def read_segment(self, import_neuroshare_segment = True, lazy=False, cascade=True): """ Arguments: import_neuroshare_segment: import neuroshare segment as SpikeTrain with associated waveforms or not imported at all. """ seg = Segment( file_origin = os.path.basename(self.filename), ) if sys.platform.startswith('win'): neuroshare = ctypes.windll.LoadLibrary(self.dllname) elif sys.platform.startswith('linux'): neuroshare = ctypes.cdll.LoadLibrary(self.dllname) neuroshare = DllWithError(neuroshare) #elif sys.platform.startswith('darwin'): # API version info = ns_LIBRARYINFO() neuroshare.ns_GetLibraryInfo(ctypes.byref(info) , ctypes.sizeof(info)) seg.annotate(neuroshare_version = str(info.dwAPIVersionMaj)+'.'+str(info.dwAPIVersionMin)) if not cascade: return seg # open file hFile = ctypes.c_uint32(0) neuroshare.ns_OpenFile(ctypes.c_char_p(self.filename) ,ctypes.byref(hFile)) fileinfo = ns_FILEINFO() neuroshare.ns_GetFileInfo(hFile, ctypes.byref(fileinfo) , ctypes.sizeof(fileinfo)) # read all entities for dwEntityID in range(fileinfo.dwEntityCount): entityInfo = ns_ENTITYINFO() neuroshare.ns_GetEntityInfo( hFile, dwEntityID, ctypes.byref(entityInfo), ctypes.sizeof(entityInfo)) # EVENT if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_EVENT': pEventInfo = ns_EVENTINFO() neuroshare.ns_GetEventInfo ( hFile, dwEntityID, ctypes.byref(pEventInfo), ctypes.sizeof(pEventInfo)) if pEventInfo.dwEventType == 0: #TEXT pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 1:#CVS pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 2:# 8bit pData = ctypes.c_byte(0) elif pEventInfo.dwEventType == 3:# 16bit pData = ctypes.c_int16(0) elif pEventInfo.dwEventType == 4:# 32bit pData = ctypes.c_int32(0) pdTimeStamp = ctypes.c_double(0.) pdwDataRetSize = ctypes.c_uint32(0) ea = Event(name = str(entityInfo.szEntityLabel),) if not lazy: times = [ ] labels = [ ] for dwIndex in range(entityInfo.dwItemCount ): neuroshare.ns_GetEventData ( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), ctypes.byref(pData), ctypes.sizeof(pData), ctypes.byref(pdwDataRetSize) ) times.append(pdTimeStamp.value) labels.append(str(pData.value)) ea.times = times*pq.s ea.labels = np.array(labels, dtype ='S') else : ea.lazy_shape = entityInfo.dwItemCount seg.eventarrays.append(ea) # analog if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_ANALOG': pAnalogInfo = ns_ANALOGINFO() neuroshare.ns_GetAnalogInfo( hFile, dwEntityID,ctypes.byref(pAnalogInfo),ctypes.sizeof(pAnalogInfo) ) dwIndexCount = entityInfo.dwItemCount if lazy: signal = [ ]*pq.Quantity(1, pAnalogInfo.szUnits) else: pdwContCount = ctypes.c_uint32(0) pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'float64') total_read = 0 while total_read< entityInfo.dwItemCount: dwStartIndex = ctypes.c_uint32(total_read) dwStopIndex = ctypes.c_uint32(entityInfo.dwItemCount - total_read) neuroshare.ns_GetAnalogData( hFile, dwEntityID, dwStartIndex, dwStopIndex, ctypes.byref( pdwContCount) , pData[total_read:].ctypes.data_as(ctypes.POINTER(ctypes.c_double))) total_read += pdwContCount.value signal = pq.Quantity(pData, units=pAnalogInfo.szUnits, copy = False) #t_start dwIndex = 0 pdTime = ctypes.c_double(0) neuroshare.ns_GetTimeByIndex( hFile, dwEntityID, dwIndex, ctypes.byref(pdTime)) anaSig = AnalogSignal(signal, sampling_rate = pAnalogInfo.dSampleRate*pq.Hz, t_start = pdTime.value * pq.s, name = str(entityInfo.szEntityLabel), ) anaSig.annotate( probe_info = str(pAnalogInfo.szProbeInfo)) if lazy: anaSig.lazy_shape = entityInfo.dwItemCount seg.analogsignals.append( anaSig ) #segment if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_SEGMENT' and