/usr/share/pyshared/neo/io/plexonio.py is in python-neo 0.2.0-1.
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"""
Class for reading data from Plexion acquisition system (.plx)
Compatible with versions 100 to 106.
Other versions have not been tested.
This IO is developed thanks to the header file downloadable from:
http://www.plexon.com/downloads.html
Depend on:
Supported : Read
Author: sgarcia
"""
from .baseio import BaseIO
from ..core import *
from .tools import create_many_to_one_relationship, iteritems
import numpy as np
import quantities as pq
import struct
import datetime
import os
class PlexonIO(BaseIO):
"""
Class for reading plx file.
Usage:
>>> from neo import io
>>> r = io.PlexonIO(filename='File_plexon_1.plx')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals
[]
>>> print seg.spiketrains # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<SpikeTrain(array([ 2.75000000e-02, 5.68250000e-02, 8.52500000e-02, ...,
...
>>> print seg.eventarrays
[]
"""
is_readable = True
is_writable = False
supported_objects = [Segment , AnalogSignal, SpikeTrain, EventArray, EpochArray]
readable_objects = [ Segment]
writeable_objects = []
has_header = False
is_streameable = False
# This is for GUI stuf : a definition for parameters when reading.
read_params = {
Segment : [
('load_spike_waveform' , { 'value' : False } ) ,
]
}
write_params = None
name = 'Plexon'
extensions = [ 'plx' ]
mode = 'file'
def __init__(self , filename = None) :
"""
This class read a plx file.
Arguments:
filename : the filename
"""
BaseIO.__init__(self)
self.filename = filename
def read_segment(self,
lazy = False,
cascade = True,
load_spike_waveform = False,
):
"""
"""
fid = open(self.filename, 'rb')
globalHeader = HeaderReader(fid , GlobalHeader ).read_f(offset = 0)
# metadatas
seg = Segment()
seg.rec_datetime = datetime.datetime( globalHeader['Year'] , globalHeader['Month'] , globalHeader['Day'] ,
globalHeader['Hour'] , globalHeader['Minute'] , globalHeader['Second'] )
seg.file_origin = os.path.basename(self.filename)
seg.annotate(plexon_version = globalHeader['Version'])
if not cascade:
return seg
## Step 1 : read headers
# dsp channels heade
dspChannelHeaders = { }
maxunit=0
maxchan = 0
for i in xrange(globalHeader['NumDSPChannels']):
# channel is 1 based
channelHeader = HeaderReader(fid , ChannelHeader ).read_f(offset = None)
channelHeader['Template'] = np.array(channelHeader['Template']).reshape((5,64))
channelHeader['Boxes'] = np.array(channelHeader['Boxes']).reshape((5,2,4))
dspChannelHeaders[channelHeader['Channel']]=channelHeader
maxunit = max(channelHeader['NUnits'],maxunit)
maxchan = max(channelHeader['Channel'],maxchan)
# event channel header
eventHeaders = { }
for i in xrange(globalHeader['NumEventChannels']):
eventHeader = HeaderReader(fid , EventHeader ).read_f(offset = None)
eventHeaders[eventHeader['Channel']] = eventHeader
# slow channel header
slowChannelHeaders = { }
for i in xrange(globalHeader['NumSlowChannels']):
slowChannelHeader = HeaderReader(fid , SlowChannelHeader ).read_f(offset = None)
slowChannelHeaders[slowChannelHeader['Channel']] = slowChannelHeader
## Step 2 : prepare allocating
# for allocating continuous signal
ncontinuoussamples = np.zeros(len(slowChannelHeaders))
sampleposition = np.zeros(len(slowChannelHeaders))
anaSigs = { }
# for allocating spiketimes and waveform
spiketrains = { }
nspikecounts = np.zeros((maxchan+1, maxunit+1) ,dtype='i')
for i,channelHeader in iteritems(dspChannelHeaders):
spiketrains[i] = { }
# for allocating EventArray
eventarrays = { }
neventsperchannel = { }
#maxstrsizeperchannel = { }
for chan, h in iteritems(eventHeaders):
neventsperchannel[chan] = 0
#maxstrsizeperchannel[chan] = 0
## Step 3 : a first loop for counting size
start = fid.tell()
while fid.tell() !=-1 :
# read block header
dataBlockHeader = HeaderReader(fid , DataBlockHeader ).read_f(offset = None)
if dataBlockHeader is None : break
chan = dataBlockHeader['Channel']
unit = dataBlockHeader['Unit']
