def test_fromMultipleArrays(self):
ary = arange(8, dtype=dtype('int16')).reshape((2, 4))
ary2 = arange(8, 16, dtype=dtype('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArrays([ary, ary2])
seriesvals = series.collect()
seriesary = series.pack()
# check ordering of keys
assert_equals((0, 0), seriesvals[0][0]) # first key
assert_equals((1, 0), seriesvals[1][0]) # second key
assert_equals((3, 0), seriesvals[3][0])
assert_equals((0, 1), seriesvals[4][0])
assert_equals((3, 1), seriesvals[7][0])
# check dimensions tuple is reversed from numpy shape
assert_equals(ary.shape[::-1], series.dims.count)
# check that values are in original order, with subsequent point concatenated in values
collectedvals = array([kv[1] for kv in seriesvals], dtype=dtype('int16'))
assert_true(array_equal(ary.ravel(), collectedvals[:, 0]))
assert_true(array_equal(ary2.ravel(), collectedvals[:, 1]))
# check that packing returns concatenation of input arrays, with time as first dimension
assert_true(array_equal(ary.T, seriesary[0]))
assert_true(array_equal(ary2.T, seriesary[1]))
def test_fromArrays(self):
ary = arange(8, dtype=dtype('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArrays(ary)
seriesvals = series.collect()
seriesary = series.pack()
# check ordering of keys
assert_equals((0, 0), seriesvals[0][0]) # first key
assert_equals((1, 0), seriesvals[1][0]) # second key
assert_equals((2, 0), seriesvals[2][0])
assert_equals((3, 0), seriesvals[3][0])
assert_equals((0, 1), seriesvals[4][0])
assert_equals((1, 1), seriesvals[5][0])
assert_equals((2, 1), seriesvals[6][0])
assert_equals((3, 1), seriesvals[7][0])
# check dimensions tuple is reversed from numpy shape
assert_equals(ary.shape[::-1], series.dims.count)
# check that values are in original order
collectedvals = array([kv[1] for kv in seriesvals], dtype=dtype('int16')).ravel()
assert_true(array_equal(ary.ravel(), collectedvals))
# check that packing returns transpose of original array
assert_true(array_equal(ary.T, seriesary))
def loadSeriesFromArray(self, values, index=None, npartitions=None):
"""
Load Series data from a local array
Parameters
----------
values : list or ndarray
A list of 1d numpy arrays, or a single 2d numpy array
index : array-like, optional, deafult = None
Index to set for Series object, if None will use linear indices.
npartitions : position int, optional, default = None
Number of partitions for RDD, if unspecified will use
default parallelism.
"""
from numpy import ndarray, asarray
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc)
if not npartitions:
npartitions = self._sc.defaultParallelism
if isinstance(values, list):
values = asarray(values)
if isinstance(values, ndarray) and values.ndim > 1:
values = list(values)
data = loader.fromArrays(values, npartitions=npartitions)
if index:
data.index = index
return data
def loadSeriesFromArray(self, values, index=None, npartitions=None):
"""
Load Series data from a local array
Parameters
----------
values : list or ndarray
A list of 1d numpy arrays, or a single 2d numpy array
index : array-like, optional, deafult = None
Index to set for Series object, if None will use linear indices.
npartitions : position int, optional, default = None
Number of partitions for RDD, if unspecified will use
default parallelism.
"""
from numpy import ndarray, asarray
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc)
if not npartitions:
npartitions = self._sc.defaultParallelism
if isinstance(values, list):
values = asarray(values)
if isinstance(values, ndarray) and values.ndim > 1:
values = list(values)
data = loader.fromArrays(values, npartitions=npartitions)
if index:
data.index = index
return data
def test_fromArrays(self):
ary = arange(8, dtype=dtypeFunc('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArraysAsImages(ary)
seriesVals = series.collect()
seriesAry = series.pack()
# check ordering of keys
assert_equals((0, 0), seriesVals[0][0]) # first key
assert_equals((1, 0), seriesVals[1][0]) # second key
assert_equals((2, 0), seriesVals[2][0])
assert_equals((3, 0), seriesVals[3][0])
assert_equals((0, 1), seriesVals[4][0])
assert_equals((1, 1), seriesVals[5][0])
assert_equals((2, 1), seriesVals[6][0])
assert_equals((3, 1), seriesVals[7][0])
# check dimensions tuple is reversed from numpy shape
assert_equals(ary.shape[::-1], series.dims.count)
# check that values are in original order
collectedVals = array([kv[1] for kv in seriesVals],
dtype=dtypeFunc('int16')).ravel()
assert_true(array_equal(ary.ravel(), collectedVals))
# check that packing returns transpose of original array
assert_true(array_equal(ary.T, seriesAry))
def _run_roundtrip_tst(self, testCount, arrays, blockSize):
print "Running TestSeriesBinaryWriteFromStack roundtrip test #%d" % testCount
insubdir = os.path.join(self.outputdir, 'input%d' % testCount)
os.mkdir(insubdir)
outsubdir = os.path.join(self.outputdir, 'output%d' % testCount)
#os.mkdir(outsubdir)
for aryCount, array in enumerate(arrays):
# array.tofile always writes in column-major order...
array.tofile(os.path.join(insubdir, "img%02d.stack" % aryCount))
# ... but we will read and interpret these as though they are in row-major order
dims = list(arrays[0].shape)
dims.reverse()
underTest = SeriesLoader(self.sc)
underTest.saveFromStack(insubdir, outsubdir, dims, blockSize=blockSize, datatype=str(arrays[0].dtype))
roundtripped = underTest.fromBinary(outsubdir).collect()
for serieskeys, seriesvalues in roundtripped:
for seriesidx, seriesval in enumerate(seriesvalues):
#print "seriesidx: %d; serieskeys: %s; seriesval: %g" % (seriesidx, serieskeys, seriesval)
# flip indices again for row vs col-major insanity
arykeys = list(serieskeys)
arykeys.reverse()
msg = "Failure on test #%d, time point %d, indices %s" % (testCount, seriesidx, str(tuple(arykeys)))
try:
assert_almost_equal(arrays[seriesidx][tuple(arykeys)], seriesval, places=4)
except AssertionError, e:
raise AssertionError(msg, e)
def test_fromMultipleArrays(self):
ary = arange(8, dtype=dtypeFunc('int16')).reshape((2, 4))
ary2 = arange(8, 16, dtype=dtypeFunc('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArraysAsImages([ary, ary2])
seriesVals = series.collect()
seriesAry = series.pack()
# check ordering of keys
assert_equals((0, 0), seriesVals[0][0]) # first key
assert_equals((1, 0), seriesVals[1][0]) # second key
assert_equals((3, 0), seriesVals[3][0])
assert_equals((0, 1), seriesVals[4][0])
assert_equals((3, 1), seriesVals[7][0])
# check dimensions tuple is reversed from numpy shape
assert_equals(ary.shape[::-1], series.dims.count)
# check that values are in original order, with subsequent point concatenated in values
collectedVals = array([kv[1] for kv in seriesVals],
dtype=dtypeFunc('int16'))
assert_true(array_equal(ary.ravel(), collectedVals[:, 0]))
assert_true(array_equal(ary2.ravel(), collectedVals[:, 1]))
# check that packing returns concatenation of input arrays, with time as first dimension
assert_true(array_equal(ary.T, seriesAry[0]))
assert_true(array_equal(ary2.T, seriesAry[1]))
def loadSeries(self, datapath, nkeys=None, nvalues=None, inputformat='binary', minPartitions=None,
conffile='conf.json', keytype=None, valuetype=None):
"""
Loads a Series object from data stored as text or binary files.
Supports single files or multiple files stored on a local file system, a networked file system (mounted
and available on all cluster nodes), Amazon S3, or HDFS.
Parameters
----------
datapath: string
Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
including scheme. A datapath argument may include a single '*' wildcard character in the filename. Examples
of valid datapaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
"/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".
nkeys: int, optional (but required if `inputformat` is 'text')
dimensionality of data keys. (For instance, (x,y,z) keyed data for 3-dimensional image timeseries data.)
For text data, number of keys must be specified in this parameter; for binary data, number of keys must be
specified either in this parameter or in a configuration file named by the 'conffile' argument if this
parameter is not set.
nvalues: int, optional (but required if `inputformat` is 'text')
Number of values expected to be read. For binary data, nvalues must be specified either in this parameter
or in a configuration file named by the 'conffile' argument if this parameter is not set.
inputformat: {'text', 'binary'}. optional, default 'binary'
Format of data to be read.
minPartitions: int, optional
Explicitly specify minimum number of Spark partitions to be generated from this data. Used only for
text data. Default is to use minParallelism attribute of Spark context object.
conffile: string, optional, default 'conf.json'
Path to JSON file with configuration options including 'nkeys', 'nvalues', 'keytype', and 'valuetype'.
