This file is indexed.

/usr/lib/python2.7/dist-packages/dipy/reconst/tests/test_dsi.py is in python-dipy 0.10.1-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
import numpy as np
from numpy.testing import (assert_equal,
                           assert_almost_equal,
                           run_module_suite,
                           assert_array_equal,
                           assert_raises)
from dipy.data import get_data, dsi_voxels
from dipy.reconst.dsi import DiffusionSpectrumModel
from dipy.reconst.odf import gfa
from dipy.direction.peaks import peak_directions
from dipy.sims.voxel import SticksAndBall
from dipy.core.sphere import Sphere
from dipy.core.gradients import gradient_table
from dipy.data import get_sphere
from numpy.testing import assert_equal
from dipy.core.subdivide_octahedron import create_unit_sphere
from dipy.core.sphere_stats import angular_similarity


def test_dsi():
    #load symmetric 724 sphere
    sphere = get_sphere('symmetric724')

    #load icosahedron sphere
    sphere2 = create_unit_sphere(5)
    btable = np.loadtxt(get_data('dsi515btable'))
    gtab = gradient_table(btable[:,0], btable[:,1:])
    data, golden_directions = SticksAndBall(gtab, d=0.0015,
                                            S0=100, angles=[(0, 0), (90, 0)],
                                            fractions=[50, 50], snr=None)

    ds = DiffusionSpectrumModel(gtab)

    # symmetric724
    dsfit = ds.fit(data)
    odf = dsfit.odf(sphere)

    directions, _, _ = peak_directions(odf, sphere)
    assert_equal(len(directions), 2)
    assert_almost_equal(angular_similarity(directions, golden_directions),
                        2, 1)

    # 5 subdivisions
    dsfit = ds.fit(data)
    odf2 = dsfit.odf(sphere2)
    directions, _, _ = peak_directions(odf2, sphere2)
    assert_equal(len(directions), 2)
    assert_almost_equal(angular_similarity(directions, golden_directions),
                        2, 1)

    assert_equal(dsfit.pdf().shape, 3 * (ds.qgrid_size, ))
    sb_dummies=sticks_and_ball_dummies(gtab)
    for sbd in sb_dummies:
        data, golden_directions = sb_dummies[sbd]
        odf = ds.fit(data).odf(sphere2)
        directions, _, _ = peak_directions(odf, sphere2)
        if len(directions) <= 3:
            assert_equal(len(directions), len(golden_directions))
        if len(directions) > 3:
            assert_equal(gfa(odf) < 0.1, True)

    assert_raises(ValueError, DiffusionSpectrumModel, gtab, qgrid_size=16)


def test_multivox_dsi():
    data, gtab = dsi_voxels()
    DS = DiffusionSpectrumModel(gtab)
    sphere = get_sphere('symmetric724')

    DSfit = DS.fit(data)
    PDF = DSfit.pdf()
    assert_equal(data.shape[:-1] + (17, 17, 17), PDF.shape)
    assert_equal(np.alltrue(np.isreal(PDF)), True)


def test_multib0_dsi():
    data, gtab = dsi_voxels()
    # Create a new data-set with a b0 measurement:
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    sphere = get_sphere('repulsion724')
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    odf = dsfit.odf(sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)

    # And again, with one more b0 measurement (two in total):
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    odf = dsfit.odf(sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)


def sticks_and_ball_dummies(gtab):
    sb_dummies={}
    S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
                              angles=[(0, 0)],
                              fractions=[100], snr=None)
    sb_dummies['1fiber'] = (S, sticks)
    S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
                              angles=[(0, 0), (90, 0)],
                              fractions=[50, 50], snr=None)
    sb_dummies['2fiber'] = (S, sticks)
    S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
                              angles=[(0, 0), (90, 0), (90, 90)],
                              fractions=[33, 33, 33], snr=None)
    sb_dummies['3fiber'] = (S, sticks)
    S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
                              angles=[(0, 0), (90, 0), (90, 90)],
                              fractions=[0, 0, 0], snr=None)
    sb_dummies['isotropic'] = (S, sticks)
    return sb_dummies


if __name__ == '__main__':
    run_module_suite()