def saveAsBinarySeries(self, outputDirPath, overwrite=False):
"""Writes out Series-formatted data.
Subclasses are *not* expected to override this method.
Parameters
----------
outputdirname : string path or URI to directory to be created
Output files will be written underneath outputdirname. This directory must not yet exist
(unless overwrite is True), and must be no more than one level beneath an existing directory.
It will be created as a result of this call.
overwrite : bool
If true, outputdirname and all its contents will be deleted and recreated as part
of this call.
"""
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.seriesloader import writeSeriesConfig
from thunder.utils.common import AWSCredentials
if not overwrite:
self._checkOverwrite(outputDirPath)
overwrite = True # prevent additional downstream checks for this path
awsCredentialsOverride = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(outputDirPath, overwrite=overwrite,
awsCredentialsOverride=awsCredentialsOverride)
binseriesRdd = self.toBinarySeries()
binseriesRdd.foreach(writer.writerFcn)
writeSeriesConfig(outputDirPath, len(self.dims), self.nimages, keyType='int16', valueType=self.dtype,
overwrite=overwrite, awsCredentialsOverride=awsCredentialsOverride)
def setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""
Manually set AWS access credentials to be used by Thunder.
Provided for hosted cloud environments without filesystem access.
If launching a cluster using the thunder-ec2 script, credentials will be configured
automatically (inside core-site.xml and ~/.boto), so this method should not need to be called.
Parameters
----------
awsAccessKeyId : string
AWS public key, usually starts with "AKIA"
awsSecretAccessKey : string
AWS private key
"""
from thunder.utils.common import AWSCredentials
self._credentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self._credentials.setOnContext(self._sc)
def __init__(self, sparkContext):
"""Initialize a new ImagesLoader object.
Parameters
----------
sparkcontext: SparkContext
The pyspark SparkContext object used by the current Thunder environment.
"""
from thunder.utils.common import AWSCredentials
self.sc = sparkContext
self.awsCredentialsOverride = AWSCredentials.fromContext(sparkContext)
def exportAsPngs(self, outputDirPath, filePrefix="export", overwrite=False,
collectToDriver=True):
"""
Write out basic png files for two-dimensional image data.
Files will be written into a newly-created directory on the local file system given by outputdirname.
All workers must be able to see the output directory via an NFS share or similar.
Parameters
----------
outputDirPath : string
Path to output directory to be created. Exception will be thrown if this directory already
exists, unless overwrite is True. Directory must be one level below an existing directory.
filePrefix : string
String to prepend to all filenames. Files will be named <fileprefix>00000.png, <fileprefix>00001.png, etc
overwrite : bool
If true, the directory given by outputdirname will first be deleted if it already exists.
collectToDriver : bool, default True
If true, images will be collect()'ed at the driver first before being written out, allowing
for use of a local filesystem at the expense of network overhead. If false, images will be written
in parallel by each executor, presumably to a distributed or networked filesystem.
"""
dims = self.dims
if not len(dims) == 2:
raise ValueError("Only two-dimensional images can be exported as .png files; image is %d-dimensional." %
len(dims))
from matplotlib.pyplot import imsave
from io import BytesIO
from thunder.rdds.fileio.writers import getParallelWriterForPath, getCollectedFileWriterForPath
from thunder.utils.common import AWSCredentials
def toFilenameAndPngBuf(kv):
key, img = kv
fname = filePrefix+"%05d.png" % int(key)
bytebuf = BytesIO()
imsave(bytebuf, img, format="png")
return fname, bytebuf.getvalue()
bufRdd = self.rdd.map(toFilenameAndPngBuf)
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
if collectToDriver:
writer = getCollectedFileWriterForPath(outputDirPath)(outputDirPath, overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
writer.writeCollectedFiles(bufRdd.collect())
else:
writer = getParallelWriterForPath(outputDirPath)(outputDirPath, overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
bufRdd.foreach(writer.writerFcn)
def __init__(self, sparkContext):
"""Initialize a new ImagesLoader object.
Parameters
----------
sparkcontext: SparkContext
The pyspark SparkContext object used by the current Thunder environment.
"""
from thunder.utils.common import AWSCredentials
self.sc = sparkContext
self.awsCredentialsOverride = AWSCredentials.fromContext(sparkContext)
def setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""
Manually set AWS access credentials to be used by Thunder.
This method is provided primarily for hosted environments that do not provide
filesystem access (e.g. Databricks Cloud). Typically AWS credentials can be set
and read from core-site.xml (for Hadoop input format readers, such as Series
binary files), ~/.boto or other boto credential file locations, or the environment
variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. These credentials should
be configured automatically in clusters launched by the thunder-ec2 script, and
so this method should not have to be called.
Parameters
----------
awsAccessKeyId : string
AWS public key, usually starts with "AKIA"
awsSecretAccessKey : string
AWS private key
"""
from thunder.utils.common import AWSCredentials
self._credentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self._credentials.setOnContext(self._sc)
def __init__(self, sparkContext, minPartitions=None):
"""Initialize a new SeriesLoader object.
Parameters
----------
sparkcontext: SparkContext
The pyspark SparkContext object used by the current Thunder environment.
minPartitions: int
minimum number of partitions to use when loading data. (Used by fromText, fromMatLocal, and fromNpyLocal)
"""
from thunder.utils.common import AWSCredentials
self.sc = sparkContext
self.minPartitions = minPartitions
self.awsCredentialsOverride = AWSCredentials.fromContext(sparkContext)
def saveAsBinaryImages(self,
outputDirPath,
filePrefix="export",
overwrite=False):
"""
Write out images or volumes as flat binary files.
Files will be written into a newly-created directory given by outputdirname.
