From 5870a4f6407a3b6083ec0eaedbbf8a23598045a5 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauld.mccarthy@gmail.com>
Date: Sun, 16 Apr 2017 13:27:03 +0100
Subject: [PATCH] featimage module unit tests. Needs more.
---
tests/__init__.py | 99 +++++++++++++++++++++++---
tests/test_featdesign.py | 30 +++-----
tests/test_featimage.py | 150 +++++++++++++++++++++++++++++++++++++++
3 files changed, 252 insertions(+), 27 deletions(-)
create mode 100644 tests/test_featimage.py
diff --git a/tests/__init__.py b/tests/__init__.py
index 3b89f3a30..c35bd760b 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -7,12 +7,14 @@
"""Unit tests for ``fslpy``. """
-import os
-import shutil
-import tempfile
-import os.path as op
-import numpy as np
-import nibabel as nib
+import os
+import glob
+import shutil
+import tempfile
+import itertools as it
+import os.path as op
+import numpy as np
+import nibabel as nib
def testdir(contents=None):
@@ -102,14 +104,95 @@ def cleardir(dir):
elif op.isdir(f): shutil.rmtree(f)
-def make_random_image(filename, dims=(10, 10, 10)):
+def make_random_image(filename, dims=(10, 10, 10), xform=None):
"""Creates a NIFTI1 image with random data, saves and
returns it.
"""
+ if xform is None:
+ xform = np.eye(4)
+
data = np.array(np.random.random(dims) * 100, dtype=np.float32)
- img = nib.Nifti1Image(data, np.eye(4))
+ img = nib.Nifti1Image(data, xform)
nib.save(img, filename)
return img
+
+def make_mock_feat_analysis(featdir,
+ testdir,
+ shape4D,
+ xform=None,
+ indata=True,
+ voxEVs=True,
+ pes=True,
+ copes=True,
+ zstats=True,
+ residuals=True,
+ clustMasks=True):
+
+ if xform is None:
+ xform = np.eye(4)
+
+ timepoints = shape4D[ 3]
+ shape = shape4D[:3]
+
+ src = featdir
+ dest = op.join(testdir, op.basename(featdir))
+ featdir = dest
+
+ shutil.copytree(src, dest)
+
+ if indata:
+ filtfunc = op.join(featdir, 'filtered_func_data.nii.gz')
+ make_random_image(filtfunc, shape4D, xform)
+
+ # and some dummy voxelwise EV files
+ if voxEVs:
+ voxFiles = list(it.chain(
+ glob.glob(op.join(featdir, 'designVoxelwiseEV*nii.gz')),
+ glob.glob(op.join(featdir, 'InputConfoundEV*nii.gz'))))
+
+ for i, vf in enumerate(voxFiles):
+
+ # Each voxel contains range(i, i + timepoints),
+ # offset by the flattened voxel index
+ data = np.meshgrid(*[range(s) for s in shape], indexing='ij')
+ data = np.ravel_multi_index(data, shape)
+ data = data.reshape(list(shape) + [1]).repeat(timepoints, axis=3)
+ data[..., :] += range(i, i + timepoints)
+
+ nib.save(nib.nifti1.Nifti1Image(data, xform), vf)
+
+ otherFiles = []
+ otherShapes = []
+
+ if pes:
+ files = glob.glob(op.join(featdir, 'stats', 'pe*nii.gz'))
+ otherFiles .extend(files)
+ otherShapes.extend([shape] * len(files))
+
+ if copes:
+ files = glob.glob(op.join(featdir, 'stats', 'cope*nii.gz'))
+ otherFiles .extend(files)
+ otherShapes.extend([shape] * len(files))
+
+ if zstats:
+ files = glob.glob(op.join(featdir, 'stats', 'zstat*nii.gz'))
+ otherFiles .extend(files)
+ otherShapes.extend([shape] * len(files))
+
+ if residuals:
+ files = glob.glob(op.join(featdir, 'stats', 'res4d.nii.gz'))
+ otherFiles .extend(files)
+ otherShapes.extend([shape4D])
+
+ if clustMasks:
+ files = glob.glob(op.join(featdir, 'cluster_mask*nii.gz'))
+ otherFiles .extend(files)
+ otherShapes.extend([shape] * len(files))
+
+ for f, s in zip(otherFiles, otherShapes):
+ make_random_image(f, s, xform)
+
+ return featdir
diff --git a/tests/test_featdesign.py b/tests/test_featdesign.py
index 5b392bd2e..412503e55 100644
--- a/tests/test_featdesign.py
+++ b/tests/test_featdesign.py
@@ -136,25 +136,17 @@ def test_FEATFSFDesign_firstLevelVoxelwiseEV(seed):
