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Commit dbeda417 authored by Paul McCarthy's avatar Paul McCarthy
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New test case for overlapping writes. One more test case to go.

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test/test_imagewrapper.py
+ 30
− 77
View file @ dbeda417
...@@ -663,11 +663,11 @@ def test_ImageWrapper_write_out(niters=150): ...@@ -663,11 +663,11 @@ def test_ImageWrapper_write_out(niters=150):
assert np.isclose(newHi, expHi) assert np.isclose(newHi, expHi)
print '--------------' print '--------------'
def test_ImageWrapper_write_in_overlap(niters=150):
# This is HORRIBLE
# Generate an image with just two volumes. We're only
def best_ImageWrapper_write_in_overlap():
# Generate an image with only two volumes. We're only
# testing within-volume modifications here. # testing within-volume modifications here.
ndims = random.choice((2, 3, 4)) - 1 ndims = random.choice((2, 3, 4)) - 1
shape = np.random.randint(5, 60, size=ndims + 1) shape = np.random.randint(5, 60, size=ndims + 1)
...@@ -683,27 +683,22 @@ def best_ImageWrapper_write_in_overlap(): ...@@ -683,27 +683,22 @@ def best_ImageWrapper_write_in_overlap():
data[..., i] = data[..., 0] * (i + 1) data[..., i] = data[..., 0] * (i + 1)
# Generate a bunch of random coverages # Generate a bunch of random coverages
for i in range(1): for i in range(niters):
# Generate a random coverage # Generate a random coverage
cov = random_coverage(shape, vol_limit=1) 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 # Now, we'll simulate some writes
# outside of the coverage area. # which are contained within, or
for i in range(5): # overlap with, the initial coverage
for i in range(niters):
# Generate some slices inside/overlapping # Generate some slices outside
# with the coverage area, making sure that # of the coverage area, making
# the slice covers at least two elements # sure that the slice covers
# at least two elements
while True: 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] slices[-1] = [0, 1]
sliceshape = [hi - lo for lo, hi in slices] sliceshape = [hi - lo for lo, hi in slices]
...@@ -715,82 +710,40 @@ def best_ImageWrapper_write_in_overlap(): ...@@ -715,82 +710,40 @@ def best_ImageWrapper_write_in_overlap():
sliceshape = sliceshape[:-1] sliceshape = sliceshape[:-1]
break break
print '---------------'
print 'Slice {}'.format(slices)
# Expected wrapper coverage after the write # Expected wrapper coverage after the write
expCov = imagewrap.adjustCoverage(cov[..., 0], slices) # The write will invalidate the current
# known data range and coverage, so the
# Figure out the data range of the # expected coverage after the write will
# expanded coverage (the original # be the area covered by the write slice
# coverage expanded to include this nullCov = np.zeros(cov.shape)
# slice). nullCov[:] = np.nan
elo, ehi = coverageDataRange(data, cov, slices) expCov = imagewrap.adjustCoverage(nullCov[..., 0], slices)
# Test all data range possibilities: for i in range(10):
# - 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)]
hiRanges = [rfloat(loRanges[0], chi), rlo = rfloat(data.min() - 100, data.max() + 100)
rfloat(ehi, ehi + 100), rhi = rfloat(rlo, data.max() + 100)
rfloat(clo, chi),
rfloat(ehi, ehi + 100),
rfloat(loRanges[4], ehi + 100),
rfloat(loRanges[5], elo)]
for rlo, rhi in zip(loRanges, hiRanges):
img = nib.Nifti1Image(np.copy(data), np.eye(4)) img = nib.Nifti1Image(np.copy(data), np.eye(4))
wrapper = imagewrap.ImageWrapper(img) wrapper = imagewrap.ImageWrapper(img)
applyCoverage(wrapper, cov) applyCoverage(wrapper, cov)
print 'ndims', ndims
print 'sliceshape', sliceshape
print 'sliceobjs', sliceobjs
newData = np.linspace(rlo, rhi, np.prod(sliceshape)) newData = np.linspace(rlo, rhi, np.prod(sliceshape))
newData = newData.reshape(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) print 'Old range: {} - {}'.format(*wrapper.dataRange)
oldCov = wrapper.coverage(0)
wrapper[tuple(sliceobjs)] = newData wrapper[tuple(sliceobjs)] = newData
expLo, expHi = coverageDataRange(img.get_data(), cov, slices) newCov = wrapper.coverage(0)
newLo, newHi = wrapper.dataRange newLo, newHi = wrapper.dataRange
print 'Old range: {} - {}'.format(clo, chi) print 'Expected range: {} - {}'.format(rlo, rhi)
print 'Sim range: {} - {}'.format(rlo, rhi) print 'New range: {} - {}'.format(newLo, newHi)
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
assert np.all(newCov == expCov) assert np.all(newCov == expCov)
assert np.isclose(newLo, expLo) assert np.isclose(newLo, rlo)
assert np.isclose(newHi, expHi) assert np.isclose(newHi, rhi)
print '--------------'
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