import_neuroshare_segment: pdwSegmentInfo = ns_SEGMENTINFO() if not str(entityInfo.szEntityLabel).startswith('spks'): continue neuroshare.ns_GetSegmentInfo( hFile, dwEntityID, ctypes.byref(pdwSegmentInfo), ctypes.sizeof(pdwSegmentInfo) ) nsource = pdwSegmentInfo.dwSourceCount pszMsgBuffer = ctypes.create_string_buffer(" "*256) neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256) for dwSourceID in range(pdwSegmentInfo.dwSourceCount) : pSourceInfo = ns_SEGSOURCEINFO() neuroshare.ns_GetSegmentSourceInfo( hFile, dwEntityID, dwSourceID, ctypes.byref(pSourceInfo), ctypes.sizeof(pSourceInfo) ) if lazy: sptr = SpikeTrain(times, name = str(entityInfo.szEntityLabel), t_stop = 0.*pq.s) sptr.lazy_shape = entityInfo.dwItemCount else: pdTimeStamp = ctypes.c_double(0.) dwDataBufferSize = pdwSegmentInfo.dwMaxSampleCount*pdwSegmentInfo.dwSourceCount pData = np.zeros( (dwDataBufferSize), dtype = 'float64') pdwSampleCount = ctypes.c_uint32(0) pdwUnitID= ctypes.c_uint32(0) nsample = int(dwDataBufferSize) times = np.empty( (entityInfo.dwItemCount), dtype = 'f') waveforms = np.empty( (entityInfo.dwItemCount, nsource, nsample), dtype = 'f') for dwIndex in range(entityInfo.dwItemCount ): neuroshare.ns_GetSegmentData ( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), dwDataBufferSize * 8, ctypes.byref(pdwSampleCount), ctypes.byref(pdwUnitID ) ) times[dwIndex] = pdTimeStamp.value waveforms[dwIndex, :,:] = pData[:nsample*nsource].reshape(nsample ,nsource).transpose() sptr = SpikeTrain(times = pq.Quantity(times, units = 's', copy = False), t_stop = times.max(), waveforms = pq.Quantity(waveforms, units = str(pdwSegmentInfo.szUnits), copy = False ), left_sweep = nsample/2./float(pdwSegmentInfo.dSampleRate)*pq.s, sampling_rate = float(pdwSegmentInfo.dSampleRate)*pq.Hz, name = str(entityInfo.szEntityLabel), ) seg.spiketrains.append(sptr) # neuralevent if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_NEURALEVENT': pNeuralInfo = ns_NEURALINFO() neuroshare.ns_GetNeuralInfo ( hFile, dwEntityID, ctypes.byref(pNeuralInfo), ctypes.sizeof(pNeuralInfo)) if lazy: times = [ ]*pq.s t_stop = 0*pq.s else: pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'float64') dwStartIndex = 0 dwIndexCount = entityInfo.dwItemCount neuroshare.ns_GetNeuralData( hFile, dwEntityID, dwStartIndex, dwIndexCount, pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double))) times = pData*pq.s t_stop = times.max() sptr = SpikeTrain(times, t_stop =t_stop, name = str(entityInfo.szEntityLabel),) if lazy: sptr.lazy_shape = entityInfo.dwItemCount seg.spiketrains.append(sptr) # close neuroshare.ns_CloseFile(hFile) seg.create_many_to_one_relationship() return seg
def read_segment(self, import_neuroshare_segment=True, lazy=False, cascade=True): """ Arguments: import_neuroshare_segment: import neuroshare segment as SpikeTrain with associated waveforms or not imported at all. """ seg = Segment(file_origin=os.path.basename(self.filename), ) if sys.platform.startswith('win'): neuroshare = ctypes.windll.LoadLibrary(self.dllname) elif sys.platform.startswith('linux'): neuroshare = ctypes.cdll.LoadLibrary(self.dllname) neuroshare = DllWithError(neuroshare) #elif sys.platform.startswith('darwin'): # API version info = ns_LIBRARYINFO() neuroshare.ns_GetLibraryInfo(ctypes.byref(info), ctypes.sizeof(info)) seg.annotate(neuroshare_version=str(info.dwAPIVersionMaj) + '.' + str(info.dwAPIVersionMin)) if not cascade: return seg # open file hFile = ctypes.c_uint32(0) neuroshare.ns_OpenFile(ctypes.c_char_p(self.filename), ctypes.byref(hFile)) fileinfo = ns_FILEINFO() neuroshare.ns_GetFileInfo(hFile, ctypes.byref(fileinfo), ctypes.sizeof(fileinfo)) # read all entities for dwEntityID in range(fileinfo.dwEntityCount): entityInfo = ns_ENTITYINFO() neuroshare.ns_GetEntityInfo(hFile, dwEntityID, ctypes.byref(entityInfo), ctypes.sizeof(entityInfo)) # EVENT if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_EVENT': pEventInfo = ns_EVENTINFO() neuroshare.ns_GetEventInfo(hFile, dwEntityID, ctypes.byref(pEventInfo), ctypes.sizeof(pEventInfo)) if pEventInfo.dwEventType == 0: #TEXT pData = ctypes.create_string_buffer( pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 1: #CVS pData = ctypes.create_string_buffer( pEventInfo.dwMaxDataLength) elif pEventInfo.dwEventType == 2: # 8bit pData = ctypes.c_byte(0) elif pEventInfo.dwEventType == 3: # 16bit pData = ctypes.c_int16(0) elif pEventInfo.dwEventType == 4: # 32bit pData = ctypes.c_int32(0) pdTimeStamp = ctypes.c_double(0.) pdwDataRetSize = ctypes.c_uint32(0) ea = Event(name=str(entityInfo.szEntityLabel), ) if not lazy: times = [] labels = [] for dwIndex in range(entityInfo.dwItemCount): neuroshare.ns_GetEventData( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), ctypes.byref(pData), ctypes.sizeof(pData), ctypes.byref(pdwDataRetSize)) times.append(pdTimeStamp.value) labels.append(str(pData.value)) ea.times = times * pq.s ea.labels = np.array(labels, dtype='S') else: ea.lazy_shape = entityInfo.dwItemCount seg.eventarrays.append(ea) # analog if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_ANALOG': pAnalogInfo = ns_ANALOGINFO() neuroshare.ns_GetAnalogInfo(hFile, dwEntityID, ctypes.byref(pAnalogInfo), ctypes.sizeof(pAnalogInfo)) dwIndexCount = entityInfo.dwItemCount if lazy: signal = [] * pq.Quantity(1, pAnalogInfo.szUnits) else: pdwContCount = ctypes.c_uint32(0) pData = np.zeros((entityInfo.dwItemCount, ), dtype='float64') total_read = 0 while total_read < entityInfo.dwItemCount: dwStartIndex = ctypes.c_uint32(total_read) dwStopIndex = ctypes.c_uint32(entityInfo.dwItemCount - total_read) neuroshare.ns_GetAnalogData( hFile, dwEntityID, dwStartIndex, dwStopIndex, ctypes.byref(pdwContCount), pData[total_read:].ctypes.data_as( ctypes.POINTER(ctypes.c_double))) total_read += pdwContCount.value signal = pq.Quantity(pData, units=pAnalogInfo.szUnits, copy=False) #t_start dwIndex = 0 pdTime = ctypes.c_double(0) neuroshare.ns_GetTimeByIndex(hFile, dwEntityID, dwIndex, ctypes.byref(pdTime)) anaSig = AnalogSignal( signal, sampling_rate=pAnalogInfo.dSampleRate * pq.Hz, t_start=pdTime.value * pq.s, name=str(entityInfo.szEntityLabel), ) anaSig.annotate(probe_info=str(pAnalogInfo.szProbeInfo)) if lazy: anaSig.lazy_shape = entityInfo.dwItemCount seg.analogsignals.append(anaSig) #segment if entity_types[ entityInfo. dwEntityType] == 'ns_ENTITY_SEGMENT' and import_neuroshare_segment: pdwSegmentInfo = ns_SEGMENTINFO() if not str(entityInfo.szEntityLabel).startswith('spks'): continue neuroshare.ns_GetSegmentInfo(hFile, dwEntityID, ctypes.byref(pdwSegmentInfo), ctypes.sizeof(pdwSegmentInfo)) nsource = pdwSegmentInfo.dwSourceCount pszMsgBuffer = ctypes.create_string_buffer(" " * 256) neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256) for dwSourceID in range(pdwSegmentInfo.dwSourceCount): pSourceInfo = ns_SEGSOURCEINFO() neuroshare.ns_GetSegmentSourceInfo( hFile, dwEntityID, dwSourceID, ctypes.byref(pSourceInfo), ctypes.sizeof(pSourceInfo)) if lazy: sptr = SpikeTrain(times, name=str(entityInfo.szEntityLabel), t_stop=0. * pq.s) sptr.lazy_shape = entityInfo.dwItemCount else: pdTimeStamp = ctypes.c_double(0.) dwDataBufferSize = pdwSegmentInfo.dwMaxSampleCount * pdwSegmentInfo.dwSourceCount