If a file is not found at the given path, then the base directory given in 'datafile'
will also be checked. Parameters `nkeys` or `nvalues` that are specified as explicit arguments to this
method will take priority over those found in conffile if both are present.
Returns
-------
data: thunder.rdds.Series
A newly-created Series object, wrapping an RDD of series data. This RDD will have as keys an n-tuple
of int, with n given by `nkeys` or the configuration passed in `conffile`. RDD values will be a numpy
array of length `nvalues` (or as specified in the passed configuration file).
"""
checkparams(inputformat, ['text', 'binary'])
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputformat.lower() == 'text':
data = loader.fromText(datapath, nkeys=nkeys)
else:
# must be either 'text' or 'binary'
data = loader.fromBinary(datapath, conffilename=conffile, nkeys=nkeys, nvalues=nvalues,
keytype=keytype, valuetype=valuetype)
return data
def _run_roundtrip_exception_tst(self, nimages, aryShape, dtypeSpec,
sizeSpec):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(
nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
assert_raises(ValueError, series.toBlocks, sizeSpec)
def _run_fromFishTif(self, blocksize="150M"):
imagepath = TestSeriesLoader._findSourceTreeDir("utils/data/fish/tif-stack")
series = SeriesLoader(self.sc).fromMultipageTif(imagepath, blockSize=blocksize)
series_ary = series.pack()
series_ary_xpose = series.pack(transpose=True)
assert_equals((76, 87, 2), series.dims.count)
assert_equals((20, 76, 87, 2), series_ary.shape)
assert_equals((20, 2, 87, 76), series_ary_xpose.shape)
def _run_tst_fromBinary(self, useConfJson=False):
# run this as a single big test so as to avoid repeated setUp and tearDown of the spark context
# data will be a sequence of test data
# all keys and all values in a test data item must be of the same length
# keys get converted to ints regardless of raw input format
DATA = [
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3], [5, 6, 7]], [[11], [12]], 'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int16', 'int32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int32', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]], 'int16', 'float32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]], 'float32', 'float32'),
SeriesBinaryTestData.fromArrays([[2, 3, 4]], [[11.0, 12.0, 13.0]], 'float32', 'float32'),
]
for itemidx, item in enumerate(DATA):
outSubdir = os.path.join(self.outputdir, 'input%d' % itemidx)
os.mkdir(outSubdir)
fname = os.path.join(outSubdir, 'inputfile%d.bin' % itemidx)
with open(fname, 'wb') as f:
item.writeToFile(f)
loader = SeriesLoader(self.sc)
if not useConfJson:
series = loader.fromBinary(outSubdir, nkeys=item.nkeys, nvalues=item.nvals, keyType=str(item.keyDtype),
valueType=str(item.valDtype))
else:
# write configuration file
conf = {'input': outSubdir,
'nkeys': item.nkeys, 'nvalues': item.nvals,
'valuetype': str(item.valDtype), 'keytype': str(item.keyDtype)}
with open(os.path.join(outSubdir, "conf.json"), 'wb') as f:
json.dump(conf, f, indent=2)
series = loader.fromBinary(outSubdir)
seriesData = series.rdd.collect()
expectedData = item.data
assert_equals(len(expectedData), len(seriesData),
"Differing numbers of k/v pairs in item %d; expected %d, got %d" %
(itemidx, len(expectedData), len(seriesData)))
for expected, actual in zip(expectedData, seriesData):
expectedKeys = tuple(expected[0])
expectedType = smallestFloatType(item.valDtype)
expectedVals = array(expected[1], dtype=expectedType)
assert_equals(expectedKeys, actual[0],
"Key mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedKeys), str(actual[0])))
assert_true(allclose(expectedVals, actual[1]),
"Value mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedVals), str(actual[1])))
assert_equals(expectedType, str(actual[1].dtype),
"Value type mismatch in item %d; expected %s, got %s" %
(itemidx, expectedType, str(actual[1].dtype)))
def _run_fromFishTif(self, blocksize):
imagepath = TestSeriesLoader._findSourceTreeDir("utils/data/fish/images")
series = SeriesLoader(self.sc).fromTif(imagepath, blockSize=blocksize)
assert_equals('float16', series._dtype)
seriesAry = series.pack()
seriesAry_xpose = series.pack(transpose=True)
assert_equals('float16', str(seriesAry.dtype))
assert_equals((76, 87, 2), series.dims.count)
assert_equals((20, 76, 87, 2), seriesAry.shape)
assert_equals((20, 2, 87, 76), seriesAry_xpose.shape)
def _run_fromFishTif(self, blocksize):
imagepath = TestSeriesLoader._findSourceTreeDir(
"utils/data/fish/tif-stack")
series = SeriesLoader(self.sc).fromTif(imagepath, blockSize=blocksize)
assert_equals('float16', series._dtype)
seriesAry = series.pack()
seriesAry_xpose = series.pack(transpose=True)
assert_equals('float16', str(seriesAry.dtype))
assert_equals((76, 87, 2), series.dims.count)
assert_equals((20, 76, 87, 2), seriesAry.shape)
assert_equals((20, 2, 87, 76), seriesAry_xpose.shape)
def test_loadStacksAsSeries(self):
rangeary = arange(64*128, dtype=dtype('int16'))
rangeary.shape = (64, 128)
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
series = SeriesLoader(self.sc).fromStack(filepath, dims=(128, 64))
series_ary = series.pack()
assert_equals((128, 64), series.dims.count)
assert_equals((128, 64), series_ary.shape)
assert_true(array_equal(rangeary.T, series_ary))
def test_loadStacksAsSeries(self):
rangeary = arange(64 * 128, dtype=dtype('int16'))
rangeary.shape = (64, 128)
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
series = SeriesLoader(self.sc).fromStack(filepath, dims=(128, 64))
series_ary = series.pack()
assert_equals((128, 64), series.dims.count)
assert_equals((128, 64), series_ary.shape)
assert_true(array_equal(rangeary.T, series_ary))
def test_castToFloat(self):
from numpy import arange
shape = (3, 2, 2)
size = 3*2*2
ary = arange(size, dtype=dtypeFunc('uint8')).reshape(shape)
ary2 = ary + size
from thunder.rdds.fileio.seriesloader import SeriesLoader
series = SeriesLoader(self.sc).fromArrays([ary, ary2])
castSeries = series.astype("smallfloat")
assert_equals('float16', str(castSeries.dtype))
assert_equals('float16', str(castSeries.first()[1].dtype))
def test_maxProject(self):
from thunder.rdds.fileio.seriesloader import SeriesLoader
ary = arange(8, dtype=dtypeFunc('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArraysAsImages(ary)
project0Series = series.maxProject(axis=0)
project0 = project0Series.pack()
project1Series = series.maxProject(axis=1)
project1 = project1Series.pack(sorting=True)
assert_true(array_equal(amax(ary.T, 0), project0))
assert_true(array_equal(amax(ary.T, 1), project1))
def test_castToFloat(self):
from numpy import arange
shape = (3, 2, 2)
size = 3 * 2 * 2
ary = arange(size, dtype=dtypeFunc('uint8')).reshape(shape)
ary2 = ary + size
from thunder.rdds.fileio.seriesloader import SeriesLoader
series = SeriesLoader(self.sc).fromArraysAsImages([ary, ary2])
castSeries = series.astype("smallfloat")
assert_equals('float16', str(castSeries.dtype))
assert_equals('float16', str(castSeries.first()[1].dtype))
def _run_roundtrip_tst(self, nimages, aryShape, dtypeSpec, sizeSpec):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
blocks = series.toBlocks(sizeSpec)
roundtrippedSeries = blocks.toSeries(newDType=series.dtype)
packedSeries = series.pack()
packedRoundtrippedSeries = roundtrippedSeries.pack()
assert_true(array_equal(packedSeries, packedRoundtrippedSeries))
def test_maxProject(self):
from thunder.rdds.fileio.seriesloader import SeriesLoader
ary = arange(8, dtype=dtypeFunc('int16')).reshape((2, 4))
series = SeriesLoader(self.sc).fromArrays(ary)
project0Series = series.maxProject(axis=0)
project0 = project0Series.pack()
project1Series = series.maxProject(axis=1)
project1 = project1Series.pack(sorting=True)
assert_true(array_equal(amax(ary.T, 0), project0))
assert_true(array_equal(amax(ary.T, 1), project1))
def _run_roundtrip_tst(self, testCount, arrays, blockSize):
print "Running TestSeriesBinaryWriteFromStack roundtrip test #%d" % testCount
insubdir = os.path.join(self.outputdir, 'input%d' % testCount)
os.mkdir(insubdir)
outsubdir = os.path.join(self.outputdir, 'output%d' % testCount)
#os.mkdir(outsubdir)
for aryCount, array in enumerate(arrays):
