diff --git a/tests/__init__.py b/tests/__init__.py
index eb16fa95ab31bdcce6494524b28a481493cf7cb1..4960e909532e5d5f1a970fd9f44368ed23c7d31e 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -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
diff --git a/tests/test_featdesign.py b/tests/test_featdesign.py
index f685ff712364c6338faaade5af3cea151ccc87a5..b10aa23091954d0dd4b840d0c11132d3427bd8ab 100644
--- a/tests/test_featdesign.py
+++ b/tests/test_featdesign.py
@@ -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():
diff --git a/tests/test_featimage.py b/tests/test_featimage.py
index 6489cc0bfc41582eae0640ba92adfd315f8f2552..fb33c6e4ddb13b43dbc4d1883faddf227489b305 100644
--- a/tests/test_featimage.py
+++ b/tests/test_featimage.py
@@ -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)