def aggregate(dataPath, outputPath, days=0, hours=0):
with FileRecordStream(dataPath) as reader:
aggregator = Aggregator({'fields': [('messages', 'sum')],
'days': days,
'hours': hours},
reader.getFields())
with open(outputPath, 'w') as outfile:
writer = csv.writer(outfile)
writer.writerow(['timestamp', 'messages'])
writer.writerow(['datetime', 'int'])
writer.writerow(['T', ''])
while True:
inRecord = reader.getNextRecord()
bookmark = reader.getBookmark()
(aggRecord, aggBookmark) = aggregator.next(inRecord, bookmark)
# reached EOF?
if inRecord is None and aggRecord is None:
break
if aggRecord is not None:
timestamp = aggRecord[0].strftime('%Y-%m-%d %H:%M:%S.0')
writer.writerow([timestamp, aggRecord[1]])
def _aggregate(input, options, output, timeFieldName):
""" Aggregate the input stream and write aggregated records to the output
stream
"""
aggregator = Aggregator(aggregationInfo=options, inputFields=input.getFields(), timeFieldName=timeFieldName)
while True:
inRecord = input.getNextRecord()
print "Feeding in: ", inRecord
(outRecord, aggBookmark) = aggregator.next(record=inRecord, curInputBookmark=None)
print "Record out: ", outRecord
if outRecord is not None:
output.appendRecord(outRecord, None)
if inRecord is None and outRecord is None:
break
def _aggregate(input, options, output, timeFieldName):
""" Aggregate the input stream and write aggregated records to the output
stream
"""
aggregator = Aggregator(aggregationInfo=options,
inputFields=input.getFields(),
timeFieldName=timeFieldName)
while True:
inRecord = input.getNextRecord()
print "Feeding in: ", inRecord
(outRecord, aggBookmark) = aggregator.next(record=inRecord,
curInputBookmark=None)
print "Record out: ", outRecord
if outRecord is not None:
output.appendRecord(outRecord, None)
if inRecord is None and outRecord is None:
break
class StreamReader(RecordStreamIface):
"""
Implements a stream reader. This is a high level class that owns one or more
underlying implementations of a RecordStreamIFace. Each RecordStreamIFace
implements the raw reading of records from the record store (which could be a
file, hbase table or something else).
In the future, we will support joining of two or more RecordStreamIface's (
which is why the streamDef accepts a list of 'stream' elements), but for now
only 1 source is supported.
The class also implements aggregation of the (in the future) joined records
from the sources.
This module parses the stream definition (as defined in
/nupic/frameworks/opf/jsonschema/stream_def.json), creates the
RecordStreamIFace for each source ('stream's element) defined in the stream
def, performs aggregation, and returns each record in the correct format
according to the desired column names specified in the streamDef.
This class implements the RecordStreamIFace interface and thus can be used
in place of a raw record stream.
This is an example streamDef:
{
'version': 1
'info': 'test_hotgym',
'streams': [
{'columns': [u'*'],
'info': u'hotGym.csv',
'last_record': 4000,
'source': u'file://extra/hotgym/hotgym.csv'}.
],
'timeField': 'timestamp',
'aggregation': {
'hours': 1,
'fields': [
('timestamp', 'first'),
('gym', 'first'),
('consumption', 'sum')
],
}
}
"""
def __init__(self, streamDef, bookmark=None, saveOutput=False,
isBlocking=True, maxTimeout=0, eofOnTimeout=False):
""" Base class constructor, performs common initialization
Parameters:
----------------------------------------------------------------
streamDef: The stream definition, potentially containing multiple sources
(not supported yet). See
/nupic/frameworks/opf/jsonschema/stream_def.json for the format
of this dict
bookmark: Bookmark to start reading from. This overrides the first_record
field of the streamDef if provided.
saveOutput: If true, save the output to a csv file in a temp directory.
The path to the generated file can be found in the log
output.
isBlocking: should read operation block *forever* if the next row of data
is not available, but the stream is not marked as 'completed'
yet?
maxTimeout: if isBlocking is False, max seconds to wait for more data before
timing out; ignored when isBlocking is True.
eofOnTimeout: If True and we get a read timeout (isBlocking must be False
to get read timeouts), assume we've reached the end of the
input and produce the last aggregated record, if one can be
completed.