# array.tofile always writes in column-major order...
array.tofile(os.path.join(insubdir, "img%02d.stack" % aryCount))
# ... but we will read and interpret these as though they are in row-major order
dims = list(arrays[0].shape)
dims.reverse()
underTest = SeriesLoader(self.sc)
underTest.saveFromStack(insubdir, outsubdir, dims, blockSize=blockSize, datatype=str(arrays[0].dtype))
series = underTest.fromStack(insubdir, dims, datatype=str(arrays[0].dtype))
roundtripped_series = underTest.fromBinary(outsubdir)
roundtripped = roundtripped_series.collect()
direct = series.collect()
expecteddtype = str(smallest_float_type(arrays[0].dtype))
assert_equals(expecteddtype, roundtripped_series.dtype)
assert_equals(expecteddtype, series.dtype)
assert_equals(expecteddtype, str(roundtripped[0][1].dtype))
assert_equals(expecteddtype, str(direct[0][1].dtype))
with open(os.path.join(outsubdir, "conf.json"), 'r') as fp:
# check that binary series file data type *matches* input stack data type (not yet converted to float)
# at least according to conf.json
conf = json.load(fp)
assert_equals(str(arrays[0].dtype), conf["valuetype"])
for ((serieskeys, seriesvalues), (directkeys, directvalues)) in zip(roundtripped, direct):
assert_equals(directkeys, serieskeys)
assert_equals(directvalues, seriesvalues)
for seriesidx, seriesval in enumerate(seriesvalues):
#print "seriesidx: %d; serieskeys: %s; seriesval: %g" % (seriesidx, serieskeys, seriesval)
# flip indices again for row vs col-major insanity
arykeys = list(serieskeys)
arykeys.reverse()
msg = "Failure on test #%d, time point %d, indices %s" % (testCount, seriesidx, str(tuple(arykeys)))
try:
assert_almost_equal(arrays[seriesidx][tuple(arykeys)], seriesval, places=4)
except AssertionError, e:
raise AssertionError(msg, e)
def _run_roundtrip_tst(self, nimages, aryShape, dtypeSpec, sizeSpec):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(
nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
blocks = series.toBlocks(sizeSpec)
roundtrippedSeries = blocks.toSeries(newDType=series.dtype)
packedSeries = series.pack()
packedRoundtrippedSeries = roundtrippedSeries.pack()
assert_true(array_equal(packedSeries, packedRoundtrippedSeries))
def _run_tst_roundtripConvertToSeries(self, images, strategy):
outdir = os.path.join(self.outputdir, "fish-series-dir")
partitionedimages = images.toBlocks(strategy)
series = partitionedimages.toSeries()
series_ary = series.pack()
partitionedimages.saveAsBinarySeries(outdir)
converted_series = SeriesLoader(self.sc).fromBinary(outdir)
converted_series_ary = converted_series.pack()
assert_equals(images.dims.count, series.dims.count)
expected_shape = tuple([images.nimages] + list(images.dims.count))
assert_equals(expected_shape, series_ary.shape)
assert_true(array_equal(series_ary, converted_series_ary))
def _run_tst_roundtripConvertToSeries(self, images, strategy):
outdir = os.path.join(self.outputdir, "fish-series-dir")
partitionedimages = images.toBlocks(strategy)
series = partitionedimages.toSeries()
series_ary = series.pack()
partitionedimages.saveAsBinarySeries(outdir)
converted_series = SeriesLoader(self.sc).fromBinary(outdir)
converted_series_ary = converted_series.pack()
assert_equals(images.dims.count, series.dims.count)
expected_shape = tuple([images.nrecords] + list(images.dims.count))
assert_equals(expected_shape, series_ary.shape)
assert_true(array_equal(series_ary, converted_series_ary))
def test_roundtripConvertToSeries(self):
imagepath = TestImagesUsingOutputDir._findSourceTreeDir("utils/data/fish/tif-stack")
outdir = os.path.join(self.outputdir, "fish-series-dir")
images = ImagesLoader(self.sc).fromMultipageTif(imagepath)
series = images.toSeries(blockSize=76*20)
series_ary = series.pack()
images.saveAsBinarySeries(outdir, blockSize=76*20)
converted_series = SeriesLoader(self.sc).fromBinary(outdir)
converted_series_ary = converted_series.pack()
assert_equals((76, 87, 2), series.dims.count)
assert_equals((20, 76, 87, 2), series_ary.shape)
assert_true(array_equal(series_ary, converted_series_ary))
def test_roundtripConvertToSeries(self):
imagepath = TestImagesUsingOutputDir._findSourceTreeDir(
"utils/data/fish/tif-stack")
outdir = os.path.join(self.outputdir, "fish-series-dir")
images = ImagesLoader(self.sc).fromMultipageTif(imagepath)
series = images.toSeries(blockSize=76 * 20)
series_ary = series.pack()
images.saveAsBinarySeries(outdir, blockSize=76 * 20)
converted_series = SeriesLoader(self.sc).fromBinary(outdir)
converted_series_ary = converted_series.pack()
assert_equals((76, 87, 2), series.dims.count)
assert_equals((20, 76, 87, 2), series_ary.shape)
assert_true(array_equal(series_ary, converted_series_ary))
def loadSeriesLocal(self,
dataFilePath,
inputFormat='npy',
minPartitions=None,
keyFilePath=None,
varName=None):
"""
Load a Series object from a local file (either npy or MAT format).
File should contain a 1d or 2d matrix, where each row
of the input matrix is a record.
Keys can be provided in a separate file (with variable name 'keys', for MAT files).
If not provided, linear indices will be used for keys.
Parameters
----------
dataFilePath: str
File to import
varName : str, optional, default = None
Variable name to load (for MAT files only)
keyFilePath : str, optional, default = None
File containing the keys for each record as another 1d or 2d array
minPartitions : Int, optional, default = 1
Number of partitions for RDD
"""
checkParams(inputFormat, ['mat', 'npy'])
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputFormat.lower() == 'mat':
if varName is None:
raise Exception(
'Must provide variable name for loading MAT files')
data = loader.fromMatLocal(dataFilePath, varName, keyFilePath)
else:
data = loader.fromNpyLocal(dataFilePath, keyFilePath)
return data
def _run_roundtrip_tst(self, testIdx, nimages, aryShape, dtypeSpec,
npartitions):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(
nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
saveDirPath = os.path.join(self.outputdir, 'save%d' % testIdx)
series.repartition(
npartitions
) # note: this does an elementwise shuffle! won't be in sorted order
series.saveAsBinarySeries(saveDirPath)
nnonemptyPartitions = 0
for partitionList in series.rdd.glom().collect():
if partitionList:
nnonemptyPartitions += 1
del partitionList
nsaveFiles = len(glob.glob(saveDirPath + os.sep + "*.bin"))
roundtrippedSeries = loader.fromBinary(saveDirPath)
with open(os.path.join(saveDirPath, "conf.json"), 'r') as fp:
conf = json.load(fp)
