def setUp(self):
"""
Create a blockwise fileset to test with.
"""
if platform.system() == 'Windows':
# On windows, there are errors, and we make no attempt to solve them (at the moment).
raise nose.SkipTest
try:
BlockwiseFileset._prepare_system()
except ValueError:
# If the system isn't configured to allow lots of open files, we can't run this test.
raise nose.SkipTest
testConfig = \
"""
{
"_schema_name" : "blockwise-fileset-description",
"_schema_version" : 1.0,
"name" : "synapse_small",
"format" : "hdf5",
"axes" : "txyzc",
"shape" : [1,400,400,100,1],
"dtype" : "numpy.uint8",
"block_shape" : [1, 50, 50, 50, 100],
"block_file_name_format" : "cube{roiString}.h5/volume/data"
}
"""
self.tempDir = tempfile.mkdtemp()
self.configpath = os.path.join(self.tempDir, "config.json")
logger.debug("Loading config file...")
with open(self.configpath, 'w') as f:
f.write(testConfig)
logger.debug("Creating random test data...")
bfs = BlockwiseFileset(self.configpath, 'a')
dataShape = tuple(bfs.description.shape)
self.data = numpy.random.randint(255,
size=dataShape).astype(numpy.uint8)
logger.debug("Writing test data...")
datasetRoi = ([0, 0, 0, 0, 0], dataShape)
bfs.writeData(datasetRoi, self.data)
block_starts = getIntersectingBlocks(bfs.description.block_shape,
datasetRoi)
for block_start in block_starts:
bfs.setBlockStatus(block_start, BlockwiseFileset.BLOCK_AVAILABLE)
bfs.close()
def setUp(self):
"""
Create a blockwise fileset to test with.
"""
if platform.system() == 'Windows':
# On windows, there are errors, and we make no attempt to solve them (at the moment).
raise nose.SkipTest
try:
BlockwiseFileset._prepare_system()
except ValueError:
# If the system isn't configured to allow lots of open files, we can't run this test.
raise nose.SkipTest
testConfig = \
"""
{
"_schema_name" : "blockwise-fileset-description",
"_schema_version" : 1.0,
"name" : "synapse_small",
"format" : "hdf5",
"axes" : "txyzc",
"shape" : [1,400,400,100,1],
"dtype" : "numpy.uint8",
"block_shape" : [1, 50, 50, 50, 100],
"block_file_name_format" : "cube{roiString}.h5/volume/data"
}
"""
self.tempDir = tempfile.mkdtemp()
self.configpath = os.path.join(self.tempDir, "config.json")
logger.debug( "Loading config file..." )
with open(self.configpath, 'w') as f:
f.write(testConfig)
logger.debug( "Creating random test data..." )
bfs = BlockwiseFileset( self.configpath, 'a' )
dataShape = tuple(bfs.description.shape)
self.data = numpy.random.randint( 255, size=dataShape ).astype(numpy.uint8)
logger.debug( "Writing test data..." )
datasetRoi = ([0,0,0,0,0], dataShape)
bfs.writeData( datasetRoi, self.data )
block_starts = getIntersectingBlocks(bfs.description.block_shape, datasetRoi)
for block_start in block_starts:
bfs.setBlockStatus(block_start, BlockwiseFileset.BLOCK_AVAILABLE)
bfs.close()
def setUp(self):
"""
Create a blockwise fileset to test with.
"""
if "Darwin" in platform.platform():
# For unknown reasons, blockwise fileset tests fail due to strange "too many files" errors on mac
raise nose.SkipTest
testConfig = """
{
"_schema_name" : "blockwise-fileset-description",
"_schema_version" : 1.0,
"name" : "synapse_small",
"format" : "hdf5",
"axes" : "txyzc",
"shape" : [1,400,400,100,1],
"dtype" : "numpy.uint8",
"block_shape" : [1, 50, 50, 50, 100],
"block_file_name_format" : "cube{roiString}.h5/volume/data"
}
"""
self.tempDir = tempfile.mkdtemp()
self.configpath = os.path.join(self.tempDir, "config.json")
logger.debug("Loading config file...")
with open(self.configpath, "w") as f:
f.write(testConfig)
logger.debug("Creating random test data...")
bfs = BlockwiseFileset(self.configpath, "a")
dataShape = tuple(bfs.description.shape)
self.data = numpy.random.randint(255, size=dataShape).astype(numpy.uint8)
logger.debug("Writing test data...")
