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test_featdesign.py 13.70 KiB
#!/usr/bin/env python
#
# test_featdesign.py -
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
"""Test data sets (in testdata/test_feat) were generated with FSL 5.0.9, and
then 'cleaned' to remove unnecessary files and reduce size, with the following
commands:
First level analyses:
find . -name "*png" -delete
find . -name "*ppm" -delete
find . -name "*html" -delete
rm -r logs .files tsplot .ramp.gif
for f in `find . -name "*nii.gz"`; do
echo $f > $f
done
Second level analyses:
find . -name "*png" -delete
find . -name "*ppm" -delete
find . -name "*html" -delete
rm -r logs .files .ramp.gif
for f in `find . -name "*nii.gz"`; do
echo $f > $f
done
Second level cope?.feats:
rm -r logs .files tsplot .ramp.gif
Two data sets - `1stlevel_realdata.feat` and `2ndlevel_realdata.gfeat` -
still have real (cut-down) NIFTI images, and have only been cleaned
with the following commands:
find . -name "*png" -delete
find . -name "*ppm" -delete
find . -name "*html" -delete
rm -r logs .files tsplot .ramp.gif
`1stlevel_realdata.feat`
- 45 time points
- Shape (4, 4, 5)
- 4 evs in total:
- 2 stimulus EVs
- 2 temporal derivative EVs
- 2 contrasts - one on each stimulus EV
`1stlevel_1.feat`
- 45 time points
- Shape (64, 64, 5)
- 10 EVs in total:
- 2 stimulus EVs
- 2 temporal derivative EVs
- 6 Standard motion parameters
- 2 contrasts - one on each stimulus EV
`1stlevel_2.feat`
- 45 time points
- Shape (64, 64, 5)
- 11 EVs in total:
- 2 stimulus EVs
- 2 temporal derivative EVs
- 1 voxelwise EV
- 6 Standard motion parameters
- 2 contrasts - one on each stimulus EV
`1stlevel_3.feat`
- 45 time points
- Shape (64, 64, 5)
- 32 EVs in total:
- 2 stimulus EVs
- 1 Temporal derivative EV on first
- 2 gamma basis functions on second
- 24 Standard+extended motion parameters
- 2 Confound EVs
- 1 Voxelwise confound EV
- 2 contrasts - one on each stimulus EV
`2ndlevel_1.gfeat`
- Shape (91, 109, 91)
- Three subjects
- Two copes
- One main EV - group average
`2ndlevel_2.gfeat`
- Shape (91, 109, 91)
- Three subjects
- Two copes
- One main EV - group average
- One voxelwise EV
`2ndlevel_realdata.gfeat`
- Shape (10, 10, 10)
- Three subjects
- Two copes
- One main EV - group average
"""
import os
import os.path as op
import itertools as it
import glob
import shutil
import numpy as np
import pytest
import tests
import fsl.data.image as fslimage
import fsl.data.featdesign as featdesign
import fsl.data.featanalysis as featanalysis
datadir = op.join(op.dirname(__file__), 'testdata', 'test_feat')
featdirs = ['1stlevel_1.feat', '1stlevel_2.feat', '1stlevel_3.feat',
'1stlevel_realdata.feat',
'2ndlevel_1.gfeat', '2ndlevel_2.gfeat', '2ndlevel_realdata.gfeat']
nevs = [10, 11, 32, 4, 1, 2, 1]
shapes = [(64, 64, 5),
(64, 64, 5),
(64, 64, 5),
( 4, 4, 5),
(91, 109, 91),
(91, 109, 91),
(10, 10, 10)]
designShapes = [(45, 10), (45, 11), (45, 32), (45, 4), (3, 1), (3, 2), (3, 1)]
TEST_ANALYSES = {}
for i, featdir in enumerate(featdirs):
TEST_ANALYSES[featdir] = {
'nevs' : nevs[i],
'shape' : shapes[i],
'designShape' : designShapes[i]
}
def test_FEATFSFDesign():
for featdir in TEST_ANALYSES.keys():
nev = TEST_ANALYSES[featdir]['nevs']
shape = TEST_ANALYSES[featdir]['shape']
desshape = TEST_ANALYSES[featdir]['designShape']
featdir = op.join(datadir, featdir)