# sorting is required here b/c of the randomization induced by the repartition.
# orig and roundtripped will in general be different from each other, since roundtripped
# will have (0, 0, 0) index as first element (since it will be the lexicographically first
# file) while orig has only a 1 in npartitions chance of starting with (0, 0, 0) after repartition.
expectedPackedAry = series.pack(sorting=True)
actualPackedAry = roundtrippedSeries.pack(sorting=True)
assert_true(array_equal(expectedPackedAry, actualPackedAry))
assert_equals(nnonemptyPartitions, nsaveFiles)
assert_equals(len(aryShape), conf["nkeys"])
assert_equals(nimages, conf["nvalues"])
assert_equals("int16", conf["keytype"])
assert_equals(str(series.dtype), conf["valuetype"])
# check that we have converted ourselves to an appropriate float after reloading
assert_equals(str(smallestFloatType(series.dtype)),
str(roundtrippedSeries.dtype))
def test_fromMultipageTif(self):
testresourcesdir = TestSeriesLoader._findTestResourcesDir()
imagepath = os.path.join(testresourcesdir, "multilayer_tif", "dotdotdot_lzw.tif")
testimg_pil = Image.open(imagepath)
testimg_arys = list()
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(1)
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(2)
testimg_arys.append(pil_to_array(testimg_pil))
series = SeriesLoader(self.sc).fromMultipageTif(imagepath)
series_ary = series.pack()
assert_equals((70, 75, 3), series.dims.count)
assert_equals((70, 75, 3), series_ary.shape)
assert_true(array_equal(testimg_arys[0], series_ary[:, :, 0]))
assert_true(array_equal(testimg_arys[1], series_ary[:, :, 1]))
assert_true(array_equal(testimg_arys[2], series_ary[:, :, 2]))
def test_fromMultipageTif(self):
testresourcesdir = TestSeriesLoader._findTestResourcesDir()
imagepath = os.path.join(testresourcesdir, "multilayer_tif",
"dotdotdot_lzw.tif")
testimg_pil = Image.open(imagepath)
testimg_arys = list()
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(1)
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(2)
testimg_arys.append(pil_to_array(testimg_pil))
series = SeriesLoader(self.sc).fromMultipageTif(imagepath)
assert_equals('float16', series._dtype)
series_ary = series.pack()
assert_equals((70, 75, 3), series.dims.count)
assert_equals((70, 75, 3), series_ary.shape)
assert_true(array_equal(testimg_arys[0], series_ary[:, :, 0]))
assert_true(array_equal(testimg_arys[1], series_ary[:, :, 1]))
assert_true(array_equal(testimg_arys[2], series_ary[:, :, 2]))
def _run_roundtrip_tst(self, testIdx, nimages, aryShape, dtypeSpec, npartitions):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
saveDirPath = os.path.join(self.outputdir, 'save%d' % testIdx)
series.repartition(npartitions) # note: this does an elementwise shuffle! won't be in sorted order
series.saveAsBinarySeries(saveDirPath)
nnonemptyPartitions = 0
for partitionList in series.rdd.glom().collect():
if partitionList:
nnonemptyPartitions += 1
del partitionList
nsaveFiles = len(glob.glob(saveDirPath + os.sep + "*.bin"))
roundtrippedSeries = loader.fromBinary(saveDirPath)
with open(os.path.join(saveDirPath, "conf.json"), 'r') as fp:
conf = json.load(fp)
# sorting is required here b/c of the randomization induced by the repartition.
# orig and roundtripped will in general be different from each other, since roundtripped
# will have (0, 0, 0) index as first element (since it will be the lexicographically first
# file) while orig has only a 1 in npartitions chance of starting with (0, 0, 0) after repartition.
expectedPackedAry = series.pack(sorting=True)
actualPackedAry = roundtrippedSeries.pack(sorting=True)
assert_true(array_equal(expectedPackedAry, actualPackedAry))
assert_equals(nnonemptyPartitions, nsaveFiles)
assert_equals(len(aryShape), conf["nkeys"])
assert_equals(nimages, conf["nvalues"])
assert_equals("int16", conf["keytype"])
assert_equals(str(series.dtype), conf["valuetype"])
# check that we have converted ourselves to an appropriate float after reloading
assert_equals(str(smallestFloatType(series.dtype)), str(roundtrippedSeries.dtype))
def loadSeriesLocal(self, datafile, inputformat='npy', minPartitions=None, keyfile=None, varname=None):
"""
Load a Series object from a local file (either npy or MAT format).
File should contain a 1d or 2d matrix, where each row
of the input matrix is a record.
Keys can be provided in a separate file (with variable name 'keys', for MAT files).
If not provided, linear indices will be used for keys.
Parameters
----------
datafile : str
File to import
varname : str, optional, default = None
Variable name to load (for MAT files only)
keyfile : str, optional, default = None
File containing the keys for each record as another 1d or 2d array
minPartitions : Int, optional, default = 1
Number of partitions for RDD
"""
checkparams(inputformat, ['mat', 'npy'])
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputformat.lower() == 'mat':
if varname is None:
raise Exception('Must provide variable name for loading MAT files')
data = loader.fromMatLocal(datafile, varname, keyfile)
else:
data = loader.fromNpyLocal(datafile, keyfile)
return data
def loadImagesAsSeries(self,
dataPath,
dims=None,
inputFormat='stack',
ext=None,
dtype='int16',
blockSize="150M",
blockSizeUnits="pixels",
startIdx=None,
stopIdx=None,
shuffle=True,
recursive=False):
"""
Load Images data as Series data.
Parameters
----------
dataPath: string
Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
including scheme. A dataPath argument may include a single '*' wildcard character in the filename. Examples
of valid dataPaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
"/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".
dims: tuple of positive int, optional (but required if inputFormat is 'stack')
Dimensions of input image data, for instance (1024, 1024, 48). Binary stack data will be interpreted as
coming from a multidimensional array of the specified dimensions.
The first dimension of the passed dims tuple should be the one that is changing most rapidly
on disk. So for instance given dims of (x, y, z), the coordinates of the data in a binary stack file
should be ordered as [(x0, y0, z0), (x1, y0, z0), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
(xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)]. This is the opposite convention from that used by numpy,
which by default has the fastest-changing dimension listed last (column-major convention). Thus, if loading
a numpy array `ary`, where `ary.shape == (z, y, x)`, written to disk by `ary.tofile("myarray.stack")`, the
corresponding dims parameter should be (x, y, z).
If inputFormat is 'tif', the dims parameter (if any) will be ignored; data dimensions will instead
be read out from the tif file headers.
inputFormat: {'stack', 'tif'}. optional, default 'stack'
Expected format of the input data. 'stack' indicates flat files of raw binary data, while 'tif' indicates
greyscale / luminance TIF images. Each page of a multipage tif file will be interpreted as a separate
z-plane. For both stacks and tif stacks, separate files are interpreted as distinct time points, with
ordering given by lexicographic sorting of file names.
ext: string, optional, default None
Extension required on data files to be loaded. By default will be "stack" if inputFormat=="stack", "tif" for
inputFormat=='tif'.
dtype: string or numpy dtype. optional, default 'int16'
Data type of the image files to be loaded, specified as a numpy "dtype" string. If inputFormat is
'tif', the dtype parameter (if any) will be ignored; data type will instead be read out from the
tif headers.
blockSize: string formatted as e.g. "64M", "512k", "2G", or positive int. optional, default "150M"
Requested size of individual output files in bytes (or kilobytes, megabytes, gigabytes). If shuffle=True,
blockSize can also be a tuple of int specifying either the number of pixels or of splits per dimension to
apply to the loaded images, or an instance of BlockingStrategy. Whether a tuple of int is interpreted as
pixels or as splits depends on the value of the blockSizeUnits parameter. blockSize also indirectly
controls the number of Spark partitions to be used, with one partition used per block created.
blockSizeUnits: string, either "pixels" or "splits" (or unique prefix of each, such as "s"), default "pixels"
Specifies units to be used in interpreting a tuple passed as blockSizeSpec when shuffle=True. If a string
or a BlockingStrategy instance is passed as blockSizeSpec, or if shuffle=False, this parameter has no
effect.
startIdx: nonnegative int, optional
startIdx and stopIdx are convenience parameters to allow only a subset of input files to be read in. These
parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
after lexicographically sorting all input data files matching the dataPath argument. For example,
startIdx=None (the default) and stopIdx=10 will cause only the first 10 data files in dataPath to be read
in; startIdx=2 and stopIdx=3 will cause only the third file (zero-based index of 2) to be read in. startIdx
and stopIdx use the python slice indexing convention (zero-based indexing with an exclusive final position).
stopIdx: nonnegative int, optional
See startIdx.
shuffle: boolean, optional, default True
Controls whether the conversion from Images to Series formats will make use of a Spark shuffle-based method.
recursive: boolean, default False
If true, will recursively descend directories rooted at dataPath, loading all files in the tree that
have an appropriate extension. Recursive loading is currently only implemented for local filesystems
(not s3), and only with shuffle=True.
Returns
-------
data: thunder.rdds.Series
A newly-created Series object, wrapping an RDD of timeseries data generated from the images in dataPath.
This RDD will have as keys an n-tuple of int, with n given by the dimensionality of the original images. The
keys will be the zero-based spatial index of the timeseries data in the RDD value. The value will be
a numpy array of length equal to the number of image files loaded. Each loaded image file will contribute
one point to this value array, with ordering as implied by the lexicographic ordering of image file names.
