/usr/lib/python2.7/dist-packages/dipy/tracking/tests/test_utils.py is in python-dipy 0.10.1-1.
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from ...utils.six.moves import xrange
import numpy as np
import nose
from dipy.io.bvectxt import orientation_from_string
from dipy.tracking.utils import (affine_for_trackvis, connectivity_matrix,
density_map, length, move_streamlines,
ndbincount, reduce_labels,
reorder_voxels_affine, seeds_from_mask,
random_seeds_from_mask, target,
_rmi, unique_rows, near_roi,
reduce_rois)
from dipy.tracking._utils import _to_voxel_coordinates
import dipy.tracking.metrics as metrix
from dipy.tracking.vox2track import streamline_mapping
import numpy.testing as npt
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal, assert_raises, assert_true
def make_streamlines():
streamlines = [np.array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2],
[5, 10, 12]], 'float'),
np.array([[1, 2, 3],
[3, 2, 0],
[5, 20, 33],
[40, 80, 120]], 'float')]
return streamlines
def test_density_map():
# One streamline diagonal in volume
streamlines = [np.array([np.arange(10)]*3).T]
shape = (10, 10, 10)
x = np.arange(10)
expected = np.zeros(shape)
expected[x, x, x] = 1.
dm = density_map(streamlines, vol_dims=shape, voxel_size=(1, 1, 1))
assert_array_equal(dm, expected)
# add streamline, make voxel_size smaller. Each streamline should only be
# counted once, even if multiple points lie in a voxel
streamlines.append(np.ones((5, 3)))
shape = (5, 5, 5)
x = np.arange(5)
expected = np.zeros(shape)
expected[x, x, x] = 1.
expected[0, 0, 0] += 1
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
# should work with a generator
dm = density_map(iter(streamlines), vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
# Test passing affine
affine = np.diag([2, 2, 2, 1.])
affine[:3, 3] = 1.
dm = density_map(streamlines, shape, affine=affine)
assert_array_equal(dm, expected)
# Shift the image by 2 voxels, ie 4mm
affine[:3, 3] -= 4.
expected_old = expected
new_shape = [i + 2 for i in shape]
expected = np.zeros(new_shape)
expected[2:, 2:, 2:] = expected_old
dm = density_map(streamlines, new_shape, affine=affine)
assert_array_equal(dm, expected)
def test_to_voxel_coordinates_precision():
# To simplify tests, use an identity affine. This would be the result of
# a call to _mapping_to_voxel with another identity affine.
transfo = np.array([[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
# Offset is computed by _mapping_to_voxel. With a 1x1x1 dataset
# having no translation, the offset is half the voxel size, i.e. 0.5.
offset = np.array([0.5, 0.5, 0.5])
# Without the added tolerance in _to_voxel_coordinates, this streamline
# should raise an Error in the call to _to_voxel_coordinates.
failing_strl = [np.array([[-0.5000001, 0.0, 0.0], [0.0, 1.0, 0.0]],
dtype=np.float32)]
indices = _to_voxel_coordinates(failing_strl, transfo, offset)
expected_indices = np.array([[[0, 0, 0], [0, 1, 0]]])
assert_array_equal(indices, expected_indices)
def test_connectivity_matrix():
label_volume = np.array([[[3, 0, 0],
[0, 0, 0],
[0, 0, 4]]])
streamlines = [np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),
np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),
np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')]
expected = np.zeros((5, 5), 'int')
expected[3, 4] = 2
expected[4, 3] = 1
# Check basic Case
matrix = connectivity_matrix(streamlines, label_volume, (1, 1, 1),
symmetric=False)
assert_array_equal(matrix, expected)
# Test mapping
matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),
symmetric=False, return_mapping=True)
assert_array_equal(matrix, expected)
assert_equal(mapping[3, 4], [0, 1])
assert_equal(mapping[4, 3], [2])
assert_equal(mapping.get((0, 0)), None)
# Test mapping and symmetric
matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),
symmetric=True, return_mapping=True)
assert_equal(mapping[3, 4], [0, 1, 2])
# When symmetric only (3,4) is a key, not (4, 3)
assert_equal(mapping.get((4, 3)), None)
# expected output matrix is symmetric version of expected
expected = expected + expected.T
assert_array_equal(matrix, expected)
# Test mapping_as_streamlines, mapping dict has lists of streamlines
matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),
symmetric=False,
return_mapping=True,
mapping_as_streamlines=True)
assert_true(mapping[3, 4][0] is streamlines[0])
assert_true(mapping[3, 4][1] is streamlines[1])
assert_true(mapping[4, 3][0] is streamlines[2])
# Test passing affine to connectivity_matrix
expected = matrix
affine = np.diag([-1, -1, -1, 1.])