"""
# Call superclass constructor
super(StreamReader, self).__init__()
loggerPrefix = 'com.numenta.nupic.data.StreamReader'
self._logger = logging.getLogger(loggerPrefix)
jsonhelpers.validate(streamDef,
schemaPath=pkg_resources.resource_filename(
jsonschema.__name__, "stream_def.json"))
assert len(streamDef['streams']) == 1, "Only 1 source stream is supported"
# Save constructor args
sourceDict = streamDef['streams'][0]
self._recordCount = 0
self._eofOnTimeout = eofOnTimeout
self._logger.debug('Reading stream with the def: %s', sourceDict)
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
# ---------------------------------------------------------------------
# Get the stream definition params
# Limiting window of the stream. It would not return any records until
# 'first_record' ID is read (or very first with the ID above that). The
# stream will return EOS once it reads record with ID 'last_record' or
# above (NOTE: the name 'lastRecord' is misleading because it is NOT
# inclusive).
firstRecordIdx = sourceDict.get('first_record', None)
self._sourceLastRecordIdx = sourceDict.get('last_record', None)
# If a bookmark was given, then override first_record from the stream
# definition.
if bookmark is not None:
firstRecordIdx = None
# Column names must be provided in the streamdef json
# Special case is ['*'], meaning all available names from the record stream
self._streamFieldNames = sourceDict.get('columns', None)
if self._streamFieldNames != None and self._streamFieldNames[0] == '*':
self._needFieldsFiltering = False
else:
self._needFieldsFiltering = True
# Types must be specified in streamdef json, or in case of the
# file_recod_stream types could be implicit from the file
streamFieldTypes = sourceDict.get('types', None)
self._logger.debug('Types from the def: %s', streamFieldTypes)
# Validate that all types are valid
if streamFieldTypes is not None:
for dataType in streamFieldTypes:
assert FieldMetaType.isValid(dataType)
# Reset, sequence and time fields might be provided by streamdef json
streamResetFieldName = streamDef.get('resetField', None)
streamTimeFieldName = streamDef.get('timeField', None)
streamSequenceFieldName = streamDef.get('sequenceIdField', None)
self._logger.debug('r, t, s fields: %s, %s, %s', streamResetFieldName,
streamTimeFieldName,
streamSequenceFieldName)
# =======================================================================
# Open up the underlying record store
dataUrl = sourceDict.get('source', None)
assert dataUrl is not None
self._recordStore = self._openStream(dataUrl, isBlocking, maxTimeout,
bookmark, firstRecordIdx)
assert self._recordStore is not None
# =======================================================================
# Prepare the data structures we need for returning just the fields
# the caller wants from each record
recordStoreFields = self._recordStore.getFields()
self._recordStoreFieldNames = self._recordStore.getFieldNames()
if not self._needFieldsFiltering:
self._streamFieldNames = self._recordStoreFieldNames
# Build up the field definitions for each field. This is a list of tuples
# of (name, type, special)
self._streamFields = []
for dstIdx, name in enumerate(self._streamFieldNames):
if name not in self._recordStoreFieldNames:
raise RuntimeError("The column '%s' from the stream definition "
"is not present in the underlying stream which has the following "
"columns: %s" % (name, self._recordStoreFieldNames))
fieldIdx = self._recordStoreFieldNames.index(name)
fieldType = recordStoreFields[fieldIdx].type
fieldSpecial = recordStoreFields[fieldIdx].special
# If the types or specials were defined in the stream definition,
# then override what was found in the record store
if streamFieldTypes is not None:
fieldType = streamFieldTypes[dstIdx]
if streamResetFieldName is not None and streamResetFieldName == name:
fieldSpecial = FieldMetaSpecial.reset
if streamTimeFieldName is not None and streamTimeFieldName == name:
fieldSpecial = FieldMetaSpecial.timestamp
if (streamSequenceFieldName is not None and
streamSequenceFieldName == name):
fieldSpecial = FieldMetaSpecial.sequence
self._streamFields.append(FieldMetaInfo(name, fieldType, fieldSpecial))