"""
checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])
if inputFormat.lower() == 'stack' and not dims:
raise ValueError(
"Dimensions ('dims' parameter) must be specified if loading from binary image stack"
+ " ('stack' value for 'inputFormat' parameter)")
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if shuffle:
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if inputFormat.lower() == 'stack':
images = loader.fromStack(dataPath,
dims,
dtype=dtype,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive)
else:
# tif / tif stack
images = loader.fromTif(dataPath,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive)
return images.toBlocks(blockSize, units=blockSizeUnits).toSeries()
else:
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc)
if inputFormat.lower() == 'stack':
return loader.fromStack(dataPath,
dims,
ext=ext,
dtype=dtype,
blockSize=blockSize,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive)
else:
# tif / tif stack
return loader.fromTif(dataPath,
ext=ext,
blockSize=blockSize,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive)
def convertImagesToSeries(self,
datapath,
outputdirpath,
dims=None,
inputformat='stack',
dtype='int16',
blocksize="150M",
startidx=None,
stopidx=None,
shuffle=False,
overwrite=False):
"""
Write out Images data as Series data, saved in a flat binary format.
The resulting Series data files may subsequently be read in using the loadSeries() method. The Series data
object that results will be equivalent to that which would be generated by loadImagesAsSeries(). It is expected
that loading Series data directly from the series flat binary format, using loadSeries(), will be faster than
converting image data to a Series object through loadImagesAsSeries().
Parameters
----------
datapath: string
Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
including scheme. A datapath argument may include a single '*' wildcard character in the filename. Examples
of valid datapaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
"/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".
outputdirpath: string
Path to a directory into which to write Series file output. An outputdir argument may be either a path
on the local file system or a URI-like format, as in datapath. Examples of valid outputdirpaths include
"a/relative/directory/", "s3n:///my-s3-bucket/data/myoutput/", or "file:///mnt/a/new/directory/". If the
directory specified by outputdirpath already exists and the 'overwrite' parameter is False, this method
will throw a ValueError. If the directory exists and 'overwrite' is True, the existing directory and all
its contents will be deleted and overwritten.
dims: tuple of positive int, optional (but required if inputformat is 'stack')
Dimensions of input image data, for instance (1024, 1024, 48). Binary stack data will be interpreted as
coming from a multidimensional array of the specified dimensions.
The first dimension of the passed dims tuple should be the one that is changing most rapidly
on disk. So for instance given dims of (x, y, z), the coordinates of the data in a binary stack file
should be ordered as [(x0, y0, z0), (x1, y0, z0), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
(xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)]. This is the opposite convention from that used by numpy,
which by default has the fastest-changing dimension listed last (column-major convention). Thus, if loading
a numpy array `ary`, where `ary.shape == (z, y, x)`, written to disk by `ary.tofile("myarray.stack")`, the
corresponding dims parameter should be (x, y, z).
If inputformat is 'tif-stack', the dims parameter (if any) will be ignored; data dimensions will instead
be read out from the tif file headers.
inputformat: {'stack', 'tif-stack'}. optional, default 'stack'
Expected format of the input data. 'stack' indicates flat files of raw binary data, while 'tif-stack'
indicates a sequence of multipage tif files, with each page of the tif corresponding to a separate z-plane.
For both stacks and tif stacks, separate files are interpreted as distinct time points, with ordering
given by lexicographic sorting of file names.
This method assumes that stack data consists of signed 16-bit integers in native byte order. The lower-level
API method SeriesLoader.saveFromStack() allows alternative data types to be read in.
dtype: string or numpy dtype. optional, default 'int16'
Data type of the image files to be loaded, specified as a numpy "dtype" string. If inputformat is
'tif-stack', the dtype parameter (if any) will be ignored; data type will instead be read out from the
tif headers.
blocksize: string formatted as e.g. "64M", "512k", "2G", or positive int. optional, default "150M"
Requested size of individual output files in bytes (or kilobytes, megabytes, gigabytes). This parameter
also indirectly controls the number of Spark partitions to be used, with one partition used per block
created.
startidx: nonnegative int, optional
startidx and stopidx are convenience parameters to allow only a subset of input files to be read in. These
parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
after lexicographically sorting all input data files matching the datapath argument. For example,
startidx=None (the default) and stopidx=10 will cause only the first 10 data files in datapath to be read
in; startidx=2 and stopidx=3 will cause only the third file (zero-based index of 2) to be read in. startidx
and stopidx use the python slice indexing convention (zero-based indexing with an exclusive final position).
stopidx: nonnegative int, optional
See startidx.
shuffle: boolean, optional, default False
Controls whether the conversion from Images to Series formats will make use of a Spark shuffle-based method.
The default at present is not to use a shuffle. The shuffle-based method may lead to higher performance in
some cases, but the default method appears to be more stable with larger data set sizes. This default may
change in future releases.
overwrite: boolean, optional, default False
If true, the directory specified by outputdirpath will first be deleted, along with all its contents, if it
already exists. (Use with caution.) If false, a ValueError will be thrown if outputdirpath is found to
already exist.
"""
checkparams(inputformat, ['stack', 'tif-stack'])
if inputformat.lower() == 'stack' and not dims:
raise ValueError(
"Dimensions ('dims' parameter) must be specified if loading from binary image stack"
+ " ('stack' value for 'inputformat' parameter)")
if shuffle:
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if inputformat.lower() == 'stack':
loader.fromStack(datapath, dims, dtype=dtype, startidx=startidx, stopidx=stopidx)\
.saveAsBinarySeries(outputdirpath, blockSize=blocksize, overwrite=overwrite)
else:
loader.fromMultipageTif(datapath, startidx=startidx, stopidx=stopidx)\
.saveAsBinarySeries(outputdirpath, blockSize=blocksize, overwrite=overwrite)
else:
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc)
if inputformat.lower() == 'stack':
loader.saveFromStack(datapath,
outputdirpath,
dims,
datatype=dtype,
blockSize=blocksize,
overwrite=overwrite,
startidx=startidx,
stopidx=stopidx)
else:
loader.saveFromMultipageTif(datapath,
outputdirpath,
blockSize=blocksize,
startidx=startidx,
stopidx=stopidx,
overwrite=overwrite)
def loadSeries(self, dataPath, nkeys=None, nvalues=None, inputFormat='binary', minPartitions=None,
confFilename='conf.json', keyType=None, valueType=None, keyPath=None, varName=None):
"""
Loads a Series object from data stored as binary, text, npy, or mat.
For binary and text, supports single files or multiple files stored on a local file system,
a networked file system (mounted and available on all cluster nodes), Amazon S3, or HDFS.
For local formats (npy and mat) only local file systems currently supported.
Parameters
----------
dataPath: string
Path to data files or directory, as either a local filesystem path or a URI.
May include a single '*' wildcard in the filename. Examples of valid dataPaths include
'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".
nkeys: int, optional (required if `inputFormat` is 'text'), default = None
Number of keys per record (e.g. 3 for (x, y, z) coordinate keys). Must be specified for
text data; can be specified here or in a configuration file for binary data.
nvalues: int, optional (required if `inputFormat` is 'text')
Number of values per record. Must be specified here or in a configuration file for binary data.
inputFormat: {'text', 'binary', 'npy', 'mat'}. optional, default = 'binary'
inputFormat of data to be read.
minPartitions: int, optional, default = SparkContext.minParallelism
Minimum number of Spark partitions to use, only for text.
confFilename: string, optional, default 'conf.json'
Path to JSON file with configuration options including 'nkeys', 'nvalues',
'keyType', and 'valueType'. If a file is not found at the given path, then the base
directory in 'dataPath' will be checked. Parameters will override the conf file.
keyType: string or numpy dtype, optional, default = None
Numerical type of keys, will override conf file.
valueType: string or numpy dtype, optional, default = None
Numerical type of values, will override conf file.
keyPath: string, optional, default = None
Path to file with keys when loading from npy or mat.
varName : str, optional, default = None
Variable name to load (for MAT files only)
Returns
-------
data: thunder.rdds.Series
A Series object, wrapping an RDD, with (n-tuples of ints) : (numpy array) pairs
"""
checkParams(inputFormat, ['text', 'binary', 'npy', 'mat'])
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputFormat.lower() == 'binary':
data = loader.fromBinary(dataPath, confFilename=confFilename, nkeys=nkeys, nvalues=nvalues,
keyType=keyType, valueType=valueType)
elif inputFormat.lower() == 'text':
if nkeys is None:
raise Exception('Must provide number of keys per record for loading from text')
data = loader.fromText(dataPath, nkeys=nkeys)
elif inputFormat.lower() == 'npy':
data = loader.fromNpyLocal(dataPath, keyPath)
else:
if varName is None:
raise Exception('Must provide variable name for loading MAT files')
data = loader.fromMatLocal(dataPath, varName, keyPath)
return data
def convertImagesToSeries(self, dataPath, outputDirPath, dims=None, inputFormat='stack', ext=None,
dtype='int16', blockSize="150M", blockSizeUnits="pixels", startIdx=None, stopIdx=None,
shuffle=True, overwrite=False, recursive=False, nplanes=None, npartitions=None,
renumber=False, confFilename='conf.json'):
"""
Write out Images data as Series data, saved in a flat binary format.
