def getTopLevelAnalysisDir(path):
"""If the given path is contained within a hierarchy of FEAT or MELODIC
directories, the path to the highest-level (i.e. the shallowest in the
file system) directory is returned. Otherwise, ``None`` is returned.
See :func:`.featanalysis.getTopLevelAnalysisDir`.
"""
return featanalysis.getTopLevelAnalysisDir(path)
def test_getTopLevelAnalysisDir():
testcases = [
('analysis.feat/filtered_func_data.ica/melodic_IC.nii.gz', 'analysis.feat'),
('analysis.feat/filtered_func_data.nii.gz', 'analysis.feat'),
('analysis.gfeat/cope1.feat/stats/zstat1.nii.gz', 'analysis.gfeat'),
('filtered_func_data.ica/melodic_mix', 'filtered_func_data.ica'),
('rest.ica/filtered_func_data.ica/melodic_IC.nii.gz', 'rest.ica')
]
for path, expected in testcases:
assert featanalysis.getTopLevelAnalysisDir(path) == expected
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)
])