Python AnalogSignal.annotate方法代码示例

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
 def read_analogsignal(self, channel_index=None, lazy=False, cascade=True):
     """
     Read raw traces
     Arguments:
         channel_index: must be integer array
     """
     if self._attrs["app_data"]:
         bit_volts = self._attrs["app_data"]["channel_bit_volts"]
         sig_unit = "uV"
     else:
         bit_volts = np.ones((self._attrs["shape"][1]))  # TODO: find conversion in phy generated files
         sig_unit = "bit"
     if lazy:
         anasig = AnalogSignal(
             [],
             units=sig_unit,
             sampling_rate=self._attrs["kwik"]["sample_rate"] * pq.Hz,
             t_start=self._attrs["kwik"]["start_time"] * pq.s,
         )
         # we add the attribute lazy_shape with the size if loaded
         anasig.lazy_shape = self._attrs["shape"][0]
     else:
         data = self._kwd["recordings"][str(self._dataset)]["data"].value[:, channel_index]
         data = data * bit_volts[channel_index]
         anasig = AnalogSignal(
             data,
             units=sig_unit,
             sampling_rate=self._attrs["kwik"]["sample_rate"] * pq.Hz,
             t_start=self._attrs["kwik"]["start_time"] * pq.s,
         )
         data = []  # delete from memory
     # for attributes out of neo you can annotate
     anasig.annotate(info="raw traces")
     return anasig

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def read_analogsignal(self ,
                          # the 2 first key arguments are imposed by neo.io API
                          lazy = False,
                          cascade = True,
                          channel_index = 0,
                          segment_duration = 15.,
                          t_start = -1,
                          ):
        """
        With this IO AnalogSignal can e acces directly with its channel number

        """
        sr = 10000.
        sinus_freq = 3. # Hz
        #time vector for generated signal:
        tvect = np.arange(t_start, t_start+ segment_duration , 1./sr)


        if lazy:
            anasig = AnalogSignal([], units='V', sampling_rate=sr * pq.Hz,
                                  t_start=t_start * pq.s,
                                  channel_index=channel_index)
            # we add the attribute lazy_shape with the size if loaded
            anasig.lazy_shape = tvect.shape
        else:
            # create analogsignal (sinus of 3 Hz)
            sig = np.sin(2*np.pi*tvect*sinus_freq + channel_index/5.*2*np.pi)+np.random.rand(tvect.size)
            anasig = AnalogSignal(sig, units= 'V', sampling_rate=sr * pq.Hz,
                                  t_start=t_start * pq.s,
                                  channel_index=channel_index)

        # for attributes out of neo you can annotate
        anasig.annotate(info = 'it is a sinus of %f Hz' %sinus_freq )

        return anasig

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def read_analogsignal(self,
                          # the 2 first key arguments are imposed by neo.io
                          lazy = False,
                          cascade = True,
                          #channel index as given by the neuroshare API
                          channel_index = 0,
                          #time in seconds to be read
                          segment_duration = 0.,
                          #time in seconds to start reading from
                          t_start = 0.,
                          ):
        
        #some controls:        
        #if no segment duration is given, use the complete file
        if segment_duration ==0.:
            segment_duration=float(self.metadata["TimeSpan"])
        #if the segment duration is bigger than file, use the complete file
        if segment_duration >=float(self.metadata["TimeSpan"]):
            segment_duration=float(self.metadata["TimeSpan"])
            
        if lazy:
            anasig = AnalogSignal([], units="V", sampling_rate =  self.metadata["sampRate"] * pq.Hz,
                                  t_start=t_start * pq.s,
                                  )
            #create a dummie time vector                     
            tvect = np.arange(t_start, t_start+ segment_duration , 1./self.metadata["sampRate"])                                  
            # we add the attribute lazy_shape with the size if loaded
            anasig.lazy_shape = tvect.shape
        else:
            #get the analog object
            sig =  self.fd.get_entity(channel_index)
            #get the units (V, mV etc)            
            sigUnits = sig.units
            #get the electrode number
            chanName = sig.label[-4:]
            