The resulting files may subsequently be read in using ThunderContext.loadSeries().
Loading Series data directly will likely be faster than converting image data
to a Series object through loadImagesAsSeries().
Parameters
----------
dataPath: string
Path to data files or directory, as either a local filesystem path or a URI.
May include a single '*' wildcard in the filename. Examples of valid dataPaths include
'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".
outputDirPath: string
Path to directory to write Series file output. May be either a path on the local file system
or a URI-like format, such as "local/directory", "s3n:///my-s3-bucket/data/",
or "file:///mnt/another/directory/". If the directory exists and 'overwrite' is True,
the existing directory and all its contents will be deleted and overwritten.
dims: tuple of positive int, optional (required if inputFormat is 'stack')
Image dimensions. Binary stack data will be interpreted as a multidimensional array
with the given dimensions, and should be stored in row-major order (Fortran or Matlab convention),
where the first dimension changes most rapidly. For 'png' or 'tif' data dimensions
will be read from the image file headers.
inputFormat: str, optional, default = 'stack'
Expected format of the input data: 'stack', 'png', or 'tif'. 'stack' indicates flat binary stacks.
'png' or 'tif' indicate image formats. Page of a multipage tif file will be extend along
the third dimension. Separate files interpreted as distinct records, with ordering
given by lexicographic sorting of file names.
ext: string, optional, default = None
File extension, default will be "bin" if inputFormat=="stack", "tif" for inputFormat=='tif',
and 'png' for inputFormat=="png".
dtype: string or numpy dtype. optional, default 'int16'
Data type of the image files to be loaded, specified as a numpy "dtype" string.
Ignored for 'tif' or 'png' (data will be inferred from image formats).
blockSize: string or positive int, optional, default "150M"
Requested size of blocks (e.g "64M", "512k", "2G"). If shuffle=True, can also be a
tuple of int specifying the number of pixels or splits per dimension. Indirectly
controls the number of Spark partitions, with one partition per block.
blockSizeUnits: string, either "pixels" or "splits", default "pixels"
Units for interpreting a tuple passed as blockSize when shuffle=True.
startIdx: nonnegative int, optional, default = None
Convenience parameters to read only a subset of input files. Uses python slice conventions
(zero-based indexing with exclusive final position). These parameters give the starting
and final index after lexicographic sorting.
stopIdx: nonnegative int, optional, default = None
See startIdx.
shuffle: boolean, optional, default = True
Controls whether the conversion from Images to Series formats will use of a Spark shuffle-based method.
overwrite: boolean, optional, default False
If true, the directory specified by outputDirPath will be deleted (recursively) if it
already exists. (Use with caution.)
recursive: boolean, optional, default = False
If true, will recursively descend directories rooted at dataPath, loading all files
in the tree with an appropriate extension.
nplanes: positive integer, optional, default = None
Subdivide individual image files. Every `nplanes` from each file will be considered a new record.
With nplanes=None (the default), a single file will be considered as representing a single record.
If the number of records per file is not the same across all files, then `renumber` should be set
to True to ensure consistent keys.
npartitions: positive int, optional, default = None
Specify number of partitions for the RDD, if unspecified will use 1 partition per image.
renumber: boolean, optional, default = False
Recalculate keys for records after images are loading. Only necessary if different files contain
different number of records (e.g. due to specifying nplanes). See Images.renumber().
confFilename : string, optional, default = 'conf.json'
Name of conf file if using to specify parameters for binary stack data
"""
checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])
if not overwrite:
raiseErrorIfPathExists(outputDirPath, awsCredentialsOverride=self._credentials)
overwrite = True # prevent additional downstream checks for this path
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if shuffle:
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if inputFormat.lower() == 'stack':
images = loader.fromStack(dataPath, dims, ext=ext, dtype=dtype, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions,
confFilename=confFilename)
else:
# 'tif' or 'tif-stack'
images = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions)
if renumber:
images = images.renumber()
images.toBlocks(blockSize, units=blockSizeUnits).saveAsBinarySeries(outputDirPath, overwrite=overwrite)
else:
from thunder.rdds.fileio.seriesloader import SeriesLoader
if nplanes is not None:
raise NotImplementedError("nplanes is not supported with shuffle=False")
if npartitions is not None:
raise NotImplementedError("npartitions is not supported with shuffle=False")
loader = SeriesLoader(self._sc)
if inputFormat.lower() == 'stack':
loader.saveFromStack(dataPath, outputDirPath, dims, ext=ext, dtype=dtype,
blockSize=blockSize, overwrite=overwrite, startIdx=startIdx,
stopIdx=stopIdx, recursive=recursive)
else:
# 'tif' or 'tif-stack'
loader.saveFromTif(dataPath, outputDirPath, ext=ext, blockSize=blockSize,
startIdx=startIdx, stopIdx=stopIdx, overwrite=overwrite,
recursive=recursive)
def loadSeries(self,
dataPath,
nkeys=None,
nvalues=None,
inputFormat='binary',
minPartitions=None,
confFilename='conf.json',
keyType=None,
valueType=None):
"""
Loads a Series object from data stored as text or binary files.
Supports single files or multiple files stored on a local file system, a networked file system (mounted
and available on all cluster nodes), Amazon S3, or HDFS.
Parameters
----------
dataPath: string
Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
including scheme. A dataPath argument may include a single '*' wildcard character in the filename. Examples
of valid dataPaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
"/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".
nkeys: int, optional (but required if `inputFormat` is 'text')
dimensionality of data keys. (For instance, (x,y,z) keyed data for 3-dimensional image timeseries data.)
For text data, number of keys must be specified in this parameter; for binary data, number of keys must be
specified either in this parameter or in a configuration file named by the 'conffile' argument if this
parameter is not set.
nvalues: int, optional (but required if `inputFormat` is 'text')
Number of values expected to be read. For binary data, nvalues must be specified either in this parameter
or in a configuration file named by the 'conffile' argument if this parameter is not set.
inputFormat: {'text', 'binary'}. optional, default 'binary'
Format of data to be read.
minPartitions: int, optional
Explicitly specify minimum number of Spark partitions to be generated from this data. Used only for
text data. Default is to use minParallelism attribute of Spark context object.
confFilename: string, optional, default 'conf.json'
Path to JSON file with configuration options including 'nkeys', 'nvalues', 'keytype', and 'valuetype'.
If a file is not found at the given path, then the base directory given in 'datafile'
will also be checked. Parameters `nkeys` or `nvalues` that are specified as explicit arguments to this
method will take priority over those found in conffile if both are present.
Returns
-------
data: thunder.rdds.Series
A newly-created Series object, wrapping an RDD of series data. This RDD will have as keys an n-tuple
of int, with n given by `nkeys` or the configuration passed in `conffile`. RDD values will be a numpy
array of length `nvalues` (or as specified in the passed configuration file).
"""
checkParams(inputFormat, ['text', 'binary'])
from thunder.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputFormat.lower() == 'text':
data = loader.fromText(dataPath, nkeys=nkeys)
else:
# must be either 'text' or 'binary'
data = loader.fromBinary(dataPath,
confFilename=confFilename,
nkeys=nkeys,
nvalues=nvalues,
keyType=keyType,
valueType=valueType)
return data
def loadImagesAsSeries(self, dataPath, dims=None, inputFormat='stack', ext=None, dtype='int16',
blockSize="150M", blockSizeUnits="pixels", startIdx=None, stopIdx=None,
shuffle=True, recursive=False, nplanes=None, npartitions=None,
renumber=False, confFilename='conf.json'):
"""
Load Images data as Series data.
Parameters
----------
dataPath: string
Path to data files or directory, as either a local filesystem path or a URI.
May include a single '*' wildcard in the filename. Examples of valid dataPaths include
'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".
dims: tuple of positive int, optional (required if inputFormat is 'stack')
Image dimensions. Binary stack data will be interpreted as a multidimensional array
with the given dimensions, and should be stored in row-major order (Fortran or Matlab convention),
where the first dimension changes most rapidly. For 'png' or 'tif' data dimensions
will be read from the image file headers.
inputFormat: str, optional, default = 'stack'
Expected format of the input data: 'stack', 'png', or 'tif'. 'stack' indicates flat binary stacks.
