def test_FEATImage_fit_firstLevel():
featdir = op.join(datadir, '1stlevel_realdata.feat')
fi = featimage.FEATImage(featdir)
expect = np.array([
10625.35273455, 10625.35263602, 10625.35248499, 10625.35272602,
10625.35286707, 10625.35237145, 10625.35244999, 10625.35270435,
10625.35272762, 10629.03397661, 10685.36428581, 10658.64633521,
10524.89226543, 10415.61794156, 10373.6671577, 10437.10001383,
10403.88611746, 10226.98548936, 10080.14323091, 10012.89132265,
9936.530395, 9957.92598556, 10090.51140821, 10199.6446317,
10246.67689405, 10261.45133255, 10265.36943466, 10266.22514043,
10266.32736048, 10264.92716455, 10243.05011597, 10245.62798475,
10287.91883737, 10325.38456267, 10341.92299781, 10347.17916861,
10348.58339616, 10348.89634025, 10348.93522057, 10345.25397481,
10288.9236822, 10315.64160242, 10449.39567496, 10558.66999883,
10597.64918744
])
# bad contrast
with pytest.raises(Exception):
fi.fit([1, 2, 3, 4, 5, 6, 7], (2, 2, 2))
# bad voxel
with pytest.raises(Exception):
fi.fit([1, 0, 0, 0], (6, 7, 7))
result = fi.fit([1, 1, 1, 1], (2, 2, 2))
assert np.all(np.isclose(result, expect))
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_FEATImage_fit_higherLevel():
featdir = op.join(datadir, '2ndlevel_realdata.gfeat/cope1.feat')
fi = featimage.FEATImage(featdir)
expect = np.array([86.37929535, 86.37929535, 86.37929535])
result = fi.fit([1], (5, 5, 5))
assert np.all(np.isclose(result, expect))
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_partialFit():
featdir = op.join(datadir, '1stlevel_realdata.feat')
fi = featimage.FEATImage(featdir)
expect = np.array([
10476.23185443, 10825.13542716, 10781.15297632, 11032.96902851,
11019.47536463, 10856.61537194, 10463.38391362, 10664.79227954,
10948.11850521, 10712.01568132, 10882.88149773, 10745.65913733,
10590.78057109, 10524.89948148, 10744.82941967, 10422.96359453,
10156.39446402, 10219.8339014, 10115.32145738, 10494.28109315,
10121.89555309, 10165.92560409, 10556.70058668, 10198.67478569,
10045.04934583, 9906.60233353, 10015.75569565, 9864.38786016,
10241.91219554, 10099.08538293, 10444.7826294, 10152.72622847,
10077.09075815, 10128.63320464, 10087.93203101, 10450.29667654,
10207.89144059, 10137.98334586, 10387.09965691, 10194.79436483,
10203.21032619, 10136.1942605, 10128.23728873, 10416.78984136,
10118.51262128
])
result = fi.partialFit([1, 1, 1, 1], (2, 2, 2))
assert np.all(np.isclose(result, expect))
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)
])
def __selectedOverlayChanged(self, *a):
"""Called when the :attr:`.DisplayContext.selectedOverlay` changes,
and by the :meth:`__overlayListChanged` method.
If the newly selected overlay is a :class:`.FEATImage` (or is otherwise
associated with a FEAT analysis) which has cluster results, they are
loaded in, and displayed on a :class:`.WidgetGrid`.
"""
prevOverlay = self.__selectedOverlay
self.__selectedOverlay = None
# No overlays are loaded
if len(self.overlayList) == 0:
self.__disable(strings.messages[self, 'noOverlays'])
return
overlay = self.displayCtx.getSelectedOverlay()
# Overlay is in-memory
if overlay.dataSource is None:
self.__disable(strings.messages[self, 'notFEAT'])
return
featDir = featanalysis.getAnalysisDir(overlay.dataSource)
# No FEAT analysis, or not an Image,
# can't do anything with that
if featDir is None or not isinstance(overlay, fslimage.Nifti):
log.debug('Overlay {} is not part of a feat '
'analysis, or is not Nifti'.format(overlay))
self.__disable(strings.messages[self, 'notFEAT'])
return
# Selected overlay is either the
# same one (maybe the overlay list,
# rather than the selected overlay,
# changed) or the newly selected
# overlay is from the same FEAT
# analysis. No need to do anything.
if prevOverlay is not None:
prevFeatImage = self.__featImages.get(prevOverlay)
if prevOverlay is overlay or \
(prevFeatImage is not None and
featDir == prevFeatImage.getFEATDir()):
log.debug('Overlay {} is already selected.'.format(overlay))
# Make sure the overlay -> FEATImage
# mapping is present, and (re-)cache
# a reference to the selected overlay.
self.__featImages[overlay] = prevFeatImage
self.__selectedOverlay = overlay
return
# We're in business. The newly selected
# overlay is a part of a FEAT analysis
# which is not currently being displayed.
self.__selectedOverlay = overlay
# Clear the stat selection combo box.
self.__statSelect.Clear()
# Get the FEATImage associated with
# this overlay, so we can get
# information about the FEAT analysis
featImage = self.__featImages.get(overlay)
if featImage is None:
# If the overlay itself is a FEATImage,
# then we have nothing to do.
if isinstance(overlay, featimage.FEATImage):
featImage = overlay
else:
# The FEATImage might already
# be in the overlay list -
# let's search for it.
for ovl in self.overlayList:
if isinstance(ovl, featimage.FEATImage) and \
ovl.getFEATDir() == featDir:
featImage = ovl
# As a last resort, if the FEATImage is not
# in the overlay list, we'll create one.
if featImage is None:
featImage = featimage.FEATImage(featDir,
loadData=False,
calcRange=False)
self.__featImages[overlay] = featImage
log.debug('Identified FEAT analysis associated with overlay '
'{}: {}'.format(overlay, featImage.getFEATDir()))
# Get the contrast and cluster
# information for the FEAT analysis.
display = self.displayCtx.getDisplay(overlay)
numCons = featImage.numContrasts()
conNames = featImage.contrastNames()
try:
# clusts is a list of (contrast, clusterList) tuples
clusts = [(c, featImage.clusterResults(c)) for c in range(numCons)]
clusts = [c for c in clusts if c[1] is not None]
# Error parsing the cluster data
except Exception as e:
log.warning('Error parsing cluster data for '
'{}: {}'.format(featImage.name, str(e)),
exc_info=True)
self.__disable(strings.messages[self, 'badData'])
return
# No cluster results exist
# for any contrast
if len(clusts) == 0:
self.__disable(strings.messages[self, 'noClusters'])
return
# Populate the stat selection combo box
for contrast, clusterList in clusts:
name = conNames[contrast]
name = strings.labels[self, 'clustName'].format(contrast + 1, name)
self.__statSelect.Append(name, clusterList)
self.__overlayName.SetLabel(display.name)
# Refresh the widget grid
self.__statSelect.SetSelection(0)
self.__statSelected()
self.Layout()