streamlines = [-i for i in streamlines]
matrix = connectivity_matrix(streamlines, label_volume, affine=affine)
# In the symmetrical case, the matrix should be, well, symmetric:
assert_equal(matrix[4, 3], matrix[4, 3])
def test_ndbincount():
def check(expected):
assert_equal(bc[0, 0], expected[0])
assert_equal(bc[0, 1], expected[1])
assert_equal(bc[1, 0], expected[2])
assert_equal(bc[2, 2], expected[3])
x = np.array([[0, 0], [0, 0], [0, 1], [0, 1], [1, 0], [2, 2]]).T
expected = [2, 2, 1, 1]
# count occurrences in x
bc = ndbincount(x)
assert_equal(bc.shape, (3, 3))
check(expected)
# pass in shape
bc = ndbincount(x, shape=(4, 5))
assert_equal(bc.shape, (4, 5))
check(expected)
# pass in weights
weights = np.arange(6.)
weights[-1] = 1.23
expeceted = [1., 5., 4., 1.23]
bc = ndbincount(x, weights=weights)
check(expeceted)
# raises an error if shape is too small
assert_raises(ValueError, ndbincount, x, None, (2, 2))
def test_reduce_labels():
shape = (4, 5, 6)
# labels from 100 to 220
labels = np.arange(100, np.prod(shape)+100).reshape(shape)
# new labels form 0 to 120, and lookup maps range(0,120) to range(100, 220)
new_labels, lookup = reduce_labels(labels)
assert_array_equal(new_labels, labels-100)
assert_array_equal(lookup, labels.ravel())
def test_move_streamlines():
streamlines = make_streamlines()
affine = np.eye(4)
new_streamlines = move_streamlines(streamlines, affine)
for i, test_sl in enumerate(new_streamlines):
assert_array_equal(test_sl, streamlines[i])
affine[:3, 3] += (4, 5, 6)
new_streamlines = move_streamlines(streamlines, affine)
for i, test_sl in enumerate(new_streamlines):
assert_array_equal(test_sl, streamlines[i]+(4, 5, 6))
affine = np.eye(4)
affine = affine[[2, 1, 0, 3]]
new_streamlines = move_streamlines(streamlines, affine)
for i, test_sl in enumerate(new_streamlines):
assert_array_equal(test_sl, streamlines[i][:, [2, 1, 0]])
affine[:3, 3] += (4, 5, 6)
new_streamlines = move_streamlines(streamlines, affine)
undo_affine = move_streamlines(new_streamlines, np.eye(4),
input_space=affine)
for i, test_sl in enumerate(undo_affine):
assert_array_almost_equal(test_sl, streamlines[i])
# Test that changing affine does affect moving streamlines
affineA = affine.copy()
affineB = affine.copy()
streamlinesA = move_streamlines(streamlines, affineA)
streamlinesB = move_streamlines(streamlines, affineB)
affineB[:] = 0
for (a, b) in zip(streamlinesA, streamlinesB):
assert_array_equal(a, b)
def test_target():
streamlines = [np.array([[0., 0., 0.],
[1., 0., 0.],
[2., 0., 0.]]),
np.array([[0., 0., 0],
[0, 1., 1.],
[0, 2., 2.]])]
affine = np.eye(4)
mask = np.zeros((4, 4, 4), dtype=bool)
mask[0, 0, 0] = True
# Both pass though
new = list(target(streamlines, mask, affine=affine))
assert_equal(len(new), 2)
new = list(target(streamlines, mask, affine=affine, include=False))
assert_equal(len(new), 0)
# only first
mask[:] = False
mask[1, 0, 0] = True
new = list(target(streamlines, mask, affine=affine))
assert_equal(len(new), 1)
assert_true(new[0] is streamlines[0])
new = list(target(streamlines, mask, affine=affine, include=False))
assert_equal(len(new), 1)
assert_true(new[0] is streamlines[1])
# Test that bad points raise a value error
bad_sl = [np.array([[10., 10., 10.]])]