datasetRoi = ([0, 0, 0, 0, 0], dataShape)
bfs.writeData(datasetRoi, self.data)
block_starts = getIntersectingBlocks(bfs.description.block_shape, datasetRoi)
for block_start in block_starts:
bfs.setBlockStatus(block_start, BlockwiseFileset.BLOCK_AVAILABLE)
bfs.close()
def _prepareDestination(self):
"""
- If the result file doesn't exist yet, create it (and the dataset)
- If the result file already exists, return a list of the rois that
are NOT needed (their data already exists in the final output)
"""
originalDescription = BlockwiseFileset.readDescription(self.OutputDatasetDescription.value)
datasetDescription = copy.deepcopy(originalDescription)
# Modify description fields as needed
# -- axes
datasetDescription.axes = "".join( self.Input.meta.getTaggedShape().keys() )
assert set(originalDescription.axes) == set( datasetDescription.axes ), \
"Can't prepare destination dataset: original dataset description listed " \
"axes as {}, but actual output axes are {}".format( originalDescription.axes, datasetDescription.axes )
# -- shape
datasetDescription.view_shape = list(self.Input.meta.shape)
# -- block_shape
assert originalDescription.block_shape is not None
originalBlockDims = collections.OrderedDict( zip( originalDescription.axes, originalDescription.block_shape ) )
datasetDescription.block_shape = map( lambda a: originalBlockDims[a], datasetDescription.axes )
datasetDescription.block_shape = map( min, zip( datasetDescription.block_shape, self.Input.meta.shape ) )
# -- chunks
if originalDescription.chunks is not None:
originalChunkDims = collections.OrderedDict( zip( originalDescription.axes, originalDescription.chunks ) )
datasetDescription.chunks = map( lambda a: originalChunkDims[a], datasetDescription.axes )
datasetDescription.chunks = map( min, zip( datasetDescription.chunks, self.Input.meta.shape ) )
# -- dtype
if datasetDescription.dtype != self.Input.meta.dtype:
dtype = self.Input.meta.dtype
if type(dtype) is numpy.dtype:
dtype = dtype.type
datasetDescription.dtype = dtype().__class__.__name__
# Create a unique hash for this blocking scheme.
# If it changes, we can't use any previous data.
sha = hashlib.sha1()
sha.update( str( tuple( datasetDescription.block_shape) ) )
sha.update( datasetDescription.axes )
sha.update( datasetDescription.block_file_name_format )
datasetDescription.hash_id = sha.hexdigest()
if datasetDescription != originalDescription:
descriptionFilePath = self.OutputDatasetDescription.value
logger.info( "Overwriting dataset description: {}".format( descriptionFilePath ) )
BlockwiseFileset.writeDescription(descriptionFilePath, datasetDescription)
with open( descriptionFilePath, 'r' ) as f:
logger.info( f.read() )
# Now open the dataset
blockwiseFileset = BlockwiseFileset( self.OutputDatasetDescription.value )
taskInfos = self._prepareTaskInfos( blockwiseFileset.getAllBlockRois() )
if blockwiseFileset.description.hash_id != originalDescription.hash_id:
# Something about our blocking scheme changed.
# Make sure all blocks are marked as NOT available.
# (Just in case some were left over from a previous run.)
for roi in taskInfos.keys():
blockwiseFileset.setBlockStatus( roi[0], BlockwiseFileset.BLOCK_NOT_AVAILABLE )
return blockwiseFileset, taskInfos
def _prepareDestination(self):
"""
- If the result file doesn't exist yet, create it (and the dataset)
- If the result file already exists, return a list of the rois that
are NOT needed (their data already exists in the final output)
"""
originalDescription = BlockwiseFileset.readDescription(
self.OutputDatasetDescription.value)
datasetDescription = copy.deepcopy(originalDescription)
# Modify description fields as needed
# -- axes
datasetDescription.axes = "".join(
self.Input.meta.getTaggedShape().keys())
assert set(originalDescription.axes) == set( datasetDescription.axes ), \
"Can't prepare destination dataset: original dataset description listed " \
"axes as {}, but actual output axes are {}".format( originalDescription.axes, datasetDescription.axes )
# -- shape
datasetDescription.view_shape = list(self.Input.meta.shape)
# -- block_shape
assert originalDescription.block_shape is not None
originalBlockDims = collections.OrderedDict(
zip(originalDescription.axes, originalDescription.block_shape))
datasetDescription.block_shape = map(lambda a: originalBlockDims[a],
datasetDescription.axes)
datasetDescription.block_shape = map(
min, zip(datasetDescription.block_shape, self.Input.meta.shape))
# -- chunks
if originalDescription.chunks is not None:
originalChunkDims = collections.OrderedDict(
zip(originalDescription.axes, originalDescription.chunks))
datasetDescription.chunks = map(lambda a: originalChunkDims[a],
datasetDescription.axes)
datasetDescription.chunks = map(
min, zip(datasetDescription.chunks, self.Input.meta.shape))
# -- dtype
if datasetDescription.dtype != self.Input.meta.dtype:
dtype = self.Input.meta.dtype
if type(dtype) is numpy.dtype:
dtype = dtype.type
datasetDescription.dtype = dtype().__class__.__name__
# Create a unique hash for this blocking scheme.
# If it changes, we can't use any previous data.
sha = hashlib.sha1()
sha.update(str(tuple(datasetDescription.block_shape)))
sha.update(datasetDescription.axes)
sha.update(datasetDescription.block_file_name_format)
datasetDescription.hash_id = sha.hexdigest()
if datasetDescription != originalDescription:
descriptionFilePath = self.OutputDatasetDescription.value
logger.info("Overwriting dataset description: {}".format(
descriptionFilePath))
BlockwiseFileset.writeDescription(descriptionFilePath,
datasetDescription)
with open(descriptionFilePath, 'r') as f:
logger.info(f.read())
# Now open the dataset
blockwiseFileset = BlockwiseFileset(
self.OutputDatasetDescription.value)
taskInfos = self._prepareTaskInfos(blockwiseFileset.getAllBlockRois())
if blockwiseFileset.description.hash_id != originalDescription.hash_id:
# Something about our blocking scheme changed.
# Make sure all blocks are marked as NOT available.
# (Just in case some were left over from a previous run.)
for roi in taskInfos.keys():
blockwiseFileset.setBlockStatus(
roi[0], BlockwiseFileset.BLOCK_NOT_AVAILABLE)
return blockwiseFileset, taskInfos
