def test_FEATImage_imageAccessors():
for featdir in TEST_ANALYSES.keys():
shape = TEST_ANALYSES[featdir]['shape']
xform = TEST_ANALYSES[featdir]['xform']
with tests.testdir() as testdir:
if 'realdata' not in featdir:
featdir = tests.make_mock_feat_analysis(
op.join(datadir, featdir), testdir, shape, xform)
else:
featdir = op.join(datadir, featdir)
shape4D = shape
shape = shape4D[:3]
fi = featimage.FEATImage(featdir)
nevs = fi.numEVs()
ncons = fi.numContrasts()
# Testing the FEATImage intenral cache
for i in range(2):
assert fi.getResiduals().shape == shape4D
for ev in range(nevs):
assert fi.getPE(ev).shape == shape
for con in range(ncons):
assert fi.getCOPE(con).shape == shape
assert fi.getZStats(con).shape == shape
assert fi.getClusterMask(con).shape == shape
def test_FEATFSFDesign_firstLevelVoxelwiseEV(seed):
template = op.join(datadir, '1stlevel_2.feat')
with tests.testdir() as testdir:
# Set up some test data - we make
# a copy of the feat directory,
# and generate some dummy data for
# the voxelwise EVs.
featdir = op.join(testdir, '1stlevel_2.feat')
shape4D = (64, 64, 5, 45)
shape = shape4D[:3]
featdir = tests.make_mock_feat_analysis(template,
testdir,
shape4D,
indata=False,
pes=False,
copes=False,
zstats=False,
residuals=False,
clustMasks=False)
# Now load the design, and make sure that
# the voxel EVs are filled correctly
design = featdesign.FEATFSFDesign(featdir)
voxevIdxs = [
ev.index for ev in design.getEVs()
if isinstance(ev, (featdesign.VoxelwiseEV,
featdesign.VoxelwiseConfoundEV))
]
randVoxels = np.vstack([np.random.randint(0, s, 10) for s in shape]).T
for voxel in randVoxels:
voxel = [int(v) for v in voxel]
offset = np.ravel_multi_index(voxel, shape)
matrix = design.getDesign(voxel)
for i, evidx in enumerate(voxevIdxs):
expect = np.arange(i, i + 45) + offset
assert np.all(np.isclose(matrix[:, evidx], expect))
del design
design = None
def test_FEATImage_nostats():
featdir = op.join(datadir, '1stlevel_nostats.feat')
shape = (4, 4, 5, 45)
with tests.testdir() as testdir:
featdir = tests.make_mock_feat_analysis(featdir, testdir, shape)
fi = featimage.FEATImage(featdir)
assert fi.getDesign() is None
assert fi.numPoints() == 0
assert fi.numEVs() == 0
assert fi.evNames() == []
with pytest.raises(Exception):
fi.fit([1, 2, 3], (2, 2, 2))
with pytest.raises(Exception):
fi.partialFit([1, 2, 3], (2, 2, 2))
def test_FEATImage_attributes():
# TEst bad input
with pytest.raises(Exception):
featimage.FEATImage('baddir')
for featdir in TEST_ANALYSES.keys():
shape = TEST_ANALYSES[featdir]['shape']
xform = TEST_ANALYSES[featdir]['xform']
with tests.testdir() as testdir:
if 'realdata' not in featdir:
featdir = tests.make_mock_feat_analysis(op.join(
datadir, featdir),
testdir,
shape,
xform,
pes=False,
copes=False,
zstats=False,
residuals=False,
clustMasks=False)
else:
featdir = op.join(datadir, featdir)
# Now create a FEATImage. We validate its
# attributes against the values returned by
# the functions in featdesign/featanalysis.
fi = featimage.FEATImage(featdir)
settings = featanalysis.loadSettings(featdir)
design = featdesign.FEATFSFDesign(featdir, settings)
desmat = design.getDesign()
evnames = [ev.title for ev in design.getEVs()]
contrastnames, contrasts = featanalysis.loadContrasts(featdir)
assert np.all(np.isclose(fi.shape, shape))
assert np.all(np.isclose(fi.voxToWorldMat, xform))
assert fi.getFEATDir() == featdir
assert fi.getAnalysisName() == op.splitext(op.basename(featdir))[0]
assert fi.isFirstLevelAnalysis(
) == featanalysis.isFirstLevelAnalysis(settings)
assert fi.getTopLevelAnalysisDir(
) == featanalysis.getTopLevelAnalysisDir(featdir)
assert fi.getReportFile() == featanalysis.getReportFile(featdir)
assert fi.hasStats() == featanalysis.hasStats(featdir)
assert fi.numPoints() == desmat.shape[0]
assert fi.numEVs() == desmat.shape[1]
assert fi.evNames() == evnames
assert fi.numContrasts() == len(contrasts)
assert fi.contrastNames() == contrastnames
assert fi.contrasts() == contrasts
assert np.all(np.isclose(fi.getDesign(), desmat))
assert fi.thresholds() == featanalysis.getThresholds(settings)
for ci in range(len(contrasts)):
result = fi.clusterResults(ci)
expect = featanalysis.loadClusterResults(featdir, settings, ci)
assert len(result) == len(expect)
assert all([
rc.nvoxels == ec.nvoxels for rc, ec in zip(result, expect)
])