# ========================================================================
# Create the aggregator which will handle aggregation of records before
# returning them.
self._aggregator = Aggregator(
aggregationInfo=streamDef.get('aggregation', None),
inputFields=recordStoreFields,
timeFieldName=streamDef.get('timeField', None),
sequenceIdFieldName=streamDef.get('sequenceIdField', None),
resetFieldName=streamDef.get('resetField', None))
# We rely on the aggregator to tell us the bookmark of the last raw input
# that contributed to the aggregated record
self._aggBookmark = None
# Compute the aggregation period in terms of months and seconds
if 'aggregation' in streamDef:
self._aggMonthsAndSeconds = nupic.support.aggregationToMonthsSeconds(
streamDef.get('aggregation'))
else:
self._aggMonthsAndSeconds = None
# ========================================================================
# Are we saving the generated output to a csv?
if saveOutput:
tmpDir = tempfile.mkdtemp()
outFilename = os.path.join(tmpDir, "generated_output.csv")
self._logger.info("StreamReader: Saving generated records to: '%s'" %
outFilename)
self._writer = FileRecordStream(streamID=outFilename,
write=True,
fields=self._streamFields)
else:
self._writer = None
@staticmethod
def _openStream(dataUrl,
isBlocking, # pylint: disable=W0613
maxTimeout, # pylint: disable=W0613
bookmark,
firstRecordIdx):
"""Open the underlying file stream
This only supports 'file://' prefixed paths.
:returns: record stream instance
:rtype: FileRecordStream
"""
filePath = dataUrl[len(FILE_PREF):]
if not os.path.isabs(filePath):
filePath = os.path.join(os.getcwd(), filePath)
return FileRecordStream(streamID=filePath,
write=False,
bookmark=bookmark,
firstRecord=firstRecordIdx)
def close(self):
""" Close the stream
"""
return self._recordStore.close()
def getNextRecord(self):
""" Returns combined data from all sources (values only).
Returns None on EOF; empty sequence on timeout.
"""
# Keep reading from the raw input till we get enough for an aggregated
# record
while True:
# Reached EOF due to lastRow constraint?
if self._sourceLastRecordIdx is not None and \
self._recordStore.getNextRecordIdx() >= self._sourceLastRecordIdx:
preAggValues = None # indicates EOF
bookmark = self._recordStore.getBookmark()
else:
# Get the raw record and bookmark
preAggValues = self._recordStore.getNextRecord()
bookmark = self._recordStore.getBookmark()
if preAggValues == (): # means timeout error occurred
if self._eofOnTimeout:
preAggValues = None # act as if we got EOF
else:
return preAggValues # Timeout indicator
self._logger.debug('Read source record #%d: %r',
self._recordStore.getNextRecordIdx()-1, preAggValues)
# Perform aggregation
(fieldValues, aggBookmark) = self._aggregator.next(preAggValues, bookmark)
# Update the aggregated record bookmark if we got a real record back
if fieldValues is not None:
self._aggBookmark = aggBookmark
# Reached EOF?
if preAggValues is None and fieldValues is None:
return None
# Return it if we have a record
if fieldValues is not None:
break
# Do we need to re-order the fields in the record?
if self._needFieldsFiltering:
values = []
srcDict = dict(zip(self._recordStoreFieldNames, fieldValues))
for name in self._streamFieldNames:
values.append(srcDict[name])
fieldValues = values
# Write to debug output?
if self._writer is not None:
self._writer.appendRecord(fieldValues)
self._recordCount += 1
self._logger.debug('Returning aggregated record #%d from getNextRecord(): '
'%r. Bookmark: %r',
self._recordCount-1, fieldValues, self._aggBookmark)
return fieldValues
def getDataRowCount(self):
"""Iterates through stream to calculate total records after aggregation.
This will alter the bookmark state.
"""
inputRowCountAfterAggregation = 0
while True:
record = self.getNextRecord()
if record is None:
return inputRowCountAfterAggregation
inputRowCountAfterAggregation += 1
if inputRowCountAfterAggregation > 10000:
raise RuntimeError('No end of datastream found.')
def getLastRecords(self, numRecords):
"""Saves the record in the underlying storage."""
raise RuntimeError("Not implemented in StreamReader")
def getRecordsRange(self, bookmark=None, range=None):
""" Returns a range of records, starting from the bookmark. If 'bookmark'
is None, then records read from the first available. If 'range' is
None, all available records will be returned (caution: this could be
a lot of records and require a lot of memory).