Parameters
----------
outputDirPath : string
Path to output directory to be created. Exception will be thrown if this directory already
exists, unless overwrite is True. Directory must be one level below an existing directory.
filePrefix : string
String to prepend to all filenames. Files will be named <fileprefix>-00000.bin, <fileprefix>-00001.bin, etc
overwrite : bool
If true, the directory given by outputdirname will first be deleted if it already exists.
"""
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.imagesloader import writeBinaryImagesConfig
from thunder.utils.common import AWSCredentials
dimsTotal = list(asarray(self.dims.max) - asarray(self.dims.min) + 1)
def toFilenameAndBinaryBuf(kv):
key, img = kv
fname = filePrefix + "-" + "%05d.bin" % int(key)
return fname, img.transpose().copy()
bufRdd = self.rdd.map(toFilenameAndBinaryBuf)
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(
outputDirPath,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
bufRdd.foreach(writer.writerFcn)
writeBinaryImagesConfig(outputDirPath,
dims=dimsTotal,
dtype=self.dtype,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
def setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""Manually set AWS access credentials to be used by Thunder.
This method is provided primarily for hosted environments that do not provide
filesystem access (e.g. Databricks Cloud). Typically AWS credentials can be set
and read from core-site.xml (for Hadoop input format readers, such as Series
binary files), ~/.boto or other boto credential file locations, or the environment
variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. These credentials should
be configured automatically in clusters launched by the thunder-ec2 script, and
so this method should not have to be called.
Parameters
----------
awsAccessKeyId: string AWS public key, usually starts with "AKIA"
awsSecretAccessKey: string AWS private key
"""
from thunder.utils.common import AWSCredentials
self.awsCredentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self.awsCredentials.setOnContext(self._sc)
def saveAsBinarySeries(self, outputDirPath, overwrite=False):
"""Writes out Series-formatted data.
Subclasses are *not* expected to override this method.
Parameters
----------
outputdirname : string path or URI to directory to be created
Output files will be written underneath outputdirname. This directory must not yet exist
(unless overwrite is True), and must be no more than one level beneath an existing directory.
It will be created as a result of this call.
overwrite : bool
If true, outputdirname and all its contents will be deleted and recreated as part
of this call.
"""
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.seriesloader import writeSeriesConfig
from thunder.utils.common import AWSCredentials
if not overwrite:
self._checkOverwrite(outputDirPath)
overwrite = True # prevent additional downstream checks for this path
awsCredentialsOverride = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(
outputDirPath,
overwrite=overwrite,
awsCredentialsOverride=awsCredentialsOverride)
binseriesRdd = self.toBinarySeries()
binseriesRdd.foreach(writer.writerFcn)
writeSeriesConfig(outputDirPath,
len(self.dims),
self.nimages,
keyType='int16',
valueType=self.dtype,
overwrite=overwrite,
awsCredentialsOverride=awsCredentialsOverride)
def saveAsBinaryImages(self, outputDirPath, filePrefix="export", overwrite=False):
"""
Write out images or volumes as flat binary files.
Files will be written into a newly-created directory given by outputdirname.
Parameters
----------
outputDirPath : string
Path to output directory to be created. Exception will be thrown if this directory already
exists, unless overwrite is True. Directory must be one level below an existing directory.
filePrefix : string
String to prepend to all filenames. Files will be named <fileprefix>-00000.bin, <fileprefix>-00001.bin, etc
overwrite : bool
If true, the directory given by outputdirname will first be deleted if it already exists.
"""
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.imagesloader import writeBinaryImagesConfig
from thunder.utils.common import AWSCredentials
dimsTotal = list(asarray(self.dims.max)-asarray(self.dims.min)+1)
def toFilenameAndBinaryBuf(kv):
key, img = kv
fname = filePrefix+"-"+"%05d.bin" % int(key)
return fname, img.transpose().copy()
bufRdd = self.rdd.map(toFilenameAndBinaryBuf)
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(outputDirPath, overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
bufRdd.foreach(writer.writerFcn)
writeBinaryImagesConfig(outputDirPath, dims=dimsTotal, dtype=self.dtype,
overwrite=overwrite, awsCredentialsOverride=awsCredentials)
def _checkOverwrite(self, outputDirPath):
""" Checks for existence of outputDirPath, raising ValueError if it already exists """
from thunder.utils.common import AWSCredentials, raiseErrorIfPathExists
awsCredentialOverride = AWSCredentials.fromContext(self.rdd.ctx)
raiseErrorIfPathExists(outputDirPath,
awsCredentialsOverride=awsCredentialOverride)
def saveAsBinarySeries(self, outputDirPath, overwrite=False):
"""
Writes out Series-formatted data.
This method (Series.saveAsBinarySeries) writes out binary series files using the current partitioning
of this Series object. (That is, if mySeries.rdd.getNumPartitions() == 5, then 5 files will be written
out, one per partition.) The records will not be resorted; the file names for each partition will be
taken from the key of the first Series record in that partition. If the Series object is already
sorted and no records have been removed by filtering, then the resulting output should be equivalent
to what one would get from calling myImages.saveAsBinarySeries().
If all one wishes to do is to save out Images data in a binary series format, then
tsc.convertImagesToSeries() will likely be more efficient than
tsc.loadImages().toSeries().saveAsBinarySeries().
Parameters
----------
outputDirPath : string path or URI to directory to be created
Output files will be written underneath outputdirname. This directory must not yet exist
(unless overwrite is True), and must be no more than one level beneath an existing directory.
It will be created as a result of this call.
overwrite : bool
If true, outputdirname and all its contents will be deleted and recreated as part
of this call.