new = target(bad_sl, mask, affine=affine)
assert_raises(ValueError, list, new)
bad_sl = [-np.array([[10., 10., 10.]])]
new = target(bad_sl, mask, affine=affine)
assert_raises(ValueError, list, new)
# Test smaller voxels
affine = np.random.random((4, 4)) - .5
affine[3] = [0, 0, 0, 1]
streamlines = list(move_streamlines(streamlines, affine))
new = list(target(streamlines, mask, affine=affine))
assert_equal(len(new), 1)
assert_true(new[0] is streamlines[0])
new = list(target(streamlines, mask, affine=affine, include=False))
assert_equal(len(new), 1)
assert_true(new[0] is streamlines[1])
# Test that changing mask and affine do not break target
include = target(streamlines, mask, affine=affine)
exclude = target(streamlines, mask, affine=affine, include=False)
affine[:] = np.eye(4)
mask[:] = False
include = list(include)
exclude = list(exclude)
assert_equal(len(include), 1)
assert_true(include[0] is streamlines[0])
assert_equal(len(exclude), 1)
assert_true(exclude[0] is streamlines[1])
def test_near_roi():
streamlines = [np.array([[0., 0., 0.9],
[1.9, 0., 0.],
[3, 2., 2.]]),
np.array([[0.1, 0., 0],
[0, 1., 1.],
[0, 2., 2.]]),
np.array([[2, 2, 2],
[3, 3, 3]])]
affine = np.eye(4)
mask = np.zeros((4, 4, 4), dtype=bool)
mask[0, 0, 0] = True
mask[1, 0, 0] = True
assert_array_equal(near_roi(streamlines, mask, tol=1),
np.array([True, True, False]))
assert_array_equal(near_roi(streamlines, mask),
np.array([False, True, False]))
# If there is an affine, we need to use it:
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=1),
np.array([True, True, False]))
assert_array_equal(near_roi(x_streamlines, mask, affine=affine,
tol=None),
np.array([False, True, False]))
# Test for use of the 'all' mode:
assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=None,
mode='all'), np.array([False, False, False]))
mask[0, 1, 1] = True
mask[0, 2, 2] = True
# Test for use of the 'all' mode, also testing that setting the tolerance
# to a very small number gets overridden:
assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=0.1,
mode='all'), np.array([False, True, False]))
mask[2, 2, 2] = True
mask[3, 3, 3] = True
assert_array_equal(near_roi(x_streamlines, mask, affine=affine,
tol=None,
mode='all'),
np.array([False, True, True]))
# Test for use of endpoints as selection criteria:
mask = np.zeros((4, 4, 4), dtype=bool)
mask[0, 1, 1] = True
mask[3, 2, 2] = True
assert_array_equal(near_roi(streamlines, mask, tol=0.87,
mode="either_end"),
np.array([True, False, False]))
assert_array_equal(near_roi(streamlines, mask, tol=0.87,
mode="both_end"),
np.array([False, False, False]))
mask[0, 0, 0] = True
mask[0, 2, 2] = True
assert_array_equal(near_roi(streamlines, mask, mode="both_end"),
np.array([False, True, False]))
# Test with a generator input:
def generate_sl(streamlines):
for sl in streamlines:
yield sl
assert_array_equal(near_roi(generate_sl(streamlines),
mask, mode="both_end"),
np.array([False, True, False]))
def test_voxel_ornt():
sh = (40, 40, 40)
sz = (1, 2, 3)
I4 = np.eye(4)
ras = orientation_from_string('ras')
sra = orientation_from_string('sra')
lpi = orientation_from_string('lpi')
srp = orientation_from_string('srp')
affine = reorder_voxels_affine(ras, ras, sh, sz)
assert_array_equal(affine, I4)
affine = reorder_voxels_affine(sra, sra, sh, sz)
assert_array_equal(affine, I4)