"""
raise RuntimeError("Not implemented in StreamReader")
def getNextRecordIdx(self):
"""Returns the index of the record that will be read next from
getNextRecord()
"""
return self._recordCount
def recordsExistAfter(self, bookmark):
"""Returns True iff there are records left after the bookmark."""
return self._recordStore.recordsExistAfter(bookmark)
def getAggregationMonthsAndSeconds(self):
""" Returns the aggregation period of the record stream as a dict
containing 'months' and 'seconds'. The months is always an integer and
seconds is a floating point. Only one is allowed to be non-zero at a
time.
If there is no aggregation associated with the stream, returns None.
Typically, a raw file or hbase stream will NOT have any aggregation info,
but subclasses of RecordStreamIFace, like StreamReader, will and will
return the aggregation period from this call. This call is used by the
getNextRecordDict() method to assign a record number to a record given
its timestamp and the aggregation interval
Parameters:
------------------------------------------------------------------------
retval: aggregationPeriod (as a dict) or None
'months': number of months in aggregation period
'seconds': number of seconds in aggregation period (as a float)
"""
return self._aggMonthsAndSeconds
def appendRecord(self, record, inputRef=None):
"""Saves the record in the underlying storage."""
raise RuntimeError("Not implemented in StreamReader")
def appendRecords(self, records, inputRef=None, progressCB=None):
"""Saves multiple records in the underlying storage."""
raise RuntimeError("Not implemented in StreamReader")
def removeOldData(self):
raise RuntimeError("Not implemented in StreamReader")
def seekFromEnd(self, numRecords):
"""Seeks to numRecords from the end and returns a bookmark to the new
position.
"""
raise RuntimeError("Not implemented in StreamReader")
def getFieldNames(self):
""" Returns all fields in all inputs (list of plain names).
NOTE: currently, only one input is supported
"""
return [f.name for f in self._streamFields]
def getFields(self):
""" Returns a sequence of nupic.data.fieldmeta.FieldMetaInfo
name/type/special tuples for each field in the stream.
"""
return self._streamFields
def getBookmark(self):
""" Returns a bookmark to the current position
"""
return self._aggBookmark
def clearStats(self):
""" Resets stats collected so far.
"""
self._recordStore.clearStats()
def getStats(self):
""" Returns stats (like min and max values of the fields).
TODO: This method needs to be enhanced to get the stats on the *aggregated*
records.
"""
# The record store returns a dict of stats, each value in this dict is
# a list with one item per field of the record store
# {
# 'min' : [f1_min, f2_min, f3_min],
# 'max' : [f1_max, f2_max, f3_max]
# }
recordStoreStats = self._recordStore.getStats()
# We need to convert each item to represent the fields of the *stream*
streamStats = dict()
for (key, values) in recordStoreStats.items():
fieldStats = dict(zip(self._recordStoreFieldNames, values))
streamValues = []
for name in self._streamFieldNames:
streamValues.append(fieldStats[name])
streamStats[key] = streamValues
return streamStats
def getError(self):
""" Returns errors saved in the stream.
"""
return self._recordStore.getError()
def setError(self, error):
""" Saves specified error in the stream.
"""
self._recordStore.setError(error)
def isCompleted(self):
""" Returns True if all records have been read.
"""
return self._recordStore.isCompleted()
def setCompleted(self, completed=True):
""" Marks the stream completed (True or False)
"""
# CSV file is always considered completed, nothing to do
self._recordStore.setCompleted(completed)
def setTimeout(self, timeout):
""" Set the read timeout """
self._recordStore.setTimeout(timeout)
def flush(self):
""" Flush the file to disk """
raise RuntimeError("Not implemented in StreamReader")
class StreamReader(RecordStreamIface):
"""
Implements a stream reader. This is a high level class that owns one or more
underlying implementations of a
:class:`~nupic.data.record_stream.RecordStreamIface`. Each
:class:`~nupic.data.record_stream.RecordStreamIface` implements the raw
reading of records from the record store (which could be a file, hbase table
or something else).
In the future, we will support joining of two or more
:class:`~nupic.data.record_stream.RecordStreamIface`'s (which is why the
``streamDef`` accepts a list of 'stream' elements), but for now only 1 source
is supported.
The class also implements aggregation of the (in the future) joined records
from the sources.
This module parses the stream definition (as defined in
``/src/nupic/frameworks/opf/jsonschema/stream_def.json``), creates the
:class:`~nupic.data.record_stream.RecordStreamIface` for each source
('stream' element) defined in the stream def, performs aggregation, and
returns each record in the correct format according to the desired column
names specified in the streamDef.