"""
import cStringIO as StringIO
import struct
from thunder.rdds.imgblocks.blocks import SimpleBlocks
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.seriesloader import writeSeriesConfig
from thunder.utils.common import AWSCredentials
if not overwrite:
self._checkOverwrite(outputDirPath)
overwrite = True # prevent additional downstream checks for this path
def partitionToBinarySeries(kvIter):
""" Collects all Series records in a partition into a single binary series record. """
keypacker = None
firstKey = None
buf = StringIO.StringIO()
for seriesKey, series in kvIter:
if keypacker is None:
keypacker = struct.Struct('h' * len(seriesKey))
firstKey = seriesKey
# print >> sys.stderr, seriesKey, series, series.tostring().encode('hex')
buf.write(keypacker.pack(*seriesKey))
buf.write(series.tostring())
val = buf.getvalue()
buf.close()
# we might have an empty partition, in which case firstKey will still be None
if firstKey is None:
return iter([])
else:
label = SimpleBlocks.getBinarySeriesNameForKey(
firstKey) + ".bin"
return iter([(label, val)])
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(
outputDirPath,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
binseriesrdd = self.rdd.mapPartitions(partitionToBinarySeries)
binseriesrdd.foreach(writer.writerFcn)
# TODO: all we really need here are the number of keys and number of values, which could in principle
# be cached in _nkeys and _nvals attributes, removing the need for this .first() call in most cases.
firstKey, firstVal = self.first()
writeSeriesConfig(outputDirPath,
len(firstKey),
len(firstVal),
keyType='int16',
valueType=self.dtype,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
def saveAsBinarySeries(self, outputDirPath, overwrite=False):
"""
Writes out Series-formatted data.
This method (Series.saveAsBinarySeries) writes out binary series files using the current partitioning
of this Series object. (That is, if mySeries.rdd.getNumPartitions() == 5, then 5 files will be written
out, one per partition.) The records will not be resorted; the file names for each partition will be
taken from the key of the first Series record in that partition. If the Series object is already
sorted and no records have been removed by filtering, then the resulting output should be equivalent
to what one would get from calling myImages.saveAsBinarySeries().
If all one wishes to do is to save out Images data in a binary series format, then
tsc.convertImagesToSeries() will likely be more efficient than
tsc.loadImages().toSeries().saveAsBinarySeries().
Parameters
----------
outputDirPath : string path or URI to directory to be created
Output files will be written underneath outputdirname. This directory must not yet exist
(unless overwrite is True), and must be no more than one level beneath an existing directory.
It will be created as a result of this call.
overwrite : bool
If true, outputdirname and all its contents will be deleted and recreated as part
of this call.
"""
import cStringIO as StringIO
import struct
from thunder.rdds.imgblocks.blocks import SimpleBlocks
from thunder.rdds.fileio.writers import getParallelWriterForPath
from thunder.rdds.fileio.seriesloader import writeSeriesConfig
from thunder.utils.common import AWSCredentials
if not overwrite:
self._checkOverwrite(outputDirPath)
overwrite = True # prevent additional downstream checks for this path
def partitionToBinarySeries(kvIter):
""" Collects all Series records in a partition into a single binary series record. """
keypacker = None
firstKey = None
buf = StringIO.StringIO()
for seriesKey, series in kvIter:
if keypacker is None:
keypacker = struct.Struct('h'*len(seriesKey))
firstKey = seriesKey
# print >> sys.stderr, seriesKey, series, series.tostring().encode('hex')
buf.write(keypacker.pack(*seriesKey))
buf.write(series.tostring())
val = buf.getvalue()
buf.close()
# we might have an empty partition, in which case firstKey will still be None
if firstKey is None:
return iter([])
else:
label = SimpleBlocks.getBinarySeriesNameForKey(firstKey) + ".bin"
return iter([(label, val)])
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
writer = getParallelWriterForPath(outputDirPath)(outputDirPath, overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
binseriesrdd = self.rdd.mapPartitions(partitionToBinarySeries)
binseriesrdd.foreach(writer.writerFcn)
# TODO: all we really need here are the number of keys and number of values, which could in principle
# be cached in _nkeys and _nvals attributes, removing the need for this .first() call in most cases.
firstKey, firstVal = self.first()
writeSeriesConfig(outputDirPath, len(firstKey), len(firstVal), keyType='int16', valueType=self.dtype,
overwrite=overwrite, awsCredentialsOverride=awsCredentials)
def exportAsPngs(self,
outputDirPath,
filePrefix="export",
overwrite=False,
collectToDriver=True):
"""
Write out basic png files for two-dimensional image data.
Files will be written into a newly-created directory on the local file system given by outputdirname.
All workers must be able to see the output directory via an NFS share or similar.
Parameters
----------
outputDirPath : string
Path to output directory to be created. Exception will be thrown if this directory already
exists, unless overwrite is True. Directory must be one level below an existing directory.
filePrefix : string
String to prepend to all filenames. Files will be named <fileprefix>00000.png, <fileprefix>00001.png, etc
overwrite : bool
If true, the directory given by outputdirname will first be deleted if it already exists.
collectToDriver : bool, default True
If true, images will be collect()'ed at the driver first before being written out, allowing
for use of a local filesystem at the expense of network overhead. If false, images will be written
in parallel by each executor, presumably to a distributed or networked filesystem.
"""
dims = self.dims
if not len(dims) == 2:
raise ValueError(
"Only two-dimensional images can be exported as .png files; image is %d-dimensional."
