#!/usr/bin/env python
#
# test_dtifit.py -
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
import os.path as op
import numpy as np
import pytest
import tests
import fsl.data.dtifit as dtifit
import fsl.data.image as fslimage
def test_getDTIFitDataPrefix_and_isDTIFitPath():
def make_dtifit_dir(dir, prefix, suffixes):
for s in suffixes:
path = op.join(dir, '{}{}'.format(prefix, s))
with open(path, 'wt') as f:
f.write(path)
prefixes = ['dti', 'blob', 'random-prefix', '01234']
suffixes = ['_V1.nii', '_V2.nii', '_V3.nii',
'_L1.nii', '_L2.nii', '_L3.nii']
badSuffixes = ['_V1.txt', '_V2.nii', '_V3.nii',
'_L1.txt', '_L2.tar', '_L3.nii']
# Valid dtifit directories
with tests.testdir() as testdir:
for p in prefixes:
tests.cleardir(testdir)
make_dtifit_dir(testdir, p, suffixes)
assert dtifit.getDTIFitDataPrefix(testdir) == p
assert dtifit.isDTIFitPath(testdir)
# Invalid dtifit dir - one file missing
with tests.testdir() as testdir:
make_dtifit_dir(testdir, 'dti', suffixes[:-1])
assert dtifit.getDTIFitDataPrefix(testdir) is None
assert not dtifit.isDTIFitPath(testdir)
# Invalid dtifit dir - non-nifti files
with tests.testdir() as testdir:
make_dtifit_dir(testdir, 'dti', badSuffixes)
assert dtifit.getDTIFitDataPrefix(testdir) is None
assert not dtifit.isDTIFitPath(testdir)
# Ambiguous dtifit dir - multiple
# potential prefixes. Should return
# the first (alphabetical) one.
with tests.testdir() as testdir:
make_dtifit_dir(testdir, 'dti1', suffixes)
make_dtifit_dir(testdir, 'dti2', suffixes)
assert dtifit.getDTIFitDataPrefix(testdir) == 'dti1'
assert dtifit.isDTIFitPath(testdir)
def test_looksLikeTensorImage():
# dtifit outputs one of two formats:
# - the eigenvectors/values of the tensor matrix
# - the raw values of the tensor matrix
#
# looksLikeTensorImage tests for the latter
testcases = [((10, 10, 10), False),
((10, 10, 10, 5), False),
((10, 10, 10, 6), True)]
with tests.testdir() as testdir:
fname = op.join(testdir, 'tensor_image.nii')
for dims, expected in testcases:
tests.make_random_image(fname, dims=dims)
img = fslimage.Image(fname)
assert dtifit.looksLikeTensorImage(img) == expected
img = None
def test_decomposeTensorMatrix():
# A few tensor matrices
tensorMatrices = np.array([
[ 0.0032335606, -0.0000166876, 0.0000524589, 0.0032367723, 0.0000392826, 0.0028319934],
[ 0.0016628219, 0.0001651855, 0.0000678183, 0.0001300842, -0.0000293543, 0.0000450112],
[ 0.0006679352, -0.0000149214, 0.0000335666, 0.0007552942, -0.0003789219, 0.0008345888]])
# Expected eigenvalues and vectors
expEigVals = np.array([
[0.003252062, 0.0032290765, 0.002821188],
[0.0016829496, 0.00012794147, 0.000027026183],
[0.0011783227, 0.00066605647, 0.00041343897]])
expEigVecs = np.array([
[[ 0.701921939849854, -0.711941838264465, 0.021080270409584],
[-0.700381875038147, -0.695301055908203, -0.16131255030632 ],
[-0.129502296447754, -0.098464585840702, 0.986678183078766]],