settings = featanalysis.loadSettings(featdir)
# We can't load the voxelwise EVs
# here, because all of the .nii.gz
# files in the test directory are
# stubs. Voxelwise EVs get tested
# in the test_FEATFSFDesign_firstLevelVoxelwiseEV
# function
des = featdesign.FEATFSFDesign(featdir,
loadVoxelwiseEVs=False)
# Can also specify the design.fsf settings
featdesign.FEATFSFDesign(featdir,
settings=settings,
loadVoxelwiseEVs=False)
rvox = tests.random_voxels(shape)
assert len(des.getEVs()) == nev
assert des.getDesign().shape == desshape
assert des.getDesign(rvox).shape == desshape
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))
def test_getFirstLevelEVs_1():
featdir = op.join(datadir, '1stlevel_1.feat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
expected = [(featdesign.NormalEV, {'index' : 0, 'origIndex' : 0}),
(featdesign.TemporalDerivativeEV, {'index' : 1}),
(featdesign.NormalEV, {'index' : 2, 'origIndex' : 1}),
(featdesign.TemporalDerivativeEV, {'index' : 3}),
(featdesign.MotionParameterEV, {'index' : 4, 'motionIndex' : 0}),
(featdesign.MotionParameterEV, {'index' : 5, 'motionIndex' : 1}),
(featdesign.MotionParameterEV, {'index' : 6, 'motionIndex' : 2}),
(featdesign.MotionParameterEV, {'index' : 7, 'motionIndex' : 3}),
(featdesign.MotionParameterEV, {'index' : 8, 'motionIndex' : 4}),
(featdesign.MotionParameterEV, {'index' : 9, 'motionIndex' : 5})]
evs = featdesign.getFirstLevelEVs(featdir, settings, matrix)
assert len(evs) == 10
for i, (evtype, atts) in enumerate(expected):
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
def test_getFirstLevelEVs_2():
featdir = op.join(datadir, '1stlevel_2.feat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
expected = [(featdesign.NormalEV, {'index' : 0, 'origIndex' : 0}),
(featdesign.TemporalDerivativeEV, {'index' : 1}),
(featdesign.NormalEV, {'index' : 2, 'origIndex' : 1}),
(featdesign.TemporalDerivativeEV, {'index' : 3}),
(featdesign.VoxelwiseEV, {'index' : 4, 'origIndex' : 2}),
(featdesign.MotionParameterEV, {'index' : 5, 'motionIndex' : 0}),
(featdesign.MotionParameterEV, {'index' : 6, 'motionIndex' : 1}),
(featdesign.MotionParameterEV, {'index' : 7, 'motionIndex' : 2}),
(featdesign.MotionParameterEV, {'index' : 8, 'motionIndex' : 3}),
(featdesign.MotionParameterEV, {'index' : 9, 'motionIndex' : 4}),
(featdesign.MotionParameterEV, {'index' : 10, 'motionIndex' : 5})]
evs = featdesign.getFirstLevelEVs(featdir, settings, matrix)
assert len(evs) == 11
for i, (evtype, atts) in enumerate(expected):
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
def test_getFirstLevelEVs_3():
featdir = op.join(datadir, '1stlevel_3.feat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
expected = [(featdesign.NormalEV, {'index' : 0, 'origIndex' : 0}),
(featdesign.TemporalDerivativeEV, {'index' : 1}),
(featdesign.NormalEV, {'index' : 2, 'origIndex' : 1}),
(featdesign.BasisFunctionEV, {'index' : 3}),
(featdesign.BasisFunctionEV, {'index' : 4}),
(featdesign.VoxelwiseConfoundEV, {'index' : 5, 'voxIndex' : 0}),
(featdesign.MotionParameterEV, {'index' : 6, 'motionIndex' : 0}),
(featdesign.MotionParameterEV, {'index' : 7, 'motionIndex' : 1}),
(featdesign.MotionParameterEV, {'index' : 8, 'motionIndex' : 2}),
(featdesign.MotionParameterEV, {'index' : 9, 'motionIndex' : 3}),
(featdesign.MotionParameterEV, {'index' : 10, 'motionIndex' : 4}),