n1,n2 = dataBlockHeader['NumberOfWaveforms'] , dataBlockHeader['NumberOfWordsInWaveform']
if dataBlockHeader['Type'] == 1:
#spike
if unit not in spiketrains[chan]:
sptr = SpikeTrain([ ], units='s', t_stop=0.0)
sptr.annotate(unit_name = dspChannelHeaders[chan]['Name'])
sptr.annotate(channel_index = i)
spiketrains[chan][unit] = sptr
spiketrains[chan][unit].sizeOfWaveform = n1,n2
nspikecounts[chan,unit] +=1
fid.seek(n1*n2*2,1)
elif dataBlockHeader['Type'] ==4:
#event
neventsperchannel[chan] += 1
if chan not in eventarrays:
ea = EventArray()
ea.annotate(channel_name= eventHeaders[chan]['Name'])
ea.annotate(channel_index = chan)
eventarrays[chan] = ea
elif dataBlockHeader['Type'] == 5:
#continuous signal
fid.seek(n2*2, 1)
if n2> 0:
ncontinuoussamples[chan] += n2
if chan not in anaSigs:
anasig = AnalogSignal(
[ ],
units = 'V',
sampling_rate = float(slowChannelHeaders[chan]['ADFreq'])*pq.Hz,
t_start = 0.*pq.s,
)
anasig.annotate(channel_index = slowChannelHeaders[chan]['Channel'])
anasig.annotate(channel_name = slowChannelHeaders[chan]['Name'])
anaSigs[chan] = anasig
if lazy:
for chan, anaSig in iteritems(anaSigs):
anaSigs[chan].lazy_shape = ncontinuoussamples[chan]
for chan, sptrs in iteritems(spiketrains):
for unit, sptr in iteritems(sptrs):
spiketrains[chan][unit].lazy_shape = nspikecounts[chan][unit]
for chan, ea in iteritems(eventarrays):
ea.lazy_shape = neventsperchannel[chan]
else:
## Step 4: allocating memory if not lazy
# continuous signal
for chan, anaSig in iteritems(anaSigs):
anaSigs[chan] = anaSig.duplicate_with_new_array(np.zeros((ncontinuoussamples[chan]) , dtype = 'f4')*pq.V, )
# allocating mem for SpikeTrain
for chan, sptrs in iteritems(spiketrains):
for unit, sptr in iteritems(sptrs):
new = SpikeTrain(np.zeros((nspikecounts[chan][unit]), dtype='f')*pq.s, t_stop=1e99) # use an enormous value for t_stop for now, put in correct value later
new.annotations.update(sptr.annotations)
if load_spike_waveform:
n1, n2 = spiketrains[chan][unit].sizeOfWaveform
new.waveforms = np.zeros( (nspikecounts[chan][unit], n1, n2 )*pq.V , dtype = 'f' ) * pq.V
spiketrains[chan][unit] = new
nspikecounts[:] = 0
# event
eventpositions = { }
for chan, ea in iteritems(eventarrays):
ea.times = np.zeros( neventsperchannel[chan] )*pq.s
#ea.labels = zeros( neventsperchannel[chan] , dtype = 'S'+str(neventsperchannel[chan]) )
eventpositions[chan]=0
if not lazy:
## Step 5 : a second loop for reading if not lazy
fid.seek(start)
while fid.tell() !=-1 :
dataBlockHeader = HeaderReader(fid , DataBlockHeader ).read_f(offset = None)
if dataBlockHeader is None : break
chan = dataBlockHeader['Channel']
n1,n2 = dataBlockHeader['NumberOfWaveforms'] , dataBlockHeader['NumberOfWordsInWaveform']
time = dataBlockHeader['UpperByteOf5ByteTimestamp']*2.**32 + dataBlockHeader['TimeStamp']
time/= globalHeader['ADFrequency']
if n2 <0: break
if dataBlockHeader['Type'] == 1:
#spike
unit = dataBlockHeader['Unit']
sptr = spiketrains[chan][unit]
pos = nspikecounts[chan,unit]
sptr[pos] = time * pq.s
if load_spike_waveform and n1*n2 != 0 :
waveform = fromstring( fid.read(n1*n2*2) , dtype = 'i2').reshape(n1,n2).astype('f')
#range
if globalHeader['Version'] <103:
waveform = waveform*3000./(2048*dspChannelHeaders[chan]['Gain']*1000.)
elif globalHeader['Version'] >=103 and globalHeader['Version'] <105:
waveform = waveform*globalHeader['SpikeMaxMagnitudeMV']/(.5*2.**(globalHeader['BitsPerSpikeSample'])*1000.)
elif globalHeader['Version'] >105:
waveform = waveform*globalHeader['SpikeMaxMagnitudeMV']/(.5*2.**(globalHeader['BitsPerSpikeSample'])*globalHeader['SpikePreAmpGain'])
sptr._waveforms[pos,:,:] = waveform
else:
fid.seek(n1*n2*2,1)
nspikecounts[chan,unit] +=1
elif dataBlockHeader['Type'] == 4:
# event
pos = eventpositions[chan]
eventarrays[chan].times[pos] = time * pq.s
eventpositions[chan]+= 1
elif dataBlockHeader['Type'] == 5:
#signal
data = np.fromstring( fid.read(n2*2) , dtype = 'i2').astype('f4')
#range
if globalHeader['Version'] ==100 or globalHeader['Version'] ==101 :
data = data*5000./(2048*slowChannelHeaders[chan]['Gain']*1000.)