% len(dims))
from matplotlib.pyplot import imsave
from io import BytesIO
from thunder.rdds.fileio.writers import getParallelWriterForPath, getCollectedFileWriterForPath
from thunder.utils.common import AWSCredentials
def toFilenameAndPngBuf(kv):
key, img = kv
fname = filePrefix + "%05d.png" % int(key)
bytebuf = BytesIO()
imsave(bytebuf, img, format="png")
return fname, bytebuf.getvalue()
bufRdd = self.rdd.map(toFilenameAndPngBuf)
awsCredentials = AWSCredentials.fromContext(self.rdd.ctx)
if collectToDriver:
writer = getCollectedFileWriterForPath(outputDirPath)(
outputDirPath,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
writer.writeCollectedFiles(bufRdd.collect())
else:
writer = getParallelWriterForPath(outputDirPath)(
outputDirPath,
overwrite=overwrite,
awsCredentialsOverride=awsCredentials)
bufRdd.foreach(writer.writerFcn)
class ThunderContext():
"""
Wrapper for a SparkContext that provides functionality for loading data.
Also supports creation of example datasets, and loading example
data both locally and from EC2.
Attributes
----------
`_sc` : SparkContext
Spark context for Spark functionality
awsAccessKeyId: None, or string
awsSecretAccessKey: None, or string
Public and private keys for AWS services. Typically the credentials should be accessible
through any of several different configuration files, and so should not have to be set
on the ThunderContext. See setAWSCredentials().
"""
def __init__(self, sparkcontext):
self._sc = sparkcontext
self.awsCredentials = None
@classmethod
def start(cls, *args, **kwargs):
"""Starts a ThunderContext using the same arguments as SparkContext"""
from pyspark import SparkContext
return ThunderContext(SparkContext(*args, **kwargs))
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 loadImages(self, dataPath, dims=None, inputFormat='stack', ext=None, dtype='int16',
startIdx=None, stopIdx=None, recursive=False, nplanes=None, npartitions=None,
renumber=False):
"""
Loads an Images object from data stored as a binary image stack, tif, or png files.
Supports single files or multiple files, stored on a local file system, a networked file sytem
(mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file 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, similar to a numpy 'shape' parameter, for instance (1024, 1024, 48). Binary
stack data will be interpreted as coming from a multidimensional array of the specified dimensions. Stack
data should be stored in row-major order (Fortran or Matlab convention) rather than column-major order (C
or python/numpy convention), where the first dimension corresponds to that which 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, zo), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
(xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)].
If inputFormat is 'png' or 'tif', the dims parameter (if any) will be ignored; data dimensions
will instead be read out from the image file headers.
inputFormat: {'stack', 'png', 'tif'}. optional, default 'stack'
Expected format of the input data. 'stack' indicates flat files of raw binary data. 'png' or 'tif' indicate
image files of the corresponding formats. Each page of a multipage tif file will be interpreted as a
separate z-plane. For all formats, 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', 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. If inputFormat is
'tif' or 'png', the dtype parameter (if any) will be ignored; data type will instead be read out from the
tif headers.
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.
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).
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.
npartitions: positive int, optional
If specified, request a certain number of partitions for the underlying Spark RDD. Default is 1
partition per image file.
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. See Images.renumber().
Returns
-------
data: thunder.rdds.Images
A newly-created Images object, wrapping an RDD of <int index, numpy array> key-value pairs.
"""
checkParams(inputFormat, ['stack', 'png', 'tif', 'tif-stack'])
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if inputFormat.lower() == 'stack':
data = loader.fromStack(dataPath, dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions)
elif inputFormat.lower().startswith('tif'):
data = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
nplanes=nplanes, npartitions=npartitions)
else:
if nplanes:
raise NotImplementedError("nplanes argument is not supported for png files")
data = loader.fromPng(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, npartitions=npartitions)
if not renumber:
return data
else:
return data.renumber()
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):
"""
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.
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().
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, nplanes=nplanes, npartitions=npartitions)
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.awsCredentials)
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 makeExample(self, dataset, **opts):
"""
Make an example data set for testing analyses.
Options include 'pca', 'kmeans', and 'ica'.
See thunder.utils.datasets for detailed options.
Parameters
----------
dataset : str
Which dataset to generate
Returns
-------
data : RDD of (tuple, array) pairs
Generated dataset
"""
checkParams(dataset, ['kmeans', 'pca', 'ica'])
return DataSets.make(self._sc, dataset, **opts)
def loadExample(self, dataset):
"""
Load a local example data set for testing analyses.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : RDD of (tuple, array) pairs
Generated dataset
"""
import os
path = os.path.dirname(os.path.realpath(__file__))
# this path might actually be inside an .egg file (appears to happen with Spark 1.2)
# check whether data/ directory actually exists on the filesystem, and if not, try
# a hardcoded path that should work on ec2 clusters launched via the thunder-ec2 script
if not os.path.isdir(os.path.join(path, 'data')):
path = "/root/thunder/python/thunder/utils"
if dataset == "iris":
return self.loadSeries(os.path.join(path, 'data/iris/iris.bin'))
elif dataset == "fish-series":
return self.loadSeries(os.path.join(path, 'data/fish/bin/')).astype('float')
elif dataset == "fish-images":
return self.loadImages(os.path.join(path, 'data/fish/tif-stack'), inputFormat="tif")
else:
raise NotImplementedError("Dataset '%s' not known; should be one of 'iris', 'fish-series', 'fish-images'"
% dataset)
def loadExampleEC2(self, dataset):
"""
Load an example data set from EC2.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : RDD of (tuple, array) pairs
Generated dataset
params : Tuple or numpy array
Parameters or metadata for dataset
"""
import json
from numpy import asarray
if 'ec' not in self._sc.master:
raise Exception("must be running on EC2 to load this example data sets")
elif dataset == "zebrafish-optomotor-response":
path = 'zebrafish.datasets/optomotor-response/1/'
data = self.loadSeries("s3n://" + path + 'data/dat_plane*.txt', inputFormat='text', minPartitions=1000,
nkeys=3)
paramFile = self._sc.textFile("s3n://" + path + "params.json")
params = json.loads(paramFile.first())
modelFile = asarray(params['trials'])
return data, modelFile
else:
raise NotImplementedError("dataset '%s' not availiable" % dataset)
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 setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""Manually set AWS access credentials to be used by Thunder.