'png' or 'tif' indicate image formats. Page of a multipage tif file will be extend along
the third dimension. Separate files interpreted as distinct records, with ordering
given by lexicographic sorting of file names.
ext: string, optional, default = None
File extension, default will be "bin" if inputFormat=="stack", "tif" for inputFormat=='tif',
and 'png' for inputFormat=="png".
dtype: string or numpy dtype. optional, default 'int16'
Data type of the image files to be loaded, specified as a numpy "dtype" string.
Ignored for 'tif' or 'png' (data will be inferred from image formats).
blockSize: string or positive int, optional, default "150M"
Requested size of blocks (e.g "64M", "512k", "2G"). If shuffle=True, can also be a
tuple of int specifying the number of pixels or splits per dimension. Indirectly
controls the number of Spark partitions, with one partition per block.
blockSizeUnits: string, either "pixels" or "splits", default "pixels"
Units for interpreting a tuple passed as blockSize when shuffle=True.
startIdx: nonnegative int, optional, default = None
Convenience parameters to read only a subset of input files. Uses python slice conventions
(zero-based indexing with exclusive final position). These parameters give the starting
and final index after lexicographic sorting.
stopIdx: nonnegative int, optional, default = None
See startIdx.
shuffle: boolean, optional, default = True
Controls whether the conversion from Images to Series formats will use of a Spark shuffle-based method.
recursive: boolean, optional, default = False
If true, will recursively descend directories rooted at dataPath, loading all files
in the tree with an appropriate extension.
nplanes: positive integer, optional, default = None
Subdivide individual image files. Every `nplanes` from each file will be considered a new record.
With nplanes=None (the default), a single file will be considered as representing a single record.
If the number of records per file is not the same across all files, then `renumber` should be set
to True to ensure consistent keys.
npartitions: positive int, optional, default = None
Specify number of partitions for the RDD, if unspecified will use 1 partition per image.
renumber: boolean, optional, default = False
Recalculate keys for records after images are loading. Only necessary if different files contain
different number of records (e.g. due to specifying nplanes). See Images.renumber().
confFilename : string, optional, default = 'conf.json'
Name of conf file if using to specify parameters for binary stack data
Returns
-------
data: thunder.rdds.Series
A Series object, wrapping an RDD, with (n-tuples of ints) : (numpy array) pairs.
Keys will be n-tuples of int, with n given by dimensionality of the images, and correspond
to indexes into the image arrays. Value will have length equal to the number of image files.
With each image contributing one point to this value array, with ordering given by
the lexicographic ordering of image file names.
"""
checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if shuffle:
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if inputFormat.lower() == 'stack':
images = loader.fromStack(dataPath, dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions,
confFilename=confFilename)
else:
# tif / tif stack
images = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions)
if renumber:
images = images.renumber()
return images.toBlocks(blockSize, units=blockSizeUnits).toSeries()
else:
from thunder.rdds.fileio.seriesloader import SeriesLoader
if nplanes is not None:
raise NotImplementedError("nplanes is not supported with shuffle=False")
if npartitions is not None:
raise NotImplementedError("npartitions is not supported with shuffle=False")
if renumber:
raise NotImplementedError("renumber is not supported with shuffle=False")
loader = SeriesLoader(self._sc)
if inputFormat.lower() == 'stack':
return loader.fromStack(dataPath, dims, ext=ext, dtype=dtype, blockSize=blockSize,
startIdx=startIdx, stopIdx=stopIdx, recursive=recursive)
else:
# tif / tif stack
return loader.fromTif(dataPath, ext=ext, blockSize=blockSize,
startIdx=startIdx, stopIdx=stopIdx, recursive=recursive)
def convertImagesToSeries(self,
dataPath,
outputDirPath,
dims=None,
inputFormat='stack',
ext=None,
dtype='int16',
blockSize="150M",
blockSizeUnits="pixels",
startIdx=None,
stopIdx=None,
shuffle=True,
overwrite=False,
recursive=False,
nplanes=None,
npartitions=None,
renumber=False):
"""
Write out Images data as Series data, saved in a flat binary format.
The resulting Series data files may subsequently be read in using the loadSeries() method. The Series data
object that results will be equivalent to that which would be generated by loadImagesAsSeries(). It is expected
that loading Series data directly from the series flat binary format, using loadSeries(), will be faster than
converting image data to a Series object through loadImagesAsSeries().
Parameters
----------
dataPath: string
Path to data files or directory, specified as either a local filesystem path or in a URI-like format,
including scheme. A dataPath argument may include a single '*' wildcard character in the filename. Examples
of valid dataPaths include 'a/local/relative/directory/*.stack", "s3n:///my-s3-bucket/data/mydatafile.tif",
"/mnt/my/absolute/data/directory/", or "file:///mnt/another/data/directory/".
outputDirPath: string
Path to a directory into which to write Series file output. An outputdir argument may be either a path
on the local file system or a URI-like format, as in dataPath. Examples of valid outputDirPaths include
"a/relative/directory/", "s3n:///my-s3-bucket/data/myoutput/", or "file:///mnt/a/new/directory/". If the
directory specified by outputDirPath already exists and the 'overwrite' parameter is False, this method
will throw a ValueError. If the directory exists and 'overwrite' is True, the existing directory and all
its contents will be deleted and overwritten.
dims: tuple of positive int, optional (but required if inputFormat is 'stack')
Dimensions of input image data, for instance (1024, 1024, 48). Binary stack data will be interpreted as
coming from a multidimensional array of the specified dimensions.
The first dimension of the passed dims tuple should be the one that is changing most rapidly
on disk. So for instance given dims of (x, y, z), the coordinates of the data in a binary stack file
should be ordered as [(x0, y0, z0), (x1, y0, z0), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
(xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)]. This is the opposite convention from that used by numpy,
which by default has the fastest-changing dimension listed last (column-major convention). Thus, if loading
a numpy array `ary`, where `ary.shape == (z, y, x)`, written to disk by `ary.tofile("myarray.stack")`, the
corresponding dims parameter should be (x, y, z).
If inputFormat is 'tif', the dims parameter (if any) will be ignored; data dimensions will instead
be read out from the tif file headers.
inputFormat: {'stack', 'tif'}. optional, default 'stack'
Expected format of the input data. 'stack' indicates flat files of raw binary data, while 'tif' indicates
greyscale / luminance TIF images. Each page of a multipage tif file will be interpreted as a separate
z-plane. For both stacks and tif stacks, separate files are interpreted as distinct time points, with
ordering given by lexicographic sorting of file names.
ext: string, optional, default None
Extension required on data files to be loaded. By default will be "stack" if inputFormat=="stack", "tif" for
inputFormat=='tif'.
dtype: string or numpy dtype. optional, default 'int16'
Data type of the image files to be loaded, specified as a numpy "dtype" string. If inputFormat is
'tif', the dtype parameter (if any) will be ignored; data type will instead be read out from the
tif headers.
blockSize: string formatted as e.g. "64M", "512k", "2G", or positive int, tuple of positive int, or instance of
BlockingStrategy. optional, default "150M"
Requested size of individual output files in bytes (or kilobytes, megabytes, gigabytes). blockSize can also
be an instance of blockingStrategy, or a tuple of int specifying either the number of pixels or of splits
per dimension to apply to the loaded images. Whether a tuple of int is interpreted as pixels or as splits
depends on the value of the blockSizeUnits parameter. This parameter also indirectly controls the number
of Spark partitions to be used, with one partition used per block created.
blockSizeUnits: string, either "pixels" or "splits" (or unique prefix of each, such as "s"), default "pixels"
Specifies units to be used in interpreting a tuple passed as blockSizeSpec when shuffle=True. If a string
or a BlockingStrategy instance is passed as blockSizeSpec, or if shuffle=False, this parameter has no
effect.
startIdx: nonnegative int, optional
startIdx and stopIdx are convenience parameters to allow only a subset of input files to be read in. These
parameters give the starting index (inclusive) and final index (exclusive) of the data files to be used
after lexicographically sorting all input data files matching the dataPath argument. For example,
startIdx=None (the default) and stopIdx=10 will cause only the first 10 data files in dataPath to be read
in; startIdx=2 and stopIdx=3 will cause only the third file (zero-based index of 2) to be read in. startIdx
and stopIdx use the python slice indexing convention (zero-based indexing with an exclusive final position).
stopIdx: nonnegative int, optional
See startIdx.
shuffle: boolean, optional, default True
Controls whether the conversion from Images to Series formats will make use of a Spark shuffle-based method.
overwrite: boolean, optional, default False
If true, the directory specified by outputDirPath will first be deleted, along with all its contents, if it
already exists. (Use with caution.) If false, a ValueError will be thrown if outputDirPath is found to
already exist.
recursive: boolean, default False
If true, will recursively descend directories rooted at dataPath, loading all files in the tree that
have an appropriate extension. Recursive loading is currently only implemented for local filesystems
(not s3), and only with shuffle=True.
nplanes: positive integer, default None
If passed, will cause a single image file to be subdivided into multiple records. Every
`nplanes` z-planes (or multipage tif pages) in the file will be taken as a new record, with the
first nplane planes of the first file being record 0, the second nplane planes being record 1, etc,
until the first file is exhausted and record ordering continues with the first nplane planes of the
second file, and so on. With nplanes=None (the default), a single file will be considered as
representing a single record. Keys are calculated assuming that all input files contain the same
number of records; if the number of records per file is not the same across all files,
then `renumber` should be set to True to ensure consistent keys. nplanes is only supported for
shuffle=True (the default).
npartitions: positive int, optional
If specified, request a certain number of partitions for the underlying Spark RDD. Default is 1
partition per image file. Only applies when shuffle=True.
renumber: boolean, optional, default False
If renumber evaluates to True, then the keys for each record will be explicitly recalculated after
all images are loaded. This should only be necessary at load time when different files contain
different number of records. renumber is only supported for shuffle=True (the default). See
Images.renumber().
