def readSortedRecord(self, fid, pointer, channelList=None):
""" reads record, only one channel group per datagroup
Parameters
----------------
fid : float
file identifier
pointer
position in file of data block beginning
channelList : list of str, optional
list of channel to read
Returns
-----------
rec : numpy recarray
contains a matrix of raw data in a recarray (attributes corresponding to channel name)
Notes
--------
If channelList is None, read data using numpy.core.records.fromfile that is rather quick.
However, in case of large file, you can use channelList to load only interesting channels or
only one channel on demand, but be aware it might be much slower.
"""
fid.seek(pointer)
if channelList is None: # reads all, quickest but memory consuming
return fromfile(fid, dtype=self.numpyDataRecordFormat, shape=self.numberOfRecords, names=self.dataRecordName)
else: # reads only some channels from a sorted data block
# memory efficient but takes time
if len(list(set(channelList) & set(self.channelNames))) > 0: # are channelList in this dataGroup
# check if master channel is in the list
if not self.master['name'] in channelList:
channelList.append(self.master['name']) # adds master channel
rec = {}
recChan = []
numpyDataRecordFormat = []
for channel in channelList: # initialise data structure
rec[channel] = 0
for channel in self: # list of recordChannels from channelList
if channel.name in channelList:
recChan.append(channel)
numpyDataRecordFormat.append(channel.RecordFormat)
rec = zeros((self.numberOfRecords, ), dtype=numpyDataRecordFormat)
recordLength = self.recordIDsize + self.recordLength
for r in range(self.numberOfRecords): # for each record,
buf = fid.read(recordLength)
for channel in recChan:
rec[channel.name][r] = channel.CFormat.unpack(buf[channel.posBeg:channel.posEnd])[0]
return rec.view(recarray)
def readSortedRecord(self, fid, pointer, channelList=None):
""" reads record, only one channel group per datagroup
Parameters
----------------
fid : float
file identifier
pointer
position in file of data block beginning
channelList : list of str, optional
list of channel to read
Returns
-----------
rec : numpy recarray
contains a matrix of raw data in a recarray (attributes corresponding to channel name)
Notes
--------
If channelList is None, read data using numpy.core.records.fromfile that is rather quick.
However, in case of large file, you can use channelList to load only interesting channels or
only one channel on demand, but be aware it might be much slower.
"""
fid.seek(pointer)
if channelList is None: # reads all, quickest but memory consuming
return fromfile(fid, dtype=self.numpyDataRecordFormat, shape=self.numberOfRecords, names=self.dataRecordName)
else: # reads only some channels from a sorted data block
# memory efficient but takes time
if len(list(set(channelList) & set(self.channelNames))) > 0: # are channelList in this dataGroup
# check if master channel is in the list
if not self.master['name'] in channelList:
channelList.append(self.master['name']) # adds master channel
rec = {}
recChan = []
numpyDataRecordFormat = []
for channel in channelList: # initialise data structure
rec[channel] = 0
for channel in self: # list of recordChannels from channelList
if channel.name in channelList:
recChan.append(channel)
numpyDataRecordFormat.append(channel.RecordFormat)
rec = zeros((self.numberOfRecords, ), dtype=numpyDataRecordFormat)
recordLength = self.recordIDsize + self.recordLength
for r in range(self.numberOfRecords): # for each record,
buf = fid.read(recordLength)
for channel in recChan:
rec[channel.name][r] = channel.CFormat.unpack(buf[channel.posBeg:channel.posEnd])[0]
return rec.view(recarray)
def header(self):
from cPickle import load, dump
if not hasattr(self, '_header'):
h = fromfile(self.tsq, tsq_dtype)
self._header = h[h['type'] == 0x8101] # keep EVTYPE_STREAM only
return self._header