This method is provided primarily for hosted environments that do not provide
filesystem access (e.g. Databricks Cloud). Typically AWS credentials can be set
and read from core-site.xml (for Hadoop input format readers, such as Series
binary files), ~/.boto or other boto credential file locations, or the environment
variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. These credentials should
be configured automatically in clusters launched by the thunder-ec2 script, and
so this method should not have to be called.
Parameters
----------
awsAccessKeyId: string AWS public key, usually starts with "AKIA"
awsSecretAccessKey: string AWS private key
"""
from thunder.utils.common import AWSCredentials
self.awsCredentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self.awsCredentials.setOnContext(self._sc)
def _checkOverwrite(self, outputDirPath):
""" Checks for existence of outputDirPath, raising ValueError if it already exists """
from thunder.utils.common import AWSCredentials, raiseErrorIfPathExists
awsCredentialOverride = AWSCredentials.fromContext(self.rdd.ctx)
raiseErrorIfPathExists(outputDirPath, awsCredentialsOverride=awsCredentialOverride)
class ThunderContext():
"""
Wrapper for a SparkContext that provides functionality for loading data.
Also supports creation of example datasets, and loading example
data both locally and from EC2.
Attributes
----------
`_sc` : SparkContext
Spark context for Spark functionality
`_credentials` : AWSCredentials object, optional, default = None
Stores public and private keys for AWS services. Typically available through
configuration files, and but can optionally be set on the ThunderContext.
See setAWSCredentials().
"""
def __init__(self, sparkcontext):
self._sc = sparkcontext
self._credentials = None
@classmethod
def start(cls, *args, **kwargs):
"""Starts a ThunderContext using the same arguments as SparkContext"""
from pyspark import SparkContext
return ThunderContext(SparkContext(*args, **kwargs))
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 loadImages(self,
dataPath,
dims=None,
dtype=None,
inputFormat='stack',
ext=None,
startIdx=None,
stopIdx=None,
recursive=False,
nplanes=None,
npartitions=None,
renumber=False,
confFilename='conf.json'):
"""
Loads an Images object from data stored as a binary image stack, tif, or png files.
Supports single files or multiple files, stored on a local file system, a networked file sytem
(mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file 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, similar to a numpy 'shape' parameter, for instance (1024, 1024, 48). Binary
stack data will be interpreted as coming from a multidimensional array of the specified dimensions. Stack
data should be stored in row-major order (Fortran or Matlab convention) rather than column-major order (C
or python/numpy convention), where the first dimension corresponds to that which 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, zo), ..., (xN, y0, z0), (x0, y1, z0), (x1, y1, z0), ...,
(xN, yM, z0), (x0, y0, z1), ..., (xN, yM, zP)].
If inputFormat is 'png' or 'tif', the dims parameter (if any) will be ignored; data dimensions
will instead be read out from the image file headers.
inputFormat: {'stack', 'png', 'tif'}. optional, default 'stack'
Expected format of the input data. 'stack' indicates flat files of raw binary data. 'png' or 'tif' indicate
image files of the corresponding formats. Each page of a multipage tif file will be interpreted as a
separate z-plane. For all formats, 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', 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. If inputFormat is
'tif' or 'png', the dtype parameter (if any) will be ignored; data type will instead be read out from the
tif headers.
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.
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).
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.
npartitions: positive int, optional
If specified, request a certain number of partitions for the underlying Spark RDD. Default is 1
partition per image file.
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. See Images.renumber().
Returns
-------
data: thunder.rdds.Images
A newly-created Images object, wrapping an RDD of <int index, numpy array> key-value pairs.
"""
checkParams(inputFormat, ['stack', 'png', 'tif', 'tif-stack'])
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if inputFormat.lower() == 'stack':
data = loader.fromStack(dataPath,
dims=dims,
dtype=dtype,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive,
nplanes=nplanes,
npartitions=npartitions,
confFilename=confFilename)
elif inputFormat.lower().startswith('tif'):
data = loader.fromTif(dataPath,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive,
nplanes=nplanes,
npartitions=npartitions)
else:
if nplanes:
raise NotImplementedError(
"nplanes argument is not supported for png files")
data = loader.fromPng(dataPath,
ext=ext,
startIdx=startIdx,
stopIdx=stopIdx,
recursive=recursive,
npartitions=npartitions)
if not renumber:
return data
else:
return data.renumber()
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):
"""
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.
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().
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,
nplanes=nplanes,
npartitions=npartitions)
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 makeExample(self, dataset, **opts):
"""
Make an example data set for testing analyses.
Options include 'pca', 'kmeans', and 'ica'.
See thunder.utils.datasets for detailed options.
Parameters
----------
dataset : str
Which dataset to generate
Returns
-------
data : RDD of (tuple, array) pairs
Generated dataset
"""
checkParams(dataset, ['kmeans', 'pca', 'ica'])
return DataSets.make(self._sc, dataset, **opts)
def loadExample(self, dataset=None):
"""
Load a local example data set for testing analyses.