            #transform t_start into index (reading will start from this index)           
            startat = int(t_start*self.metadata["sampRate"])
            #get the number of bins to read in
            bins = int((segment_duration+t_start) * self.metadata["sampRate"])
            
            #if the number of bins to read is bigger than 
            #the total number of bins, read only till the end of analog object
            if startat+bins > sig.item_count:
                bins = sig.item_count-startat
            #read the data from the sig object
            sig,_,_ = sig.get_data(index = startat, count = bins)
            #store it to the 'AnalogSignal' object
            anasig = AnalogSignal(sig, units = sigUnits, sampling_rate=self.metadata["sampRate"] * pq.Hz,
                                  t_start=t_start * pq.s,
                                  t_stop = (t_start+segment_duration)*pq.s,
                                  channel_index=channel_index)

            # annotate from which electrode the signal comes from
            anasig.annotate(info = "signal from channel %s" %chanName )

        return anasig

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def create_all_annotated(cls):
        times = cls.rquant(1, pq.s)
        signal = cls.rquant(1, pq.V)
        blk = Block()
        blk.annotate(**cls.rdict(3))

        seg = Segment()
        seg.annotate(**cls.rdict(4))
        blk.segments.append(seg)

        asig = AnalogSignal(signal=signal, sampling_rate=pq.Hz)
        asig.annotate(**cls.rdict(2))
        seg.analogsignals.append(asig)

        isig = IrregularlySampledSignal(times=times, signal=signal,
                                        time_units=pq.s)
        isig.annotate(**cls.rdict(2))
        seg.irregularlysampledsignals.append(isig)

        epoch = Epoch(times=times, durations=times)
        epoch.annotate(**cls.rdict(4))
        seg.epochs.append(epoch)

        event = Event(times=times)
        event.annotate(**cls.rdict(4))
        seg.events.append(event)

        spiketrain = SpikeTrain(times=times, t_stop=pq.s, units=pq.s)
        d = cls.rdict(6)
        d["quantity"] = pq.Quantity(10, "mV")
        d["qarray"] = pq.Quantity(range(10), "mA")
        spiketrain.annotate(**d)
        seg.spiketrains.append(spiketrain)

        chx = ChannelIndex(name="achx", index=[1, 2], channel_ids=[0, 10])
        chx.annotate(**cls.rdict(5))
        blk.channel_indexes.append(chx)

        unit = Unit()
        unit.annotate(**cls.rdict(2))
        chx.units.append(unit)

        return blk

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def _extract_signals(self, data, metadata, lazy):

        signal = None
        if lazy and data.size > 0:
            signal = AnalogSignal([],
                                  units=self._determine_units(metadata),
                                  sampling_period=metadata['dt']*pq.ms)
            signal.lazy_shape = None
        else:
            arr = numpy.vstack(self._extract_array(data, channel_index)
                               for channel_index in range(metadata['first_index'], metadata['last_index'] + 1))
            if len(arr) > 0:
                signal = AnalogSignal(arr.T,
                                      units=self._determine_units(metadata),
                                      sampling_period=metadata['dt']*pq.ms)
        if signal is not None:
            signal.annotate(label=metadata["label"],
                            variable=metadata["variable"])
        return signal

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def read_analogsignal(self,
                      channel_index=None,
                      lazy=False,
                      cascade=True,
                      ):
        """
        Read raw traces
        Arguments:
            channel_index: must be integer
        """
        try:
            channel_index = int(channel_index)
        except TypeError:
            print('channel_index must be int, not %s' %type(channel_index))

