/usr/lib/python2.7/dist-packages/dipy/reconst/tests/test_multi_voxel.py is in python-dipy 0.10.1-1.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | from __future__ import division, print_function, absolute_import
from functools import reduce
import numpy as np
import numpy.testing as npt
from dipy.reconst.multi_voxel import _squash, multi_voxel_fit, CallableArray
from dipy.core.sphere import unit_icosahedron
def test_squash():
A = np.ones((3, 3), dtype=float)
B = np.asarray(A, object)
npt.assert_array_equal(A, _squash(B))
npt.assert_equal(_squash(B).dtype, A.dtype)
B[2, 2] = None
A[2, 2] = 0
npt.assert_array_equal(A, _squash(B))
npt.assert_equal(_squash(B).dtype, A.dtype)
for ijk in np.ndindex(*B.shape):
B[ijk] = np.ones((2,))
A = np.ones((3, 3, 2))
npt.assert_array_equal(A, _squash(B))
npt.assert_equal(_squash(B).dtype, A.dtype)
B[2, 2] = None
A[2, 2] = 0
npt.assert_array_equal(A, _squash(B))
npt.assert_equal(_squash(B).dtype, A.dtype)
# sub-arrays have different shapes ( (3,) and (2,) )
B[0, 0] = np.ones((3,))
npt.assert_(_squash(B) is B)
# Check dtypes for arrays and scalars
arr_arr = np.zeros((2,), dtype=object)
scalar_arr = np.zeros((2,), dtype=object)
numeric_types = sum(
[np.sctypes[t] for t in ('int', 'uint', 'float', 'complex')],
[np.bool_])
for dt0 in numeric_types:
arr_arr[0] = np.zeros((3,), dtype=dt0)
scalar_arr[0] = dt0(0)
for dt1 in numeric_types:
arr_arr[1] = np.zeros((3,), dtype=dt1)
npt.assert_equal(_squash(arr_arr).dtype,
reduce(np.add, arr_arr).dtype)
scalar_arr[1] = dt0(1)
npt.assert_equal(_squash(scalar_arr).dtype,
reduce(np.add, scalar_arr).dtype)
# Check masks and Nones
arr = np.ones((3, 4), dtype=float)
obj_arr = arr.astype(object)
arr[1, 1] = 99
obj_arr[1, 1] = None
npt.assert_array_equal(_squash(obj_arr, mask=None, fill=99), arr)
msk = arr == 1
npt.assert_array_equal(_squash(obj_arr, mask=msk, fill=99), arr)
msk[1, 1] = 1 # unmask None - object array back
npt.assert_array_equal(_squash(obj_arr, mask=msk, fill=99), obj_arr)
msk[1, 1] = 0 # remask, back to fill again
npt.assert_array_equal(_squash(obj_arr, mask=msk, fill=99), arr)
obj_arr[2, 3] = None # add another unmasked None, object again
npt.assert_array_equal(_squash(obj_arr, mask=msk, fill=99), obj_arr)
# Check array of arrays
obj_arrs = np.zeros((3,), dtype=object)
for i in range(3):
obj_arrs[i] = np.ones((4, 5))
arr_arrs = np.ones((3, 4, 5))
# No Nones
npt.assert_array_equal(_squash(obj_arrs, mask=None, fill=99), arr_arrs)
# None, implicit masking
obj_masked = obj_arrs.copy()
obj_masked[1] = None
arr_masked = arr_arrs.copy()
arr_masked[1] = 99
npt.assert_array_equal(_squash(obj_masked, mask=None, fill=99),
arr_masked)
msk = np.array([1, 0, 1], dtype=np.bool_) # explicit mask
npt.assert_array_equal(_squash(obj_masked, mask=msk, fill=99),
arr_masked)
msk[1] = True # unmask None, object array back
npt.assert_array_equal(_squash(obj_masked, mask=msk, fill=99),
obj_masked)
def test_CallableArray():
callarray = CallableArray((2, 3), dtype=object)
# Test without Nones
callarray[:] = np.arange
expected = np.empty([2, 3, 4])
expected[:] = range(4)
npt.assert_array_equal(callarray(4), expected)
# Test with Nones
callarray[0, 0] = None
expected[0, 0] = 0
npt.assert_array_equal(callarray(4), expected)
def test_multi_voxel_fit():
class SillyModel(object):
@multi_voxel_fit
def fit(self, data, mask=None):
return SillyFit(model, data)
def predict(self, S0):
return np.ones(10) * S0
class SillyFit(object):
def __init__(self, model, data):
self.model = model
self.data = data
model_attr = 2.
def odf(self, sphere):
return np.ones(len(sphere.phi))
@property
def directions(self):
n = np.random.randint(0, 10)
return np.zeros((n, 3))
def predict(self, S0):
return np.ones(self.data.shape) * S0
# Test the single voxel case
model = SillyModel()
single_voxel = np.zeros(64)
fit = model.fit(single_voxel)
npt.assert_equal(type(fit), SillyFit)
# Test without a mask
many_voxels = np.zeros((2, 3, 4, 64))
fit = model.fit(many_voxels)
expected = np.empty((2, 3, 4))
expected[:] = 2.
npt.assert_array_equal(fit.model_attr, expected)
expected = np.ones((2, 3, 4, 12))
npt.assert_array_equal(fit.odf(unit_icosahedron), expected)
npt.assert_equal(fit.directions.shape, (2, 3, 4))
S0 = 100.
npt.assert_equal(fit.predict(S0=S0), np.ones(many_voxels.shape) * S0)
# Test with a mask
mask = np.zeros((3, 3, 3)).astype('bool')
mask[0, 0] = 1
mask[1, 1] = 1
mask[2, 2] = 1
data = np.zeros((3, 3, 3, 64))
fit = model.fit(data, mask)
expected = np.zeros((3,3,3))
expected[0, 0] = 2
expected[1, 1] = 2
expected[2, 2] = 2
npt.assert_array_equal(fit.model_attr, expected)
odf = fit.odf(unit_icosahedron)
npt.assert_equal(odf.shape, (3, 3, 3, 12))
npt.assert_array_equal(odf[~mask], 0)
npt.assert_array_equal(odf[mask], 1)
predicted = np.zeros(data.shape)
predicted[mask] = S0
npt.assert_equal(fit.predict(S0=S0), predicted)
# Test fit.shape
npt.assert_equal(fit.shape, (3, 3, 3))
# Test indexing into a fit
npt.assert_equal(type(fit[0, 0, 0]), SillyFit)
npt.assert_equal(fit[:2, :2, :2].shape, (2, 2, 2))
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