Some of these data sets are extremely downsampled and should be considered
useful only for testing the API. If called with None,
will return list of available datasets.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : Data object
Generated dataset as a Thunder data objects (e.g Series or Images)
"""
import atexit
import shutil
import tempfile
from pkg_resources import resource_listdir, resource_filename
DATASETS = {
'iris': 'iris',
'fish-series': 'fish/bin',
'fish-images': 'fish/tif-stack'
}
if dataset is None:
return DATASETS.keys()
checkParams(dataset, DATASETS.keys())
if 'ec2' in self._sc.master:
tmpdir = os.path.join('/root/thunder/python/thunder/utils', 'data',
DATASETS[dataset])
else:
tmpdir = tempfile.mkdtemp()
atexit.register(shutil.rmtree, tmpdir)
def copyLocal(target):
files = resource_listdir('thunder.utils.data', target)
for f in files:
path = resource_filename('thunder.utils.data',
os.path.join(target, f))
shutil.copy(path, tmpdir)
copyLocal(DATASETS[dataset])
if dataset == "iris":
return self.loadSeries(tmpdir)
elif dataset == "fish-series":
return self.loadSeries(tmpdir).astype('float')
elif dataset == "fish-images":
return self.loadImages(tmpdir, inputFormat="tif")
def loadExampleS3(self, dataset=None):
"""
Load an example data set from S3.
Info on the included datasets can be found at the CodeNeuro data repository
(http://datasets.codeneuro.org/). If called with None, will return
list of available datasets.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : a Data object (usually a Series or Images)
The dataset as one of Thunder's data objects
params : dict
Parameters or metadata for dataset
"""
DATASETS = {
'ahrens.lab/direction.selectivity':
'ahrens.lab/direction.selectivity/1/',
'ahrens.lab/optomotor.response':
'ahrens.lab/optomotor.response/1/',
'svoboda.lab/tactile.navigation':
'svoboda.lab/tactile.navigation/1/'
}
if 'local' in self._sc.master:
raise Exception(
"Must be running on an EC2 cluster to load this example data set"
)
if dataset is None:
return DATASETS.keys()
checkParams(dataset, DATASETS.keys())
basePath = 's3n://neuro.datasets/'
dataPath = DATASETS[dataset]
data = self.loadSeries(basePath + dataPath + 'series')
params = self.loadParams(basePath + dataPath +
'params/covariates.json')
return data, params
def loadParams(self, path):
"""
Load a file with parameters from a local file system or S3.
Assumes file is JSON with basic types (strings, integers, doubles, lists),
in either a single dict or list of dict-likes, and each dict has at least
a "name" field and a "value" field.
Useful for loading generic meta data, parameters, covariates, etc.
Parameters
----------
path : str
Path to file, can be on a local file system or an S3 bucket
Returns
-------
A dict or list with the parameters
"""
import json
from thunder.rdds.fileio.readers import getFileReaderForPath, FileNotFoundError
reader = getFileReaderForPath(path)(
awsCredentialsOverride=self._credentials)
try:
buffer = reader.read(path)
except FileNotFoundError:
raise Exception("Cannot find file %s" % path)
return Params(json.loads(buffer))
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 export(self,
data,
filename,
format=None,
overwrite=False,
varname=None):
"""
Export local array data to a variety of formats.
Can write to a local file sytem or S3 (destination inferred from filename schema).
S3 writing useful for persisting arrays when working in an environment without
accessible local storage.
Parameters
----------
data : array-like
The data to export
filename : str
Output location (path/to/file.ext)
format : str, optional, default = None
Ouput format ("npy", "mat", or "txt"), if not provided will
try to infer from file extension.
overwrite : boolean, optional, default = False
Whether to overwrite if directory or file already exists
varname : str, optional, default = None
Variable name for writing "mat" formatted files
"""
from numpy import save, savetxt
from scipy.io import savemat
from StringIO import StringIO
from thunder.rdds.fileio.writers import getFileWriterForPath
path, file, format = handleFormat(filename, format)
checkParams(format, ["npy", "mat", "txt"])
clazz = getFileWriterForPath(filename)
writer = clazz(path,
file,
overwrite=overwrite,
awsCredentialsOverride=self._credentials)
stream = StringIO()
if format == "mat":
varname = os.path.splitext(file)[0] if varname is None else varname
savemat(stream,
mdict={varname: data},
oned_as='column',
do_compression='true')
if format == "npy":
save(stream, data)
if format == "txt":
savetxt(stream, data)
stream.seek(0)
writer.writeFile(stream.buf)
def setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""
Manually set AWS access credentials to be used by Thunder.
This method is provided primarily for hosted environments that do not provide
filesystem access (e.g. Databricks Cloud). Typically AWS credentials can be set
and read from core-site.xml (for Hadoop input format readers, such as Series
binary files), ~/.boto or other boto credential file locations, or the environment
variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. These credentials should
be configured automatically in clusters launched by the thunder-ec2 script, and
so this method should not have to be called.
Parameters
----------
awsAccessKeyId : string
AWS public key, usually starts with "AKIA"
awsSecretAccessKey : string
AWS private key
"""
from thunder.utils.common import AWSCredentials
self._credentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self._credentials.setOnContext(self._sc)
class ThunderContext():
"""
Wrapper for a SparkContext that provides an entry point for loading and saving.
Also supports creation of example datasets, and loading example
data both locally and from EC2.
Attributes
----------
`_sc` : SparkContext
Spark context for Spark functionality
`_credentials` : AWSCredentials object, optional, default = None
Stores public and private keys for AWS services. Typically available through
configuration files, and but can optionally be set using :func:`ThunderContext.setAWSCredentials()`.