# and generate some dummy data for
# the voxelwise EVs.
featdir = op.join(testdir, '1stlevel_2.feat')
- shape = (64, 64, 5)
- timepoints = 45
-
- shutil.copytree(template, featdir)
-
- voxFiles = list(it.chain(
- glob.glob(op.join(featdir, 'designVoxelwiseEV*nii.gz')),
- glob.glob(op.join(featdir, 'InputConfoundEV*nii.gz'))))
-
- for i, vf in enumerate(voxFiles):
-
- # Each voxel contains range(i, i + timepoints),
- # offset by the flattened voxel index
- data = np.meshgrid(*[range(s) for s in shape], indexing='ij')
- data = np.ravel_multi_index(data, shape)
- data = data.reshape(list(shape) + [1]).repeat(timepoints, axis=3)
- data[..., :] += range(i, i + timepoints)
-
- fslimage.Image(data).save(vf)
+ 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,
+ clusterMasks=False)
# Now load the design, and make sure that
# the voxel EVs are filled correctly
diff --git a/tests/test_featimage.py b/tests/test_featimage.py
new file mode 100644
index 000000000..edcc5a187
--- /dev/null
+++ b/tests/test_featimage.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+#
+# test_featimage.py -
+#
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+#
+
+
+import os.path as op
+import os
+import itertools as it
+import glob
+import shutil
+
+import numpy as np
+
+import pytest
+
+import tests
+import fsl.data.featimage as featimage
+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_2.feat',
+ '2ndlevel_1.gfeat/cope1.feat', '2ndlevel_1.gfeat/cope2.feat',
+ '2ndlevel_2.gfeat/cope1.feat', '2ndlevel_2.gfeat/cope2.feat']
+shapes = [(64, 64, 5, 45),
+ (64, 64, 5, 45),
+ (64, 64, 5, 45),
+ (91, 109, 91, 3),
+ (91, 109, 91, 3),
+ (91, 109, 91, 3),
+ (91, 109, 91, 3)]
+xforms = [[[-4, 0, 0, 0],
+ [ 0, 4, 0, 0],
+ [ 0, 0, 6, 0],
+ [ 0, 0, 0, 1]]] * 3 + \
+ [[[-2, 0, 0, 90],
+ [ 0, 2, 0, -126],
+ [ 0, 0, 2, -72],
+ [ 0, 0, 0, 1]]] * 4
+xforms = [np.array(xf) for xf in xforms]
+
+TEST_ANALYSES = list(zip(
+ featdirs,
+ shapes,
+ xforms))
+
+
+def test_FEATImage_attributes():
+
+ for i, featdir in enumerate(featdirs):
+ with tests.testdir() as testdir:
+
+ featdir = tests.make_mock_feat_analysis(
+ op.join(datadir, featdir),
+ testdir,
+ shapes[i],
+ xforms[i],
+ pes=False,
+ copes=False,
+ zstats=False,
+ residuals=False,
+ clustMasks=False)
+
+ # 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 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 test_FEATImage_imageAccessors():
+
+ for i, featdir in enumerate(featdirs):
+ with tests.testdir() as testdir:
+ featdir = tests.make_mock_feat_analysis(
+ op.join(datadir, featdir),
+ testdir,
+ shapes[i],
+ xforms[i])
+
+ shape4D = shapes[ i]
+ 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_firstLevel():
+ featdir = ''
+ fi = featimage.FEATImage(featdir)
+ fi.fit(0, (0, 0, 0))
+
+
+def test_FEATImage_fit_higherLevel():
+ featdir = ''
+ fi = featimage.FEATImage(featdir)
+ fi.fit(0, (0, 0, 0))
+
+
+def test_FEATImage_partialFit():
+ featdir = ''
+ fi = featimage.FEATImage(featdir)
+ fi.fit(0, (0, 0, 0))
+
+
+def test_FEATImage_nostats():
+ pass
--
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