        if self._attrs['app_data']:
            bit_volts = self._attrs['app_data']['channel_bit_volts']
            sig_unit = 'uV'
        else:
            bit_volts = np.ones((self._attrs['shape'][1])) # TODO: find conversion in phy generated files
            sig_unit =  'bit'
        if lazy:
            anasig = AnalogSignal([],
                                  units=sig_unit,
                                  sampling_rate=self._attrs['kwik']['sample_rate']*pq.Hz,
                                  t_start=self._attrs['kwik']['start_time']*pq.s,
                                  channel_index=channel_index,
                                  )
            # we add the attribute lazy_shape with the size if loaded
            anasig.lazy_shape = self._attrs['shape'][0]
        else:
            data = self._kwd['recordings'][str(self._dataset)]['data'].value[:,channel_index]
            data = data * bit_volts[channel_index]
            anasig = AnalogSignal(data,
                                       units=sig_unit,
                                       sampling_rate=self._attrs['kwik']['sample_rate']*pq.Hz,
                                       t_start=self._attrs['kwik']['start_time']*pq.s,
                                       channel_index=channel_index,
                                       )
            data = [] # delete from memory
        # for attributes out of neo you can annotate
        anasig.annotate(info='raw traces')
        return anasig

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
 def _extract_signal(self, data, metadata, channel_index, lazy):
     signal = None
     if lazy:
         if channel_index in data[:, 1]:
             signal = AnalogSignal([],
                                   units=self._determine_units(metadata),
                                   sampling_period=metadata['dt']*pq.ms,
                                   channel_index=channel_index)
             signal.lazy_shape = None
     else:
         arr = self._extract_array(data, channel_index)
         if len(arr) > 0:
             signal = AnalogSignal(arr,
                                   units=self._determine_units(metadata),
                                   sampling_period=metadata['dt']*pq.ms,
                                   channel_index=channel_index)
     if signal is not None:
         signal.annotate(label=metadata["label"],
                         variable=metadata["variable"])
         return signal

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
def gettrace(trec,f):
    import numpy as np
    format_type_lenghts = [2,4,4,8]
    format_type = [np.int16,np.int32,np.float32,np.float64]
    pointsize = format_type_lenghts[int(trec.trDataFormat)]
    dtype = format_type[int(trec.trDataFormat)]
    f.seek(int(trec.trData))
    byte_string = f.read(int(trec.trDataPoints)*pointsize)
    import numpy as np
    ydata = np.fromstring(byte_string,dtype = dtype)
    tunit = pq.Quantity(1,str(trec.trXUnit))
    yunit = pq.Quantity(1,str(trec.trYUnit))
    sig = AnalogSignal(ydata*float(trec.trDataScaler)*yunit,
            sampling_period=float(trec.trXInterval)*tunit,
            units = trec.trYUnit[0])
    annotations = trec.__dict__.keys()
    annotations.remove('readlist')
    for a in annotations:
        d = {a:str(trec.__dict__[a])}
        sig.annotate(**d)
    return sig

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    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))

#.........这里部分代码省略.........

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]
    def read_segment(self, cascade=True, lazy=False, ):
        """
        Arguments:
        """
        f = StructFile(open(self.filename, 'rb'))

        # Name
        f.seek(64, 0)
        surname = f.read(22).decode('ascii')
        while surname[-1] == ' ':
            if len(surname) == 0:
                break
            surname = surname[:-1]
        firstname = f.read(20).decode('ascii')
        while firstname[-1] == ' ':
            if len(firstname) == 0:
                break
            firstname = firstname[:-1]

        #Date
        f.seek(128, 0)
        day, month, year, hour, minute, sec = f.read_f('bbbbbb')
        rec_datetime = datetime.datetime(year + 1900, month, day, hour, minute,
                                         sec)

        f.seek(138, 0)
        Data_Start_Offset, Num_Chan, Multiplexer, Rate_Min, Bytes = f.read_f(
            'IHHHH')
        #~ print Num_Chan, Bytes

        #header version
        f.seek(175, 0)
        header_version, = f.read_f('b')
        assert header_version == 4

        seg = Segment(name=str(firstname + ' ' + surname),
                      file_origin=os.path.basename(self.filename))
        seg.annotate(surname=surname)
        seg.annotate(firstname=firstname)
        seg.annotate(rec_datetime=rec_datetime)

        if not cascade:
            f.close()
            return seg

        # area
        f.seek(176, 0)
        zone_names = ['ORDER', 'LABCOD', 'NOTE', 'FLAGS', 'TRONCA', 'IMPED_B',
                      'IMPED_E', 'MONTAGE',
                      'COMPRESS', 'AVERAGE', 'HISTORY', 'DVIDEO', 'EVENT A',
                      'EVENT B', 'TRIGGER']
        zones = {}
        for zname in zone_names:
            zname2, pos, length = f.read_f('8sII')
            zones[zname] = zname2, pos, length
            #~ print zname2, pos, length