This class implements the :class:`~nupic.data.record_stream.RecordStreamIface`
interface and thus can be used in place of a raw record stream.
This is an example streamDef:
.. code-block:: python
{
'version': 1
'info': 'test_hotgym',
'streams': [
{'columns': [u'*'],
'info': u'hotGym.csv',
'last_record': 4000,
'source': u'file://extra/hotgym/hotgym.csv'}.
],
'timeField': 'timestamp',
'aggregation': {
'hours': 1,
'fields': [
('timestamp', 'first'),
('gym', 'first'),
('consumption', 'sum')
],
}
}
:param streamDef: The stream definition, potentially containing multiple
sources (not supported yet). See
``src//nupic/frameworks/opf/jsonschema/stream_def.json`` for the
format of this dict
:param bookmark: Bookmark to start reading from. This overrides the
first_record field of the streamDef if provided.
:param saveOutput: If true, save the output to a csv file in a temp
directory. The path to the generated file can be found in the log
output.
:param isBlocking: should read operation block *forever* if the next row of
data is not available, but the stream is not marked as 'completed'
yet?
:param maxTimeout: if isBlocking is False, max seconds to wait for more data
before timing out; ignored when isBlocking is True.
:param eofOnTimeout: If True and we get a read timeout (isBlocking must be
False to get read timeouts), assume we've reached the end of the
input and produce the last aggregated record, if one can be
completed.
"""
def __init__(self, streamDef, bookmark=None, saveOutput=False,
isBlocking=True, maxTimeout=0, eofOnTimeout=False):
# Call superclass constructor
super(StreamReader, self).__init__()
loggerPrefix = 'com.numenta.nupic.data.StreamReader'
self._logger = logging.getLogger(loggerPrefix)
json_helpers.validate(streamDef,
schemaPath=pkg_resources.resource_filename(
jsonschema.__name__, "stream_def.json"))
assert len(streamDef['streams']) == 1, "Only 1 source stream is supported"
# Save constructor args
sourceDict = streamDef['streams'][0]
self._recordCount = 0
self._eofOnTimeout = eofOnTimeout
self._logger.debug('Reading stream with the def: %s', sourceDict)
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
# ---------------------------------------------------------------------
# Get the stream definition params
# Limiting window of the stream. It would not return any records until
# 'first_record' ID is read (or very first with the ID above that). The
# stream will return EOS once it reads record with ID 'last_record' or
# above (NOTE: the name 'lastRecord' is misleading because it is NOT
# inclusive).
firstRecordIdx = sourceDict.get('first_record', None)
self._sourceLastRecordIdx = sourceDict.get('last_record', None)
# If a bookmark was given, then override first_record from the stream
# definition.
if bookmark is not None:
firstRecordIdx = None
# Column names must be provided in the streamdef json
# Special case is ['*'], meaning all available names from the record stream
self._streamFieldNames = sourceDict.get('columns', None)
if self._streamFieldNames != None and self._streamFieldNames[0] == '*':
self._needFieldsFiltering = False
else:
self._needFieldsFiltering = True
# Types must be specified in streamdef json, or in case of the
# file_recod_stream types could be implicit from the file
streamFieldTypes = sourceDict.get('types', None)
self._logger.debug('Types from the def: %s', streamFieldTypes)
# Validate that all types are valid
if streamFieldTypes is not None:
for dataType in streamFieldTypes:
assert FieldMetaType.isValid(dataType)
# Reset, sequence and time fields might be provided by streamdef json
streamResetFieldName = streamDef.get('resetField', None)
streamTimeFieldName = streamDef.get('timeField', None)
streamSequenceFieldName = streamDef.get('sequenceIdField', None)
self._logger.debug('r, t, s fields: %s, %s, %s', streamResetFieldName,
streamTimeFieldName,
streamSequenceFieldName)
# =======================================================================
# Open up the underlying record store
dataUrl = sourceDict.get('source', None)
assert dataUrl is not None
self._recordStore = self._openStream(dataUrl, isBlocking, maxTimeout,
bookmark, firstRecordIdx)
assert self._recordStore is not None