pData = np.zeros((dwDataBufferSize), dtype='float64') pdwSampleCount = ctypes.c_uint32(0) pdwUnitID = ctypes.c_uint32(0) nsample = int(dwDataBufferSize) times = np.empty((entityInfo.dwItemCount), dtype='f') waveforms = np.empty( (entityInfo.dwItemCount, nsource, nsample), dtype='f') for dwIndex in range(entityInfo.dwItemCount): neuroshare.ns_GetSegmentData( hFile, dwEntityID, dwIndex, ctypes.byref(pdTimeStamp), pData.ctypes.data_as( ctypes.POINTER(ctypes.c_double)), dwDataBufferSize * 8, ctypes.byref(pdwSampleCount), ctypes.byref(pdwUnitID)) times[dwIndex] = pdTimeStamp.value waveforms[ dwIndex, :, :] = pData[:nsample * nsource].reshape( nsample, nsource).transpose() sptr = SpikeTrain( times=pq.Quantity(times, units='s', copy=False), t_stop=times.max(), waveforms=pq.Quantity(waveforms, units=str( pdwSegmentInfo.szUnits), copy=False), left_sweep=nsample / 2. / float(pdwSegmentInfo.dSampleRate) * pq.s, sampling_rate=float(pdwSegmentInfo.dSampleRate) * pq.Hz, name=str(entityInfo.szEntityLabel), ) seg.spiketrains.append(sptr) # neuralevent if entity_types[ entityInfo.dwEntityType] == 'ns_ENTITY_NEURALEVENT': pNeuralInfo = ns_NEURALINFO() neuroshare.ns_GetNeuralInfo(hFile, dwEntityID, ctypes.byref(pNeuralInfo), ctypes.sizeof(pNeuralInfo)) if lazy: times = [] * pq.s t_stop = 0 * pq.s else: pData = np.zeros((entityInfo.dwItemCount, ), dtype='float64') dwStartIndex = 0 dwIndexCount = entityInfo.dwItemCount neuroshare.ns_GetNeuralData( hFile, dwEntityID, dwStartIndex, dwIndexCount, pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double))) times = pData * pq.s t_stop = times.max() sptr = SpikeTrain( times, t_stop=t_stop, name=str(entityInfo.szEntityLabel), ) if lazy: sptr.lazy_shape = entityInfo.dwItemCount seg.spiketrains.append(sptr) # close neuroshare.ns_CloseFile(hFile) seg.create_many_to_one_relationship() return seg
def read_one_channel_event_or_spike(self, fid, channel_num, header, lazy=True): # return SPikeTrain or Event channelHeader = header.channelHeaders[channel_num] if channelHeader.firstblock < 0: return if channelHeader.kind not in [2, 3, 4, 5, 6, 7, 8]: return # # Step 1 : type of blocks if channelHeader.kind in [2, 3, 4]: # Event data fmt = [('tick', 'i4')] elif channelHeader.kind in [5]: # Marker data fmt = [('tick', 'i4'), ('marker', 'i4')] elif channelHeader.kind in [6]: # AdcMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('adc', 'S%d' % channelHeader.n_extra)] elif channelHeader.kind in [7]: # RealMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('real', 'S%d' % channelHeader.n_extra)] elif channelHeader.kind in [8]: # TextMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('label', 'S%d' % channelHeader.n_extra)] dt = np.dtype(fmt) ## Step 2 : first read for allocating mem fid.seek(channelHeader.firstblock) totalitems = 0 for _ in range(channelHeader.blocks): blockHeader = HeaderReader(fid, np.dtype(blockHeaderDesciption)) totalitems += blockHeader.items if blockHeader.succ_block > 0: fid.seek(blockHeader.succ_block) #~ print 'totalitems' , totalitems if lazy: if channelHeader.kind in [2, 3, 4, 5, 8]: ea = Event() ea.annotate(channel_index=channel_num) ea.lazy_shape = totalitems return ea elif channelHeader.kind in [6, 7]: # correct value for t_stop to be put in later sptr = SpikeTrain([] * pq.s, t_stop=1e99) sptr.annotate(channel_index=channel_num, ced_unit = 0) sptr.lazy_shape = totalitems return sptr else: alltrigs = np.zeros(totalitems, dtype=dt) ## Step 3 : read fid.seek(channelHeader.firstblock) pos = 0 for _ in range(channelHeader.blocks): blockHeader = HeaderReader( fid, np.dtype(blockHeaderDesciption)) # read