"""
def __init__(self, sparkcontext):
self._sc = sparkcontext
self._credentials = None
@classmethod
def start(cls, *args, **kwargs):
"""
Starts a ThunderContext using the same arguments as SparkContext
"""
from pyspark import SparkContext
return ThunderContext(SparkContext(*args, **kwargs))
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 loadImages(self, dataPath, dims=None, dtype=None, inputFormat='stack', ext=None,
startIdx=None, stopIdx=None, recursive=False, nplanes=None, npartitions=None,
renumber=False, confFilename='conf.json'):
"""
Loads an Images object from data stored as a binary image stack, tif, or png files.
Supports single files or multiple files, stored on a local file system, a networked file sytem
(mounted and available on all nodes), or Amazon S3. HDFS is not currently supported for image file 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 format. 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).
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.
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.Images
An Images object, wrapping an RDD of with (int) : (numpy array) pairs
"""
checkParams(inputFormat, ['stack', 'png', 'tif', 'tif-stack'])
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
# Checking StartIdx is smaller or equal to StopIdx
if startIdx is not None and stopIdx is not None and startIdx > stopIdx:
raise Exception("Error. startIdx {} is larger than stopIdx {}".inputFormat(startIdx, stopIdx))
if not ext:
ext = DEFAULT_EXTENSIONS.get(inputFormat.lower(), None)
if inputFormat.lower() == 'stack':
data = loader.fromStack(dataPath, dims=dims, dtype=dtype, ext=ext, startIdx=startIdx, stopIdx=stopIdx,
recursive=recursive, nplanes=nplanes, npartitions=npartitions,
confFilename=confFilename)
elif inputFormat.lower().startswith('tif'):
data = loader.fromTif(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
nplanes=nplanes, npartitions=npartitions)
else:
if nplanes:
raise NotImplementedError("nplanes argument is not supported for png files")
data = loader.fromPng(dataPath, ext=ext, startIdx=startIdx, stopIdx=stopIdx, recursive=recursive,
npartitions=npartitions)
if not renumber:
return data
else:
return data.renumber()
def loadImagesOCP(self, bucketName, resolution, server='ocp.me', startIdx=None, stopIdx=None,
minBound=None, maxBound=None):
"""
Load Images from OCP (Open Connectome Project).
The OCP is a web service for access to EM brain images and other neural image data.
The web-service can be accessed at http://www.openconnectomeproject.org/.
Parameters
----------
bucketName: string
Token name for the project in OCP. This name should exist on the server from which data is loaded.
resolution: nonnegative int
Resolution of the data in OCP
server: string, optional, default = 'ocp.me'
Name of the OCP server with the specified token.
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).
stopIdx: nonnegative int, optional
See startIdx.
minBound, maxBound: tuple of nonnegative int, optional, default = None
X,Y,Z bounds of the data to fetch from OCP. minBound contains the (xMin,yMin,zMin) while
maxBound contains (xMax,yMax,zMax).
Returns
-------
data: thunder.rdds.Images
An Images object, wrapping an RDD of with (int) : (numpy array) pairs
"""
from thunder.rdds.fileio.imagesloader import ImagesLoader
loader = ImagesLoader(self._sc)
# Checking StartIdx is smaller or equal to StopIdx
if startIdx is not None and stopIdx is not None and startIdx > stopIdx:
raise Exception("Error. startIdx {} is larger than stopIdx {}".format(startIdx, stopIdx))
data = loader.fromOCP(bucketName, resolution=resolution, server=server, startIdx=startIdx,
stopIdx=stopIdx, minBound=minBound, maxBound=maxBound)
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, 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 makeExample(self, dataset, **opts):
"""
Make an example data set for testing analyses.
Options include 'pca', 'kmeans', and 'ica'.
See thunder.utils.datasets for detailed options.
Parameters
----------
dataset : str
Which dataset to generate
Returns
-------
data : RDD of (tuple, array) pairs
Generated dataset
"""
from thunder.utils.datasets import DATASET_MAKERS
checkParams(dataset, DATASET_MAKERS.keys())
return DataSets.make(self._sc, dataset, **opts)
def loadExample(self, dataset=None):
"""
Load a local example data set for testing analyses.
Some of these data sets are extremely downsampled and should be considered
useful only for testing the API. If called with None,
will return list of available datasets.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : Data object
Generated dataset as a Thunder data objects (e.g Series or Images)
"""
import atexit
import shutil
import tempfile
from pkg_resources import resource_listdir, resource_filename
DATASETS = {
'iris': 'iris',
'fish-series': 'fish/series',
'fish-images': 'fish/images',
'mouse-series': 'mouse/series',
'mouse-images': 'mouse/images',
'mouse-params': 'mouse/params'
}
if dataset is None:
return sorted(DATASETS.keys())
checkParams(dataset, DATASETS.keys())
if 'ec2' in self._sc.master:
tmpdir = os.path.join('/root/thunder/python/thunder/utils', 'data', DATASETS[dataset])
else:
tmpdir = tempfile.mkdtemp()
atexit.register(shutil.rmtree, tmpdir)
def copyLocal(target):
files = resource_listdir('thunder.utils.data', target)
for f in files:
path = resource_filename('thunder.utils.data', os.path.join(target, f))
shutil.copy(path, tmpdir)
copyLocal(DATASETS[dataset])
npartitions = self._sc.defaultParallelism
if dataset == "iris":
return self.loadSeries(tmpdir)
elif dataset == "fish-series":
return self.loadSeries(tmpdir).astype('float')
elif dataset == "fish-images":
return self.loadImages(tmpdir, inputFormat="tif", npartitions=npartitions)
elif dataset == "mouse-series":
return self.loadSeries(tmpdir).astype('float')
elif dataset == "mouse-images":
return self.loadImages(tmpdir, npartitions=npartitions)
elif dataset == "mouse-params":
return self.loadParams(os.path.join(tmpdir, 'covariates.json'))
def loadExampleS3(self, dataset=None):
"""
Load an example data set from S3.
