi.peak_dirs, pam_single.peak_dirs)
assert_equal(pam_multi.shm_coeff.dtype, pam_single.shm_coeff.dtype)
assert_equal(pam_multi.shm_coeff.shape, pam_single.shm_coeff.shape)
assert_array_almost_equal(pam_multi.shm_coeff, pam_single.shm_coeff)
assert_equal(pam_multi.odf.dtype, pam_single.odf.dtype)
assert_equal(pam_multi.odf.shape, pam_single.odf.shape)
assert_array_almost_equal(pam_multi.odf, pam_single.odf)
def test_peaks_shm_coeff():
SNR = 100
S0 = 100
_, fbvals, fbvecs = get_data('small_64D')
from dipy.data import get_sphere
sphere = get_sphere('repulsion724')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
data, _ = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (60, 0)],
fractions=[50, 50], snr=SNR)
from dipy.reconst.shm import CsaOdfModel
model = CsaOdfModel(gtab, 4)
pam = peaks_from_model(model, data[None, :], sphere, .5, 45,
return_odf=True, return_sh=True)
# Test that spherical harmonic coefficients return back correctly
odf2 = np.dot(pam.shm_coeff, pam.B)
assert_array_almost_equal(pam.odf, odf2)
assert_equal(pam.shm_coeff.shape[-1], 45)
pam = peaks_from_model(model, data[None, :], sphere, .5, 45,
return_odf=True, return_sh=False)
assert_equal(pam.shm_coeff, None)
pam = peaks_from_model(model, data[None, :], sphere, .5, 45,
return_odf=True, return_sh=True,
sh_basis_type='mrtrix')
odf2 = np.dot(pam.shm_coeff, pam.B)
assert_array_almost_equal(pam.odf, odf2)
def test_reshape_peaks_for_visualization():
data1 = np.random.randn(10, 5, 3).astype('float32')
data2 = np.random.randn(10, 2, 5, 3).astype('float32')
data3 = np.random.randn(10, 2, 12, 5, 3).astype('float32')
data1_reshape = reshape_peaks_for_visualization(data1)
data2_reshape = reshape_peaks_for_visualization(data2)
data3_reshape = reshape_peaks_for_visualization(data3)
assert_array_equal(data1_reshape.shape, (10, 15))
assert_array_equal(data2_reshape.shape, (10, 2, 15))
assert_array_equal(data3_reshape.shape, (10, 2, 12, 15))
assert_array_equal(data1_reshape.reshape(10, 5, 3), data1)
assert_array_equal(data2_reshape.reshape(10, 2, 5, 3), data2)
assert_array_equal(data3_reshape.reshape(10, 2, 12, 5, 3), data3)
if __name__ == '__main__':
run_module_suite()
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