all events in block trigs = np.fromstring( fid.read(blockHeader.items * dt.itemsize), dtype=dt) alltrigs[pos:pos + trigs.size] = trigs pos += trigs.size if blockHeader.succ_block > 0: fid.seek(blockHeader.succ_block) ## Step 3 convert in neo standard class: eventarrays or spiketrains alltimes = alltrigs['tick'].astype( 'f') * header.us_per_time * header.dtime_base * pq.s if channelHeader.kind in [2, 3, 4, 5, 8]: #events ea = Event(alltimes) ea.annotate(channel_index=channel_num) if channelHeader.kind >= 5: # Spike2 marker is closer to label sens of neo ea.labels = alltrigs['marker'].astype('S32') if channelHeader.kind == 8: ea.annotate(extra_labels=alltrigs['label']) return ea elif channelHeader.kind in [6, 7]: # spiketrains # waveforms if channelHeader.kind == 6: waveforms = np.fromstring(alltrigs['adc'].tostring(), dtype='i2') waveforms = waveforms.astype( 'f4') * channelHeader.scale / 6553.6 + \ channelHeader.offset elif channelHeader.kind == 7: waveforms = np.fromstring(alltrigs['real'].tostring(), dtype='f4') if header.system_id >= 6 and channelHeader.interleave > 1: waveforms = waveforms.reshape( (alltimes.size, -1, channelHeader.interleave)) waveforms = waveforms.swapaxes(1, 2) else: waveforms = waveforms.reshape((alltimes.size, 1, -1)) if header.system_id in [1, 2, 3, 4, 5]: sample_interval = (channelHeader.divide * header.us_per_time * header.time_per_adc) * 1e-6 else: sample_interval = (channelHeader.l_chan_dvd * header.us_per_time * header.dtime_base) if channelHeader.unit in unit_convert: unit = pq.Quantity(1, unit_convert[channelHeader.unit]) else: #print channelHeader.unit try: unit = pq.Quantity(1, channelHeader.unit) except: unit = pq.Quantity(1, '') if len(alltimes) > 0: # can get better value from associated AnalogSignal(s) ? t_stop = alltimes.max() else: t_stop = 0.0 if not self.ced_units: sptr = SpikeTrain(alltimes, waveforms = waveforms*unit, sampling_rate = (1./sample_interval)*pq.Hz, t_stop = t_stop ) sptr.annotate(channel_index = channel_num, ced_unit = 0) return [sptr] sptrs = [] for i in set(alltrigs['marker'] & 255): sptr = SpikeTrain(alltimes[alltrigs['marker'] == i], waveforms = waveforms[alltrigs['marker'] == i]*unit, sampling_rate = (1./sample_interval)*pq.Hz, t_stop = t_stop ) sptr.annotate(channel_index = channel_num, ced_unit = i) sptrs.append(sptr) return sptrs
def read_segment(self, # the 2 first keyword arguments are imposed by neo.io API lazy = False, cascade = True, # all following arguments are decied by this IO and are free segment_duration = 15., num_analogsignal = 4, num_spiketrain_by_channel = 3, ): """ Return a fake Segment. The self.filename does not matter. In this IO read by default a Segment. This is just a example to be adapted to each ClassIO. In this case these 3 paramters are taken in account because this function return a generated segment with fake AnalogSignal and fake SpikeTrain. Parameters: segment_duration :is the size in secend of the segment. num_analogsignal : number of AnalogSignal in this segment num_spiketrain : number of SpikeTrain in this segment """ sampling_rate = 10000. #Hz t_start = -1. #time vector for generated signal timevect = np.arange(t_start, t_start+ segment_duration , 1./sampling_rate) # create an empty segment seg = Segment( name = 'it is a seg from exampleio') if cascade: # read nested analosignal for i in range(num_analogsignal): ana = self.read_analogsignal( lazy = lazy , cascade = cascade , channel_index = i ,segment_duration = segment_duration, t_start = t_start) seg.analogsignals += [ ana ] # read nested spiketrain for i in range(num_analogsignal): for _ in range(num_spiketrain_by_channel): sptr = self.read_spiketrain(lazy = lazy , cascade = cascade , segment_duration = segment_duration, t_start = t_start , channel_index = i) seg.spiketrains += [ sptr ] # create an Event that mimic triggers. # note that ExampleIO do not allow to acess directly to Event # for that you need read_segment(cascade = True) if lazy: # in lazy case no data are readed # eva is empty eva = Event() else: # otherwise it really contain data n = 1000 # neo.io support quantities my vector use second for unit eva = Event(timevect[(np.random.rand(n)*timevect.size).astype('i')]* pq.s) # all duration are the same eva.durations = np.ones(n)*500*pq.ms # Event doesn't have durations. Is Epoch intended here? # label l = [ ] for i in range(n): if np.random.rand()>.6: l.append( 'TriggerA' ) else : l.append( 'TriggerB' ) eva.labels = np.array( l ) seg.events += [ eva ] seg.create_many_to_one_relationship() return seg
def read_one_channel_event_or_spike(self, fid, channel_num, header, lazy=True): # return SPikeTrain or Event channelHeader = header.channelHeaders[channel_num] if channelHeader.firstblock < 0: return if channelHeader.kind not in [2, 3, 4, 5, 6, 7, 8]: return # # Step 1 : type of blocks if channelHeader.kind in [2, 3, 4]: # Event data fmt = [('tick', 'i4')] elif channelHeader.kind in [5]: # Marker data fmt = [('tick', 'i4'), ('marker', 'i4')] elif channelHeader.kind in [6]: # AdcMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('adc', 'S%d' % channelHeader.n_extra)] elif channelHeader.kind in [7]: # RealMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('real', 'S%d' % channelHeader.n_extra)] elif channelHeader.kind in [8]: # TextMark data fmt = [('tick', 'i4'), ('marker', 'i4'), ('label', 'S%d' % channelHeader.n_extra)] dt = np.dtype(fmt) ## Step 2 : first read for allocating mem fid.seek(channelHeader.firstblock) totalitems = 0 for _ in range(channelHeader.blocks): blockHeader = HeaderReader(fid, np.dtype(blockHeaderDesciption)) totalitems += blockHeader.items if blockHeader.succ_block > 0: fid.seek(blockHeader.succ_block) #~ print 'totalitems' , totalitems if lazy: if channelHeader.kind in [2, 3, 4, 5, 8]: ea = Event() ea.annotate(channel_index=channel_num) ea.lazy_shape = totalitems return ea elif channelHeader.kind in [6, 7]: # correct value for t_stop to be put in later sptr = SpikeTrain([] * pq.s, t_stop=1e99) sptr.annotate(channel_index=channel_num, ced_unit=0) sptr.lazy_shape = totalitems return sptr else: alltrigs = np.zeros(totalitems, dtype=dt) ## Step 3 : read fid.seek(channelHeader.firstblock) pos = 0 for _ in range(channelHeader.blocks): blockHeader = HeaderReader(fid, np.dtype(blockHeaderDesciption)) # read all events in block trigs = np.fromstring(fid.read(blockHeader.items * dt.itemsize), dtype=dt) alltrigs[pos:pos + trigs.size] = trigs pos += trigs.size if blockHeader.succ_block > 0: fid.seek(blockHeader.succ_block) ## Step 3 convert in neo standard class: eventarrays or spiketrains alltimes = alltrigs['tick'].astype( 'f') * header.us_per_time * header.dtime_base * pq.s if channelHeader.kind in [2, 3, 4, 5, 8]: #events ea = Event(alltimes) ea.annotate(channel_index=channel_num) if channelHeader.kind >= 5: # Spike2 marker is closer to label sens of neo ea.labels = alltrigs['marker'].astype('S32') if channelHeader.kind == 8: ea.annotate(extra_labels=alltrigs['label']) return ea elif channelHeader.kind in [6, 7]: # spiketrains # waveforms if channelHeader.kind == 6: waveforms = np.fromstring(alltrigs['adc'].tostring(), dtype='i2') waveforms = waveforms.astype( 'f4') * channelHeader.scale / 6553.6 + \ channelHeader.offset elif channelHeader.kind == 7: waveforms = np.fromstring(alltrigs['real'].tostring(), dtype='f4') if header.system_id >= 6 and channelHeader.interleave > 1: waveforms = waveforms.reshape( (alltimes.size, -1, channelHeader.interleave)) waveforms = waveforms.swapaxes(1, 2) else: waveforms = waveforms.reshape((alltimes.size, 1, -1)) if header.system_id in [1, 2, 3, 4, 5]: sample_interval = (channelHeader.divide * header.us_per_time * header.time_per_adc) * 1e-6 else: sample_interval = (channelHeader.l_chan_dvd * header.us_per_time * header.dtime_base) if channelHeader.unit in unit_convert: unit = pq.Quantity(1, unit_convert[channelHeader.unit]) else: #print channelHeader.unit try: unit = pq.Quantity(1, channelHeader.unit) except: unit = pq.Quantity(1, '') if len(alltimes) > 0: # can get better value from associated AnalogSignal(s) ? t_stop = alltimes.max() else: t_stop = 0.0 if not self.ced_units: sptr = SpikeTrain(alltimes, waveforms=waveforms * unit, sampling_rate=(1. / sample_interval) * pq.Hz, t_stop=t_stop) sptr.annotate(channel_index=channel_num, ced_unit=0) return [sptr] sptrs = [] for i in set(alltrigs['marker'] & 255): sptr = SpikeTrain( alltimes[alltrigs['marker'] == i], waveforms=waveforms[alltrigs['marker'] == i] * unit, sampling_rate=(1. / sample_interval) * pq.Hz, t_stop=t_stop) sptr.annotate(channel_index=channel_num, ced_unit=i) sptrs.append(sptr) return sptrs
def read_segment( self, # the 2 first keyword arguments are imposed by neo.io API lazy=False, cascade=True, # all following arguments are decied by this IO and are free segment_duration=15., num_analogsignal=4, num_spiketrain_by_channel=3, ): """ Return a fake Segment. The self.filename does not matter. In this IO read by default a Segment. This is just a example to be adapted to each ClassIO. In this case these 3 paramters are taken in account because this function return a generated segment with fake AnalogSignal and fake SpikeTrain. Parameters: segment_duration :is the size in secend of the segment. num_analogsignal : number of AnalogSignal in this segment num_spiketrain : number of SpikeTrain in this segment """ sampling_rate = 10000. #Hz t_start = -1. #time vector for generated signal timevect = np.arange(t_start, t_start + segment_duration, 1. / sampling_rate) # create an empty segment seg = Segment(name='it is a seg from exampleio') if cascade: # read nested analosignal for i in range(num_analogsignal): ana = self.read_analogsignal(lazy=lazy, cascade=cascade, channel_index=i, segment_duration=segment_duration, t_start=t_start) seg.analogsignals += [ana] # read nested spiketrain for i in range(num_analogsignal): for _ in range(num_spiketrain_by_channel): sptr = self.read_spiketrain( lazy=lazy, cascade=cascade, segment_duration=segment_duration, t_start=t_start, channel_index=i) seg.spiketrains += [sptr] # create an Event that mimic triggers. # note that ExampleIO do not allow to acess directly to Event # for that you need read_segment(cascade = True) if lazy: # in lazy case no data are readed # eva is empty eva = Event() else: # otherwise it really contain data n = 1000 # neo.io support quantities my vector use second for unit eva = Event( timevect[(np.random.rand(n) * timevect.size).astype('i')] * pq.s) # all duration are the same eva.durations = np.ones( n ) * 500 * pq.ms # Event doesn't have durations. Is Epoch intended here? # label l = [] for i in range(n): if np.random.rand() > .6: l.append('TriggerA') else: l.append('TriggerB') eva.labels = np.array(l) seg.events += [eva] seg.create_many_to_one_relationship() return seg