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Commit b2457e64 authored by Paul McCarthy's avatar Paul McCarthy :mountain_bicyclist:
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TEST: More making sure images/file handles are GC'd

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tests/__init__.py
+ 8
− 3
View file @ b2457e64
......@@ -294,7 +294,8 @@ def make_mock_feat_analysis(featdir,
if indata:
filtfunc = op.join(featdir, 'filtered_func_data.nii.gz')
make_random_image(filtfunc, shape4D, xform)
img = make_random_image(filtfunc, shape4D, xform)
del img
# and some dummy voxelwise EV files
if voxEVs:
......@@ -311,7 +312,10 @@ def make_mock_feat_analysis(featdir,
data = data.reshape(list(shape) + [1]).repeat(timepoints, axis=3)
data[..., :] += range(i, i + timepoints)
nib.save(nib.nifti1.Nifti1Image(data, xform), vf)
img = nib.nifti1.Nifti1Image(data, xform)
nib.save(img, vf)
del img
otherFiles = []
otherShapes = []
......@@ -342,7 +346,8 @@ def make_mock_feat_analysis(featdir,
otherShapes.extend([shape] * len(files))
for f, s in zip(otherFiles, otherShapes):
make_random_image(f, s, xform)
img = make_random_image(f, s, xform)
del img
return featdir
......
tests/test_featdesign.py
+ 21
− 12
View file @ b2457e64
......@@ -103,17 +103,12 @@ with the following commands:
"""
import os
import os.path as op
import itertools as it
import glob
import shutil
import numpy as np
import os.path as op
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
......@@ -172,6 +167,9 @@ def test_FEATFSFDesign():
assert des.getDesign().shape == desshape
assert des.getDesign(rvox).shape == desshape
del des
des = None
def test_FEATFSFDesign_firstLevelVoxelwiseEV(seed):
......@@ -214,6 +212,8 @@ def test_FEATFSFDesign_firstLevelVoxelwiseEV(seed):
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_getFirstLevelEVs_1():
......@@ -240,6 +240,8 @@ def test_getFirstLevelEVs_1():
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
del evs
evs = None
def test_getFirstLevelEVs_2():
......@@ -267,6 +269,8 @@ def test_getFirstLevelEVs_2():
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
del evs
evs = None
def test_getFirstLevelEVs_3():
......@@ -307,7 +311,6 @@ def test_getFirstLevelEVs_3():
(featdesign.ConfoundEV, {'index' : 30, 'confIndex' : 0}),
(featdesign.ConfoundEV, {'index' : 31, 'confIndex' : 1})]
evs = featdesign.getFirstLevelEVs(featdir, settings, matrix)
assert len(evs) == 32
......@@ -318,6 +321,10 @@ def test_getFirstLevelEVs_3():
for k, v in atts.items():
assert getattr(evs[i], k) == v
del evs
evs = None
def test_getFirstLevelEVs_realdata():
featdir = op.join(datadir, '1stlevel_realdata.feat')
settings = featanalysis.loadSettings(featdir)
......@@ -336,7 +343,8 @@ def test_getFirstLevelEVs_realdata():
assert isinstance(evs[i], evtype)
for k, v in atts.items():
assert getattr(evs[i], k) == v
del evs
evs = None
def test_getHigherLevelEVs_1():
......@@ -351,8 +359,8 @@ def test_getHigherLevelEVs_1():
assert isinstance(evs[0], featdesign.NormalEV)
assert evs[0].index == 0
assert evs[0].origIndex == 0
del evs
evs = None
def test_getHigherLevelEVs_2():
......@@ -368,7 +376,8 @@ def test_getHigherLevelEVs_2():
assert evs[0].index == 0
assert evs[0].origIndex == 0
assert isinstance(evs[1], featdesign.VoxelwiseEV)
del evs
evs = None
def test_loadDesignMat():
......
tests/test_featimage.py
+ 33
− 20
View file @ b2457e64
......@@ -91,7 +91,7 @@ def test_FEATImage_attributes():
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.
......@@ -126,12 +126,16 @@ def test_FEATImage_attributes():
expect = featanalysis.loadClusterResults(featdir, settings, ci)
assert len(result) == len(expect)
assert all([rc.nvoxels == ec.nvoxels for rc, ec in zip(result, expect)])
del design
del fi
fi = None
def test_FEATImage_imageAccessors():
for featdir in TEST_ANALYSES.keys():
shape = TEST_ANALYSES[featdir]['shape']
xform = TEST_ANALYSES[featdir]['xform']
......@@ -153,25 +157,26 @@ def test_FEATImage_imageAccessors():
nevs = fi.numEVs()
ncons = fi.numContrasts()
# Testing the FEATImage intenral cache
# Testing the FEATImage internal 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
del fi
fi = 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)
......@@ -182,13 +187,15 @@ def test_FEATImage_nostats():
with pytest.raises(Exception):
fi.fit([1, 2, 3], (2, 2, 2))
with pytest.raises(Exception):
fi.partialFit([1, 2, 3], (2, 2, 2))
fi.partialFit([1, 2, 3], (2, 2, 2))
del fi
fi = None
def test_FEATImage_fit_firstLevel():
featdir = op.join(datadir, '1stlevel_realdata.feat')
fi = featimage.FEATImage(featdir)
expect = np.array([
......@@ -203,8 +210,8 @@ def test_FEATImage_fit_firstLevel():
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])
10597.64918744])
# bad contrast
with pytest.raises(Exception):
fi.fit([1, 2, 3, 4, 5, 6, 7], (2, 2, 2))
......@@ -215,20 +222,24 @@ def test_FEATImage_fit_firstLevel():
result = fi.fit([1, 1, 1, 1], (2, 2, 2))
assert np.all(np.isclose(result, expect))
del fi
fi = None
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))
del fi
fi = None
def test_FEATImage_partialFit():
featdir = op.join(datadir, '1stlevel_realdata.feat')
fi = featimage.FEATImage(featdir)
expect = np.array([
......@@ -245,14 +256,16 @@ def test_FEATImage_partialFit():
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))
del fi
fi = None
def test_modelFit(seed):
for i in range(500):
# 2 evs, 20 timepoints
# First EV is a boxcar,
# second is a random regressor
......@@ -265,12 +278,12 @@ def test_modelFit(seed):
design[:, ev] = design[:, ev] - design[:, ev].mean()
# Generate some random PEs, and
# generate the data that would
# generate the data that would
# have resulted in them
pes = np.random.random(2)
expect = np.dot(design, pes)
contrast = [1] * design.shape[1]
result1 = featimage.modelFit(expect, design, contrast, pes, True)
result2 = featimage.modelFit(expect, design, contrast, pes, False)
......
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