elif globalHeader['Version'] ==102 :
data = data*5000./(2048*slowChannelHeaders[chan]['Gain']*slowChannelHeaders[chan]['PreampGain'])
elif globalHeader['Version'] >= 103:
data = data*globalHeader['SlowMaxMagnitudeMV']/(.5*(2**globalHeader['BitsPerSpikeSample'])*\
slowChannelHeaders[chan]['Gain']*slowChannelHeaders[chan]['PreampGain'])
anaSigs[chan][sampleposition[chan] : sampleposition[chan]+data.size] = data * pq.V
sampleposition[chan] += data.size
if sampleposition[chan] ==0:
anaSigs[chan].t_start = time* pq.s
#TODO if lazy
# add AnalogSignal to sgement
for k,anaSig in iteritems(anaSigs) :
if anaSig is not None:
seg.analogsignals.append(anaSig)
# add SpikeTrain to sgement
for chan, sptrs in iteritems(spiketrains):
for unit, sptr in iteritems(sptrs):
if len(sptr) > 0:
sptr.t_stop = sptr.max() # can probably get a better value for this, from the associated AnalogSignal
seg.spiketrains.append(sptr)
# add eventarray to segment
for chan,ea in iteritems(eventarrays):
seg.eventarrays.append(ea)
create_many_to_one_relationship(seg)
return seg
GlobalHeader = [
('MagicNumber' , 'I'),
('Version','i'),
('Comment','128s'),
('ADFrequency','i'),
('NumDSPChannels','i'),
('NumEventChannels','i'),
('NumSlowChannels','i'),
('NumPointsWave','i'),
('NumPointsPreThr','i'),
('Year','i'),
('Month','i'),
('Day','i'),
('Hour','i'),
('Minute','i'),
('Second','i'),
('FastRead','i'),
('WaveformFreq','i'),
('LastTimestamp','d'),
#version >103
('Trodalness' , 'b'),
('DataTrodalness' , 'b'),
('BitsPerSpikeSample' , 'b'),
('BitsPerSlowSample' , 'b'),
('SpikeMaxMagnitudeMV' , 'H'),
('SlowMaxMagnitudeMV' , 'H'),
#version 105
('SpikePreAmpGain' , 'H'),
#version 106
('AcquiringSoftware','18s'),
('ProcessingSoftware','18s'),
('Padding','10s'),
# all version
('TSCounts','650i'),
('WFCounts','650i'),
('EVCounts','512i'),
]
ChannelHeader = [
('Name' , '32s'),
('SIGName','32s'),
('Channel','i'),
('WFRate','i'),
('SIG','i'),
('Ref','i'),
('Gain','i'),
('Filter','i'),
('Threshold','i'),
('Method','i'),
('NUnits','i'),
('Template','320h'),
('Fit','5i'),
('SortWidth','i'),
('Boxes','40h'),
('SortBeg','i'),
#version 105
('Comment','128s'),
#version 106
('SrcId','b'),
('reserved','b'),
('ChanId','H'),
('Padding','10i'),
]
EventHeader = [
('Name' , '32s'),
('Channel','i'),
#version 105
('Comment' , '128s'),
#version 106
('SrcId','b'),
('reserved','b'),
('ChanId','H'),
('Padding','32i'),
]
SlowChannelHeader = [
('Name' , '32s'),
('Channel','i'),
('ADFreq','i'),
('Gain','i'),
('Enabled','i'),
('PreampGain','i'),
#version 104
('SpikeChannel','i'),
#version 105
('Comment','128s'),
#version 106
('SrcId','b'),
('reserved','b'),
('ChanId','H'),
('Padding','27i'),
]
DataBlockHeader = [
('Type','h'),
('UpperByteOf5ByteTimestamp','h'),
('TimeStamp','i'),
('Channel','h'),
('Unit','h'),
('NumberOfWaveforms','h'),
('NumberOfWordsInWaveform','h'),
]# 16 bytes
class HeaderReader():
def __init__(self,fid ,description ):
self.fid = fid
self.description = description
def read_f(self, offset =None):
if offset is not None :
self.fid.seek(offset)
d = { }
for key, format in self.description :
buf = self.fid.read(struct.calcsize(format))
if len(buf) != struct.calcsize(format) : return None
val = struct.unpack(format , buf)
if len(val) == 1:
val = val[0]
else :
val = list(val)
if 's' in format :
val = val.replace('\x00','')
d[key] = val
return d
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