affine = reorder_voxels_affine(lpi, lpi, sh, sz)
assert_array_equal(affine, I4)
affine = reorder_voxels_affine(srp, srp, sh, sz)
assert_array_equal(affine, I4)
streamlines = make_streamlines()
box = np.array(sh)*sz
sra_affine = reorder_voxels_affine(ras, sra, sh, sz)
toras_affine = reorder_voxels_affine(sra, ras, sh, sz)
assert_array_equal(np.dot(toras_affine, sra_affine), I4)
expected_sl = (sl[:, [2, 0, 1]] for sl in streamlines)
test_sl = move_streamlines(streamlines, sra_affine)
for ii in xrange(len(streamlines)):
assert_array_equal(next(test_sl), next(expected_sl))
lpi_affine = reorder_voxels_affine(ras, lpi, sh, sz)
toras_affine = reorder_voxels_affine(lpi, ras, sh, sz)
assert_array_equal(np.dot(toras_affine, lpi_affine), I4)
expected_sl = (box - sl for sl in streamlines)
test_sl = move_streamlines(streamlines, lpi_affine)
for ii in xrange(len(streamlines)):
assert_array_equal(next(test_sl), next(expected_sl))
srp_affine = reorder_voxels_affine(ras, srp, sh, sz)
toras_affine = reorder_voxels_affine(srp, ras, (40, 40, 40), (3, 1, 2))
assert_array_equal(np.dot(toras_affine, srp_affine), I4)
expected_sl = [sl.copy() for sl in streamlines]
for sl in expected_sl:
sl[:, 1] = box[1] - sl[:, 1]
expected_sl = (sl[:, [2, 0, 1]] for sl in expected_sl)
test_sl = move_streamlines(streamlines, srp_affine)
for ii in xrange(len(streamlines)):
assert_array_equal(next(test_sl), next(expected_sl))
def test_streamline_mapping():
streamlines = [np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),
np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),
np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')]
mapping = streamline_mapping(streamlines, (1, 1, 1))
expected = {(0, 0, 0): [0, 1, 2], (0, 2, 2): [0, 1, 2],
(0, 1, 1): [1, 2]}
assert_equal(mapping, expected)
mapping = streamline_mapping(streamlines, (1, 1, 1),
mapping_as_streamlines=True)
expected = dict((k, [streamlines[i] for i in indices])
for k, indices in expected.items())
assert_equal(mapping, expected)
# Test passing affine
affine = np.eye(4)
affine[:3, 3] = .5
mapping = streamline_mapping(streamlines, affine=affine,
mapping_as_streamlines=True)
assert_equal(mapping, expected)
# Make the voxel size smaller
affine = np.diag([.5, .5, .5, 1.])
affine[:3, 3] = .25
expected = dict((tuple(i*2 for i in key), value)
for key, value in expected.items())
mapping = streamline_mapping(streamlines, affine=affine,
mapping_as_streamlines=True)
assert_equal(mapping, expected)
def test_rmi():
I1 = _rmi([3, 4], [10, 10])
assert_equal(I1, 34)
I1 = _rmi([0, 0], [10, 10])
assert_equal(I1, 0)
assert_raises(ValueError, _rmi, [10, 0], [10, 10])
try:
from numpy import ravel_multi_index
except ImportError:
raise nose.SkipTest()
# Dtype of random integers is system dependent
A, B, C, D = np.random.randint(0, 1000, size=[4, 100])
I1 = _rmi([A, B], dims=[1000, 1000])
I2 = ravel_multi_index([A, B], dims=[1000, 1000])
assert_array_equal(I1, I2)
I1 = _rmi([A, B, C, D], dims=[1000]*4)
I2 = ravel_multi_index([A, B, C, D], dims=[1000]*4)
assert_array_equal(I1, I2)
# Check for overflow with small int types
indices = np.random.randint(0, 255, size=(2, 100))
dims = (1000, 1000)
I1 = _rmi(indices, dims=dims)
I2 = ravel_multi_index(indices, dims=dims)
assert_array_equal(I1, I2)
def test_affine_for_trackvis():
voxel_size = np.array([1., 2, 3.])