(featdesign.MotionParameterEV, {'index' : 11, 'motionIndex' : 5}),
(featdesign.MotionParameterEV, {'index' : 12, 'motionIndex' : 6}),
(featdesign.MotionParameterEV, {'index' : 13, 'motionIndex' : 7}),
(featdesign.MotionParameterEV, {'index' : 14, 'motionIndex' : 8}),
(featdesign.MotionParameterEV, {'index' : 15, 'motionIndex' : 9}),
(featdesign.MotionParameterEV, {'index' : 16, 'motionIndex' : 10}),
(featdesign.MotionParameterEV, {'index' : 17, 'motionIndex' : 11}),
(featdesign.MotionParameterEV, {'index' : 18, 'motionIndex' : 12}),
(featdesign.MotionParameterEV, {'index' : 19, 'motionIndex' : 13}),
(featdesign.MotionParameterEV, {'index' : 20, 'motionIndex' : 14}),
(featdesign.MotionParameterEV, {'index' : 21, 'motionIndex' : 15}),
(featdesign.MotionParameterEV, {'index' : 22, 'motionIndex' : 16}),
(featdesign.MotionParameterEV, {'index' : 23, 'motionIndex' : 17}),
(featdesign.MotionParameterEV, {'index' : 24, 'motionIndex' : 18}),
(featdesign.MotionParameterEV, {'index' : 25, 'motionIndex' : 19}),
(featdesign.MotionParameterEV, {'index' : 26, 'motionIndex' : 20}),
(featdesign.MotionParameterEV, {'index' : 27, 'motionIndex' : 21}),
(featdesign.MotionParameterEV, {'index' : 28, 'motionIndex' : 22}),
(featdesign.MotionParameterEV, {'index' : 29, 'motionIndex' : 23}),
(featdesign.ConfoundEV, {'index' : 30, 'confIndex' : 0}),
(featdesign.ConfoundEV, {'index' : 31, 'confIndex' : 1})]
evs = featdesign.getFirstLevelEVs(featdir, settings, matrix)
assert len(evs) == 32
for i, (evtype, atts) in enumerate(expected):
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
def test_getFirstLevelEVs_realdata():
featdir = op.join(datadir, '1stlevel_realdata.feat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
expected = [(featdesign.NormalEV, {'index' : 0, 'origIndex' : 0}),
(featdesign.TemporalDerivativeEV, {'index' : 1}),
(featdesign.NormalEV, {'index' : 2, 'origIndex' : 1}),
(featdesign.TemporalDerivativeEV, {'index' : 3})]
evs = featdesign.getFirstLevelEVs(featdir, settings, matrix)
assert len(evs) == 4
for i, (evtype, atts) in enumerate(expected):
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
def test_getHigherLevelEVs_1():
featdir = op.join(datadir, '2ndlevel_1.gfeat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
evs = featdesign.getHigherLevelEVs(featdir, settings, matrix)
assert len(evs) == 1
assert isinstance(evs[0], featdesign.NormalEV)
assert evs[0].index == 0
assert evs[0].origIndex == 0
def test_getHigherLevelEVs_2():
featdir = op.join(datadir, '2ndlevel_2.gfeat')
settings = featanalysis.loadSettings(featdir)
matrix = featdesign.loadDesignMat(op.join(featdir, 'design.mat'))
evs = featdesign.getHigherLevelEVs(featdir, settings, matrix)
assert len(evs) == 2
assert isinstance(evs[0], featdesign.NormalEV)
assert evs[0].index == 0
assert evs[0].origIndex == 0
assert isinstance(evs[1], featdesign.VoxelwiseEV)
def test_loadDesignMat():
for featdir in TEST_ANALYSES.keys():
shape = TEST_ANALYSES[featdir]['designShape']
featdir = op.join(datadir, featdir)
fname = op.join(featdir, 'design.mat')
mat = featdesign.loadDesignMat(fname)
assert mat.shape == shape
nonfile = op.join(datadir, 'non-existent-file')
badfile = op.join(datadir, '1stlevel_1.feat', 'design.fsf')
with pytest.raises(Exception):
featdesign.loadDesignMat(nonfile)
with pytest.raises(Exception):
featdesign.loadDesignMat(badfile)