Info on the included datasets can be found at the CodeNeuro data repository
(http://datasets.codeneuro.org/). If called with None, will return
list of available datasets.
Parameters
----------
dataset : str
Which dataset to load
Returns
-------
data : a Data object (usually a Series or Images)
The dataset as one of Thunder's data objects
params : dict
Parameters or metadata for dataset
"""
DATASETS = {
'ahrens.lab/direction.selectivity': 'ahrens.lab/direction.selectivity/1/',
'ahrens.lab/optomotor.response': 'ahrens.lab/optomotor.response/1/',
'svoboda.lab/tactile.navigation': 'svoboda.lab/tactile.navigation/1/'
}
if dataset is None:
return DATASETS.keys()
if 'local' in self._sc.master:
raise Exception("Must be running on an EC2 cluster to load this example data set")
checkParams(dataset, DATASETS.keys())
basePath = 's3n://neuro.datasets/'
dataPath = DATASETS[dataset]
data = self.loadSeries(basePath + dataPath + 'series')
params = self.loadParams(basePath + dataPath + 'params/covariates.json')
return data, params
def loadJSON(self, path):
"""
Generic function for loading JSON from a path, handling local file systems and S3
Parameters
----------
path : str
Path to a file, can be on a local file system or an S3 bucket
Returns
-------
A string with the JSON
"""
import json
from thunder.rdds.fileio.readers import getFileReaderForPath, FileNotFoundError
reader = getFileReaderForPath(path)(awsCredentialsOverride=self._credentials)
try:
buffer = reader.read(path)
except FileNotFoundError:
raise Exception("Cannot find file %s" % path)
return json.loads(buffer)
def loadParams(self, path):
"""
Load a file with parameters from a local file system or S3.
Assumes file is JSON with basic types (strings, integers, doubles, lists),
in either a single dict or list of dict-likes, and each dict has at least
a "name" field and a "value" field.
Useful for loading generic meta data, parameters, covariates, etc.
Parameters
----------
path : str
Path to file, can be on a local file system or an S3 bucket
Returns
-------
A dict or list with the parameters
"""
blob = self.loadJSON(path)
return Params(blob)
def loadSources(self, path):
"""
Load a file with sources from a local file system or S3.
Parameters
----------
path : str
Path to file, can be on a local file system or an S3 bucket
Returns
-------
A SourceModel
See also
--------
thunder.SourceExtraction
"""
from thunder import SourceExtraction
blob = self.loadJSON(path)
return SourceExtraction.deserialize(blob)
def export(self, data, filename, outputFormat=None, overwrite=False, varname=None):
"""
Export local array data to a variety of formats.
Can write to a local file sytem or S3 (destination inferred from filename schema).
S3 writing useful for persisting arrays when working in an environment without
accessible local storage.
Parameters
----------
data : array-like
The data to export
filename : str
Output location (path/to/file.ext)
outputFormat : str, optional, default = None
Ouput format ("npy", "mat", or "txt"), if not provided will
try to infer from file extension.
overwrite : boolean, optional, default = False
Whether to overwrite if directory or file already exists
varname : str, optional, default = None
Variable name for writing "mat" formatted files
"""
from numpy import save, savetxt, asarray
from scipy.io import savemat
from StringIO import StringIO
from thunder.rdds.fileio.writers import getFileWriterForPath
path, file, outputFormat = handleFormat(filename, outputFormat)
checkParams(outputFormat, ["npy", "mat", "txt"])
clazz = getFileWriterForPath(filename)
writer = clazz(path, file, overwrite=overwrite, awsCredentialsOverride=self._credentials)
stream = StringIO()
if outputFormat == "mat":
varname = os.path.splitext(file)[0] if varname is None else varname
savemat(stream, mdict={varname: data}, oned_as='column', do_compression='true')
if outputFormat == "npy":
save(stream, data)
if outputFormat == "txt":
if asarray(data).ndim > 2:
raise Exception("Cannot write data with more than two dimensions to text")
savetxt(stream, data)
stream.seek(0)
writer.writeFile(stream.buf)
def setAWSCredentials(self, awsAccessKeyId, awsSecretAccessKey):
"""
Manually set AWS access credentials to be used by Thunder.
Provided for hosted cloud environments without filesystem access.
If launching a cluster using the thunder-ec2 script, credentials will be configured
automatically (inside core-site.xml and ~/.boto), so this method should not need to be called.
Parameters
----------
awsAccessKeyId : string
AWS public key, usually starts with "AKIA"
awsSecretAccessKey : string
AWS private key
"""
from thunder.utils.common import AWSCredentials
self._credentials = AWSCredentials(awsAccessKeyId, awsSecretAccessKey)
self._credentials.setOnContext(self._sc)
def __init__(self, awsCredentialsOverride=None):
"""Initialization; validates that AWS keys are available as environment variables.
Will let boto library look up credentials itself according to its own rules - e.g. first looking for
AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, then going through several possible config files and finally
looking for a ~/.aws/credentials .ini-formatted file. See boto docs:
http://boto.readthedocs.org/en/latest/boto_config_tut.html
However, if an AWSCredentials object is provided, its `awsAccessKeyId` and `awsSecretAccessKey` attributes
will be used instead of those found by the standard boto credential lookup process.
"""
if not _haveBoto:
raise ValueError(
"The boto package does not appear to be available; boto is required for BotoS3Reader"
)
self.awsCredentialsOverride = awsCredentialsOverride if awsCredentialsOverride else AWSCredentials(
)