affine = affine_for_trackvis(voxel_size)
origin = np.dot(affine, [0, 0, 0, 1])
assert_array_almost_equal(origin[:3], voxel_size / 2)
def test_length():
# Generate a simulated bundle of fibers:
n_streamlines = 50
n_pts = 100
t = np.linspace(-10, 10, n_pts)
bundle = []
for i in np.linspace(3, 5, n_streamlines):
pts = np.vstack((np.cos(2 * t/np.pi), np.zeros(t.shape) + i, t)).T
bundle.append(pts)
start = np.random.randint(10, 30, n_streamlines)
end = np.random.randint(60, 100, n_streamlines)
bundle = [10 * streamline[start[i]:end[i]] for (i, streamline) in
enumerate(bundle)]
bundle_lengths = length(bundle)
for idx, this_length in enumerate(bundle_lengths):
assert_equal(this_length, metrix.length(bundle[idx]))
def test_seeds_from_mask():
mask = np.random.random_integers(0, 1, size=(10, 10, 10))
seeds = seeds_from_mask(mask, density=1)
assert_equal(mask.sum(), len(seeds))
assert_array_equal(np.argwhere(mask), seeds)
mask[:] = False
mask[3, 3, 3] = True
seeds = seeds_from_mask(mask, density=[3, 4, 5])
assert_equal(len(seeds), 3 * 4 * 5)
assert_true(np.all((seeds > 2.5) & (seeds < 3.5)))
mask[4, 4, 4] = True
seeds = seeds_from_mask(mask, density=[3, 4, 5])
assert_equal(len(seeds), 2 * 3 * 4 * 5)
assert_true(np.all((seeds > 2.5) & (seeds < 4.5)))
in_333 = ((seeds > 2.5) & (seeds < 3.5)).all(1)
assert_equal(in_333.sum(), 3 * 4 * 5)
in_444 = ((seeds > 3.5) & (seeds < 4.5)).all(1)
assert_equal(in_444.sum(), 3 * 4 * 5)
def test_random_seeds_from_mask():
mask = np.random.random_integers(0, 1, size=(4, 6, 3))
seeds = random_seeds_from_mask(mask, seeds_per_voxel=24)
assert_equal(mask.sum() * 24, len(seeds))
mask[:] = False
mask[2, 2, 2] = True
seeds = random_seeds_from_mask(mask, seeds_per_voxel=8)
assert_equal(mask.sum() * 8, len(seeds))
assert_true(np.all((seeds > 1.5) & (seeds < 2.5)))
def test_connectivity_matrix_shape():
# Labels: z-planes have labels 0,1,2
labels = np.zeros((3, 3, 3), dtype=int)
labels[:, :, 1] = 1
labels[:, :, 2] = 2
# Streamline set, only moves between first two z-planes.
streamlines = [np.array([[0., 0., 0.],
[0., 0., 0.5],
[0., 0., 1.]]),
np.array([[0., 1., 1.],
[0., 1., 0.5],
[0., 1., 0.]])]
matrix = connectivity_matrix(streamlines, labels, affine=np.eye(4))
assert_equal(matrix.shape, (3, 3))
def test_unique_rows():
"""
Testing the function unique_coords
"""
arr = np.array([[1, 2, 3], [1, 2, 3], [2, 3, 4], [3, 4, 5]])
arr_w_unique = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
assert_array_equal(unique_rows(arr), arr_w_unique)
# Should preserve order:
arr = np.array([[2, 3, 4], [1, 2, 3], [1, 2, 3], [3, 4, 5]])
arr_w_unique = np.array([[2, 3, 4], [1, 2, 3], [3, 4, 5]])
assert_array_equal(unique_rows(arr), arr_w_unique)
# Should work even with longer arrays:
arr = np.array([[2, 3, 4], [1, 2, 3], [1, 2, 3], [3, 4, 5],
[6, 7, 8], [0, 1, 0], [1, 0, 1]])
arr_w_unique = np.array([[2, 3, 4], [1, 2, 3], [3, 4, 5],
[6, 7, 8], [0, 1, 0], [1, 0, 1]])
assert_array_equal(unique_rows(arr), arr_w_unique)
def test_reduce_rois():
roi1 = np.zeros((4, 4, 4), dtype=np.bool)
roi2 = np.zeros((4, 4, 4), dtype=np.bool)
roi1[1, 1, 1] = 1
roi2[2, 2, 2] = 1
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, False])
npt.assert_equal(include_roi, roi1)
npt.assert_equal(exclude_roi, roi2)
# Array input:
include_roi, exclude_roi = reduce_rois(np.array([roi1, roi2]),
[True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
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