From dbeda417b4c473df143ef4ecffef3d14389bec62 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauld.mccarthy@gmail.com>
Date: Tue, 14 Jun 2016 16:51:15 +0100
Subject: [PATCH] New test case for overlapping writes. One more test case to
go.
---
test/test_imagewrapper.py | 107 +++++++++++---------------------------
1 file changed, 30 insertions(+), 77 deletions(-)
diff --git a/test/test_imagewrapper.py b/test/test_imagewrapper.py
index c0bdb5e3f..e51dfa491 100644
--- a/test/test_imagewrapper.py
+++ b/test/test_imagewrapper.py
@@ -663,11 +663,11 @@ def test_ImageWrapper_write_out(niters=150):
assert np.isclose(newHi, expHi)
print '--------------'
+
+def test_ImageWrapper_write_in_overlap(niters=150):
+ # This is HORRIBLE
-
-def best_ImageWrapper_write_in_overlap():
-
- # Generate an image with only two volumes. We're only
+ # Generate an image with just two volumes. We're only
# testing within-volume modifications here.
ndims = random.choice((2, 3, 4)) - 1
shape = np.random.randint(5, 60, size=ndims + 1)
@@ -683,27 +683,22 @@ def best_ImageWrapper_write_in_overlap():
data[..., i] = data[..., 0] * (i + 1)
# Generate a bunch of random coverages
- for i in range(1):
+ for i in range(niters):
# Generate a random coverage
cov = random_coverage(shape, vol_limit=1)
- print 'This is the coverage: {}'.format(cov)
-
- img = nib.Nifti1Image(data, np.eye(4))
- wrapper = imagewrap.ImageWrapper(img)
- applyCoverage(wrapper, cov)
- clo, chi = wrapper.dataRange
-
# Now, we'll simulate some writes
- # outside of the coverage area.
- for i in range(5):
+ # which are contained within, or
+ # overlap with, the initial coverage
+ for i in range(niters):
- # Generate some slices inside/overlapping
- # with the coverage area, making sure that
- # the slice covers at least two elements
+ # Generate some slices outside
+ # of the coverage area, making
+ # sure that the slice covers
+ # at least two elements
while True:
- slices = random_slices(cov, shape, np.random.choice(('in', 'overlap')))
+ slices = random_slices(cov, shape, random.choice(('in', 'overlap')))
slices[-1] = [0, 1]
sliceshape = [hi - lo for lo, hi in slices]
@@ -715,82 +710,40 @@ def best_ImageWrapper_write_in_overlap():
sliceshape = sliceshape[:-1]
break
- print '---------------'
- print 'Slice {}'.format(slices)
- # Expected wrapper coverage after the write
- expCov = imagewrap.adjustCoverage(cov[..., 0], slices)
-
- # Figure out the data range of the
- # expanded coverage (the original
- # coverage expanded to include this
- # slice).
- elo, ehi = coverageDataRange(data, cov, slices)
+ # Expected wrapper coverage after the write
+ # The write will invalidate the current
+ # known data range and coverage, so the
+ # expected coverage after the write will
+ # be the area covered by the write slice
+ nullCov = np.zeros(cov.shape)
+ nullCov[:] = np.nan
+ expCov = imagewrap.adjustCoverage(nullCov[..., 0], slices)
- # Test all data range possibilities:
- # - inside the existing range (clo < rlo < rhi < chi)
- # - encompassing the existing range (rlo < clo < chi < rhi)
- # - Overlapping the existing range (rlo < clo < rhi < chi)
- # (clo < rlo < chi < rhi)
- # - Outside of the existing range (clo < chi < rlo < rhi)
- # (rlo < rhi < clo < chi)
- loRanges = [rfloat(clo, chi),
- rfloat(elo - 100, elo),
- rfloat(elo - 100, elo),
- rfloat(clo, chi),
- rfloat(ehi, ehi + 100),
- rfloat(elo - 100, elo)]
+ for i in range(10):
- hiRanges = [rfloat(loRanges[0], chi),
- rfloat(ehi, ehi + 100),
- rfloat(clo, chi),
- rfloat(ehi, ehi + 100),
- rfloat(loRanges[4], ehi + 100),
- rfloat(loRanges[5], elo)]
-
- for rlo, rhi in zip(loRanges, hiRanges):
+ rlo = rfloat(data.min() - 100, data.max() + 100)
+ rhi = rfloat(rlo, data.max() + 100)
img = nib.Nifti1Image(np.copy(data), np.eye(4))
wrapper = imagewrap.ImageWrapper(img)
applyCoverage(wrapper, cov)
- print 'ndims', ndims
- print 'sliceshape', sliceshape
- print 'sliceobjs', sliceobjs
-
newData = np.linspace(rlo, rhi, np.prod(sliceshape))
newData = newData.reshape(sliceshape)
- # Make sure that the expected low/high values
- # are present in the data being written
- print 'Writing data (shape: {})'.format(newData.shape)
-
- oldCov = wrapper.coverage(0)
+ print 'Old range: {} - {}'.format(*wrapper.dataRange)
wrapper[tuple(sliceobjs)] = newData
- expLo, expHi = coverageDataRange(img.get_data(), cov, slices)
+ newCov = wrapper.coverage(0)
newLo, newHi = wrapper.dataRange
- print 'Old range: {} - {}'.format(clo, chi)
- print 'Sim range: {} - {}'.format(rlo, rhi)
- print 'Exp range: {} - {}'.format(expLo, expHi)
- print 'NewDat range: {} - {}'.format(newData.min(), newData.max())
- print 'Data range: {} - {}'.format(data.min(), data.max())
- print 'Expand range: {} - {}'.format(elo, ehi)
- print 'New range: {} - {}'.format(newLo, newHi)
-
- newCov = wrapper.coverage(0)
- print 'Old coverage: {}'.format(oldCov)
- print 'New coverage: {}'.format(newCov)
- print 'Expected coverage: {}'.format(expCov)
- print
- print
+ print 'Expected range: {} - {}'.format(rlo, rhi)
+ print 'New range: {} - {}'.format(newLo, newHi)
assert np.all(newCov == expCov)
- assert np.isclose(newLo, expLo)
- assert np.isclose(newHi, expHi)
-
- print '--------------'
+ assert np.isclose(newLo, rlo)
+ assert np.isclose(newHi, rhi)
--
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