# =======================================================================
# Prepare the data structures we need for returning just the fields
# the caller wants from each record
recordStoreFields = self._recordStore.getFields()
self._recordStoreFieldNames = self._recordStore.getFieldNames()
if not self._needFieldsFiltering:
self._streamFieldNames = self._recordStoreFieldNames
# Build up the field definitions for each field. This is a list of tuples
# of (name, type, special)
self._streamFields = []
for dstIdx, name in enumerate(self._streamFieldNames):
if name not in self._recordStoreFieldNames:
raise RuntimeError("The column '%s' from the stream definition "
"is not present in the underlying stream which has the following "
"columns: %s" % (name, self._recordStoreFieldNames))
fieldIdx = self._recordStoreFieldNames.index(name)
fieldType = recordStoreFields[fieldIdx].type
fieldSpecial = recordStoreFields[fieldIdx].special
# If the types or specials were defined in the stream definition,
# then override what was found in the record store
if streamFieldTypes is not None:
fieldType = streamFieldTypes[dstIdx]
if streamResetFieldName is not None and streamResetFieldName == name:
fieldSpecial = FieldMetaSpecial.reset
if streamTimeFieldName is not None and streamTimeFieldName == name:
fieldSpecial = FieldMetaSpecial.timestamp
if (streamSequenceFieldName is not None and
streamSequenceFieldName == name):
fieldSpecial = FieldMetaSpecial.sequence
self._streamFields.append(FieldMetaInfo(name, fieldType, fieldSpecial))
# ========================================================================
# Create the aggregator which will handle aggregation of records before
# returning them.
self._aggregator = Aggregator(
aggregationInfo=streamDef.get('aggregation', None),
inputFields=recordStoreFields,
timeFieldName=streamDef.get('timeField', None),
sequenceIdFieldName=streamDef.get('sequenceIdField', None),
resetFieldName=streamDef.get('resetField', None))
# We rely on the aggregator to tell us the bookmark of the last raw input
# that contributed to the aggregated record
self._aggBookmark = None
# Compute the aggregation period in terms of months and seconds
if 'aggregation' in streamDef:
self._aggMonthsAndSeconds = nupic.support.aggregationToMonthsSeconds(
streamDef.get('aggregation'))
else:
self._aggMonthsAndSeconds = None
# ========================================================================
# Are we saving the generated output to a csv?
if saveOutput:
tmpDir = tempfile.mkdtemp()
outFilename = os.path.join(tmpDir, "generated_output.csv")
self._logger.info("StreamReader: Saving generated records to: '%s'" %
outFilename)
self._writer = FileRecordStream(streamID=outFilename,
write=True,
fields=self._streamFields)
else:
self._writer = None
@staticmethod
def _openStream(dataUrl,
isBlocking, # pylint: disable=W0613
maxTimeout, # pylint: disable=W0613
bookmark,
firstRecordIdx):
"""Open the underlying file stream
This only supports 'file://' prefixed paths.
:returns: record stream instance
:rtype: FileRecordStream
"""
filePath = dataUrl[len(FILE_PREF):]
if not os.path.isabs(filePath):
filePath = os.path.join(os.getcwd(), filePath)
return FileRecordStream(streamID=filePath,
write=False,
bookmark=bookmark,
firstRecord=firstRecordIdx)
def close(self):
""" Close the stream
"""
return self._recordStore.close()
def getNextRecord(self):
""" Returns combined data from all sources (values only).
:returns: None on EOF; empty sequence on timeout.
"""
# Keep reading from the raw input till we get enough for an aggregated
# record
while True:
# Reached EOF due to lastRow constraint?
if self._sourceLastRecordIdx is not None and \
self._recordStore.getNextRecordIdx() >= self._sourceLastRecordIdx:
preAggValues = None # indicates EOF
bookmark = self._recordStore.getBookmark()
else:
# Get the raw record and bookmark
preAggValues = self._recordStore.getNextRecord()
bookmark = self._recordStore.getBookmark()
if preAggValues == (): # means timeout error occurred
if self._eofOnTimeout:
preAggValues = None # act as if we got EOF
else:
return preAggValues # Timeout indicator
self._logger.debug('Read source record #%d: %r',
self._recordStore.getNextRecordIdx()-1, preAggValues)
# Perform aggregation
(fieldValues, aggBookmark) = self._aggregator.next(preAggValues, bookmark)
# Update the aggregated record bookmark if we got a real record back
if fieldValues is not None:
self._aggBookmark = aggBookmark
# Reached EOF?
if preAggValues is None and fieldValues is None:
return None
# Return it if we have a record
if fieldValues is not None:
break
# Do we need to re-order the fields in the record?
if self._needFieldsFiltering:
values = []
srcDict = dict(list(zip(self._recordStoreFieldNames, fieldValues)))
for name in self._streamFieldNames:
values.append(srcDict[name])
fieldValues = values
# Write to debug output?
if self._writer is not None:
self._writer.appendRecord(fieldValues)
self._recordCount += 1
self._logger.debug('Returning aggregated record #%d from getNextRecord(): '
'%r. Bookmark: %r',
self._recordCount-1, fieldValues, self._aggBookmark)
return fieldValues
def getDataRowCount(self):
"""
Iterates through stream to calculate total records after aggregation.
This will alter the bookmark state.
"""
inputRowCountAfterAggregation = 0
while True:
record = self.getNextRecord()
if record is None:
return inputRowCountAfterAggregation
inputRowCountAfterAggregation += 1
if inputRowCountAfterAggregation > 10000:
raise RuntimeError('No end of datastream found.')
def getNextRecordIdx(self):
"""
:returns: the index of the record that will be read next from
:meth:`getNextRecord`.
"""
return self._recordCount
def recordsExistAfter(self, bookmark):
"""
:returns: True if there are records left after the bookmark.
"""
return self._recordStore.recordsExistAfter(bookmark)
def getAggregationMonthsAndSeconds(self):
""" Returns the aggregation period of the record stream as a dict
containing 'months' and 'seconds'. The months is always an integer and
seconds is a floating point. Only one is allowed to be non-zero at a
time.
Will return the aggregation period from this call. This call is
used by the :meth:`nupic.data.record_stream.RecordStream.getNextRecordDict`
method to assign a record number to a record given its timestamp and the
aggregation interval.
:returns: aggregationPeriod (as a dict) where:
- ``months``: number of months in aggregation period
- ``seconds``: number of seconds in aggregation period
(as a float)
"""
return self._aggMonthsAndSeconds
def appendRecord(self, record):
raise RuntimeError("Not implemented in StreamReader")
def appendRecords(self, records, progressCB=None):
raise RuntimeError("Not implemented in StreamReader")
def seekFromEnd(self, numRecords):
raise RuntimeError("Not implemented in StreamReader")
def getFieldNames(self):
"""
Returns all fields in all inputs (list of plain names).
.. note:: currently, only one input is supported
"""
return [f.name for f in self._streamFields]
def getFields(self):
"""
:returns: a sequence of :class:`nupic.data.fieldmeta.FieldMetaInfo` for each
field in the stream.
"""
return self._streamFields
def getBookmark(self):
"""
:returns: a bookmark to the current position
"""
return self._aggBookmark
def clearStats(self):
""" Resets stats collected so far.
"""
self._recordStore.clearStats()
def getStats(self):
"""
TODO: This method needs to be enhanced to get the stats on the *aggregated*
records.
:returns: stats (like min and max values of the fields).
"""
# The record store returns a dict of stats, each value in this dict is
# a list with one item per field of the record store
# {
# 'min' : [f1_min, f2_min, f3_min],
# 'max' : [f1_max, f2_max, f3_max]
# }
recordStoreStats = self._recordStore.getStats()
# We need to convert each item to represent the fields of the *stream*
streamStats = dict()
for (key, values) in list(recordStoreStats.items()):
fieldStats = dict(list(zip(self._recordStoreFieldNames, values)))
streamValues = []
for name in self._streamFieldNames:
streamValues.append(fieldStats[name])
streamStats[key] = streamValues
return streamStats
def getError(self):
"""
:returns: errors saved in the stream.
"""
return self._recordStore.getError()
def setError(self, error):
""" Saves specified error in the stream.
:param error: to save
"""
self._recordStore.setError(error)
def isCompleted(self):
"""
:returns: True if all records have been read.
"""
return self._recordStore.isCompleted()
def setCompleted(self, completed=True):
"""
Marks the stream completed (True or False)
:param completed: (bool) is completed or not
"""
# CSV file is always considered completed, nothing to do
self._recordStore.setCompleted(completed)
def setTimeout(self, timeout):
"""Set the read timeout.
:param timeout: (float or int) timeout length
"""
self._recordStore.setTimeout(timeout)
def flush(self):
raise RuntimeError("Not implemented in StreamReader")