[[-0.993700802326202, -0.104962401092052, -0.039262764155865],
[-0.081384353339672, 0.916762292385101, -0.391054302453995],
[-0.077040620148182, 0.385395616292953, 0.919529736042023]],
[[ 0.068294189870358, -0.666985750198364, 0.741933941841125],
[ 0.996652007102966, 0.079119503498077, -0.020613647997379],
[ 0.044952429831028, -0.740857720375061, -0.670156061649323]]])
tensorMatrices = tensorMatrices.reshape(1, 1, 3, 6)
expEigVals = expEigVals .reshape(1, 1, 3, 3)
expEigVecs = expEigVecs .reshape(1, 1, 3, 3, 3)
v1, v2, v3, l1, l2, l3 = dtifit.decomposeTensorMatrix(tensorMatrices)
expV1 = expEigVecs[:, :, :, 0]
expV2 = expEigVecs[:, :, :, 1]
expV3 = expEigVecs[:, :, :, 2]
expL1 = expEigVals[:, :, :, 0]
expL2 = expEigVals[:, :, :, 1]
expL3 = expEigVals[:, :, :, 2]
assert np.all(np.isclose(l1, expL1))
assert np.all(np.isclose(l2, expL2))
assert np.all(np.isclose(l3, expL3))
# Vector signs are arbitrary
for vox in range(3):
for resvec, expvec in zip([v1, v2, v3], [expV1, expV2, expV3]):
resvec = resvec[:, :, vox]
expvec = expvec[:, :, vox]
assert np.all(np.isclose(resvec, expvec)) or \
np.all(np.isclose(resvec, -expvec))
assert np.allclose(
dtifit.eigendecompositionToComponents(expV1, expV2, expV3, expL1, expL2, expL3),
tensorMatrices
)
random_tensor = np.random.randn(6)
assert np.allclose(
random_tensor,
dtifit.eigendecompositionToComponents(*dtifit.componentsToEigendecomposition(random_tensor))
)
random_tensor = np.random.randn(2, 2, 3, 6)
assert np.allclose(
random_tensor,
dtifit.eigendecompositionToComponents(*dtifit.componentsToEigendecomposition(random_tensor))
)
def test_DTIFitTensor():
with tests.testdir() as testdir:
with pytest.raises(Exception):
dtifit.DTIFitTensor(testdir)
v1file = op.join(testdir, 'dti_V1.nii')
v2file = op.join(testdir, 'dti_V2.nii')
v3file = op.join(testdir, 'dti_V3.nii')
l1file = op.join(testdir, 'dti_L1.nii')
l2file = op.join(testdir, 'dti_L2.nii')
l3file = op.join(testdir, 'dti_L3.nii')
v1 = tests.make_random_image(v1file, (5, 5, 5, 3)).get_data()
v2 = tests.make_random_image(v2file, (5, 5, 5, 3)).get_data()
v3 = tests.make_random_image(v3file, (5, 5, 5, 3)).get_data()
l1 = tests.make_random_image(l1file, (5, 5, 5)) .get_data()
l2 = tests.make_random_image(l2file, (5, 5, 5)) .get_data()
l3 = tests.make_random_image(l3file, (5, 5, 5)) .get_data()
dtiobj = dtifit.DTIFitTensor(testdir)
assert np.all(np.isclose(dtiobj.V1()[:], v1))
assert np.all(np.isclose(dtiobj.V2()[:], v2))
assert np.all(np.isclose(dtiobj.V3()[:], v3))
assert np.all(np.isclose(dtiobj.L1()[:], l1))
assert np.all(np.isclose(dtiobj.L2()[:], l2))
assert np.all(np.isclose(dtiobj.L3()[:], l3))
v1 = fslimage.Image(v1file)
assert np.all(np.isclose(dtiobj.voxToWorldMat, v1.voxToWorldMat))
assert np.all(np.isclose(dtiobj.shape[:3], v1.shape[:3]))
assert np.all(np.isclose(dtiobj.pixdim[:3], v1.pixdim[:3]))
del v1
del dtiobj
v1 = None