"""
checkParams(inputFormat, ['stack', 'tif', 'tif-stack'])
if inputFormat.lower() == 'stack' and not dims:
raise ValueError(
"Dimensions ('dims' parameter) must be specified if loading from binary image stack"
+ " ('stack' value for 'inputFormat' parameter)")
if not overwrite:
raiseErrorIfPathExists(outputDirPath,
awsCredentialsOverride=self._credentials)
overwrite = True # prevent additional downstream checks for this path
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if shuffle:
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if inputFormat.lower() == 'stack':
images = loader.fromStack(dataPath,
dims,
ext=ext,
dtype=dtype,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive,
nplanes=nplanes,
npartitions=npartitions)
else:
# 'tif' or 'tif-stack'
images = loader.fromTif(dataPath,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive,
nplanes=nplanes,
npartitions=npartitions)
if renumber:
images = images.renumber()
images.toBlocks(blockSize,
units=blockSizeUnits).saveAsBinarySeries(
outputDirPath, overwrite=overwrite)
else:
from thunder.rdds.fileio.seriesloader import SeriesLoader
if nplanes is not None:
raise NotImplementedError(
"nplanes is not supported with shuffle=False")
if npartitions is not None:
raise NotImplementedError(
"npartitions is not supported with shuffle=False")
loader = SeriesLoader(self._sc)
if inputFormat.lower() == 'stack':
loader.saveFromStack(dataPath,
outputDirPath,
dims,
ext=ext,
dtype=dtype,
blockSize=blockSize,
overwrite=overwrite,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive)
else:
# 'tif' or 'tif-stack'
loader.saveFromTif(dataPath,
outputDirPath,
ext=ext,
blockSize=blockSize,
startIdx=startIdx,
stopIdx=stopIdx,
overwrite=overwrite,
recursive=recursive)
def _run_roundtrip_exception_tst(self, nimages, aryShape, dtypeSpec, sizeSpec):
testArrays = TestSeriesBinaryWriteFromStack.generateTestImages(nimages, aryShape, dtypeSpec)
loader = SeriesLoader(self.sc)
series = loader.fromArrays(testArrays)
assert_raises(ValueError, series.toBlocks, sizeSpec)
def _run_roundtrip_tst(self, testCount, arrays, blockSize):
print "Running TestSeriesBinaryWriteFromStack roundtrip test #%d" % testCount
insubdir = os.path.join(self.outputdir, 'input%d' % testCount)
os.mkdir(insubdir)
outsubdir = os.path.join(self.outputdir, 'output%d' % testCount)
#os.mkdir(outsubdir)
for aryCount, array in enumerate(arrays):
# array.tofile always writes in column-major order...
array.tofile(os.path.join(insubdir, "img%02d.stack" % aryCount))
# ... but we will read and interpret these as though they are in row-major order
dims = list(arrays[0].shape)
dims.reverse()
underTest = SeriesLoader(self.sc)
underTest.saveFromStack(insubdir,
outsubdir,
dims,
blockSize=blockSize,
datatype=str(arrays[0].dtype))
series = underTest.fromStack(insubdir,
dims,
datatype=str(arrays[0].dtype))
roundtripped_series = underTest.fromBinary(outsubdir)
roundtripped = roundtripped_series.collect()
direct = series.collect()
expecteddtype = str(smallest_float_type(arrays[0].dtype))
assert_equals(expecteddtype, roundtripped_series.dtype)
assert_equals(expecteddtype, series.dtype)
assert_equals(expecteddtype, str(roundtripped[0][1].dtype))
assert_equals(expecteddtype, str(direct[0][1].dtype))
with open(os.path.join(outsubdir, "conf.json"), 'r') as fp:
# check that binary series file data type *matches* input stack data type (not yet converted to float)
# at least according to conf.json
conf = json.load(fp)
assert_equals(str(arrays[0].dtype), conf["valuetype"])
for ((serieskeys, seriesvalues),
(directkeys, directvalues)) in zip(roundtripped, direct):
assert_equals(directkeys, serieskeys)
assert_equals(directvalues, seriesvalues)
for seriesidx, seriesval in enumerate(seriesvalues):
#print "seriesidx: %d; serieskeys: %s; seriesval: %g" % (seriesidx, serieskeys, seriesval)
# flip indices again for row vs col-major insanity
arykeys = list(serieskeys)
arykeys.reverse()
msg = "Failure on test #%d, time point %d, indices %s" % (
testCount, seriesidx, str(tuple(arykeys)))
try:
assert_almost_equal(arrays[seriesidx][tuple(arykeys)],
seriesval,
places=4)
except AssertionError, e:
raise AssertionError(msg, e)
def generateTestSeries(self):
from thunder.rdds.fileio.seriesloader import SeriesLoader
ary1 = arange(8, dtype=dtypeFunc('uint8')).reshape((2, 4))
ary2 = arange(8, 16, dtype=dtypeFunc('uint8')).reshape((2, 4))
return SeriesLoader(self.sc).fromArraysAsImages([ary1, ary2])
def _run_tst_fromBinary(self, useConfJson=False):
# run this as a single big test so as to avoid repeated setUp and tearDown of the spark context
# data will be a sequence of test data
# all keys and all values in a test data item must be of the same length
# keys get converted to ints regardless of raw input format
DATA = [
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]],
'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3], [5, 6, 7]],
[[11], [12]], 'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]],
'int16', 'int32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]],
'int32', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]],
'int16', 'float32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]],
'float32', 'float32'),
SeriesBinaryTestData.fromArrays([[2, 3, 4]], [[11.0, 12.0, 13.0]],
'float32', 'float32'),
]
for itemidx, item in enumerate(DATA):
outSubdir = os.path.join(self.outputdir, 'input%d' % itemidx)
os.mkdir(outSubdir)
fname = os.path.join(outSubdir, 'inputfile%d.bin' % itemidx)
with open(fname, 'wb') as f:
item.writeToFile(f)
loader = SeriesLoader(self.sc)
if not useConfJson:
series = loader.fromBinary(outSubdir,
nkeys=item.nkeys,
nvalues=item.nvals,
keyType=str(item.keyDtype),
valueType=str(item.valDtype))
else:
# write configuration file
conf = {
'input': outSubdir,
'nkeys': item.nkeys,
'nvalues': item.nvals,
'valuetype': str(item.valDtype),
'keytype': str(item.keyDtype)
}
with open(os.path.join(outSubdir, "conf.json"), 'wb') as f:
json.dump(conf, f, indent=2)
series = loader.fromBinary(outSubdir)
seriesData = series.rdd.collect()
expectedData = item.data
assert_equals(
len(expectedData), len(seriesData),
"Differing numbers of k/v pairs in item %d; expected %d, got %d"
% (itemidx, len(expectedData), len(seriesData)))
for expected, actual in zip(expectedData, seriesData):
expectedKeys = tuple(expected[0])
expectedType = smallestFloatType(item.valDtype)
expectedVals = array(expected[1], dtype=expectedType)
assert_equals(
expectedKeys, actual[0],
"Key mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedKeys), str(actual[0])))
assert_true(
allclose(expectedVals, actual[1]),
"Value mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedVals), str(actual[1])))
assert_equals(
expectedType, str(actual[1].dtype),
"Value type mismatch in item %d; expected %s, got %s" %
(itemidx, expectedType, str(actual[1].dtype)))