        # reading raw data
        if not lazy:
            f.seek(Data_Start_Offset, 0)
            rawdata = np.fromstring(f.read(), dtype='u' + str(Bytes))
            rawdata = rawdata.reshape((-1, Num_Chan))

        # Reading Code Info
        zname2, pos, length = zones['ORDER']
        f.seek(pos, 0)
        code = np.fromstring(f.read(Num_Chan*2), dtype='u2', count=Num_Chan)

        units = {-1: pq.nano * pq.V, 0: pq.uV, 1: pq.mV, 2: 1, 100: pq.percent,
                 101: pq.dimensionless, 102: pq.dimensionless}

        for c in range(Num_Chan):
            zname2, pos, length = zones['LABCOD']
            f.seek(pos + code[c] * 128 + 2, 0)

            label = f.read(6).strip(b"\x00").decode('ascii')
            ground = f.read(6).strip(b"\x00").decode('ascii')
            (logical_min, logical_max, logical_ground, physical_min,
             physical_max) = f.read_f('iiiii')
            k, = f.read_f('h')
            if k in units.keys():
                unit = units[k]
            else:
                unit = pq.uV

            f.seek(8, 1)
            sampling_rate, = f.read_f('H') * pq.Hz
            sampling_rate *= Rate_Min

            if lazy:
                signal = [] * unit
            else:
                factor = float(physical_max - physical_min) / float(
                    logical_max - logical_min + 1)
                signal = (rawdata[:, c].astype(
                    'f') - logical_ground) * factor * unit

            ana_sig = AnalogSignal(signal, sampling_rate=sampling_rate,
                                   name=str(label), channel_index=c)
            if lazy:
#.........这里部分代码省略.........

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]

#.........这里部分代码省略.........
        l = f.readline()

        # sampling rate sample
        l = f.readline()
        sampling_rate = 1. / float(l) * pq.Hz

        # nb channel
        l = f.readline()
        nbchannel = int(l) - 2

        #channel label
        labels = []
        for c in range(nbchannel + 2):
            labels.append(f.readline()[:-1])

        # channel type
        types = []
        for c in range(nbchannel + 2):
            types.append(f.readline()[:-1])

        # channel unit
        units = []
        for c in range(nbchannel + 2):
            units.append(f.readline()[:-1])
        #print units

        #range
        min_physic = []
        for c in range(nbchannel + 2):
            min_physic.append(float(f.readline()))
        max_physic = []
        for c in range(nbchannel + 2):
            max_physic.append(float(f.readline()))
        min_logic = []
        for c in range(nbchannel + 2):
            min_logic.append(float(f.readline()))
        max_logic = []
        for c in range(nbchannel + 2):
            max_logic.append(float(f.readline()))

        #info filter
        info_filter = []
        for c in range(nbchannel + 2):
            info_filter.append(f.readline()[:-1])

        f.close()

        #raw data
        n = int(round(np.log(max_logic[0] - min_logic[0]) / np.log(2)) / 8)
        data = np.fromfile(self.filename, dtype='i' + str(n))
        data = data.byteswap().reshape(
            (data.size / (nbchannel + 2), nbchannel + 2)).astype('f4')
        for c in range(nbchannel):
            if lazy:
                sig = []
            else:
                sig = (data[:, c] - min_logic[c]) / (
                    max_logic[c] - min_logic[c]) * \
                    (max_physic[c] - min_physic[c]) + min_physic[c]

            try:
                unit = pq.Quantity(1, units[c])
            except:
                unit = pq.Quantity(1, '')

            ana_sig = AnalogSignal(
                sig * unit, sampling_rate=sampling_rate,
                t_start=0. * pq.s, name=labels[c], channel_index=c)
            if lazy:
                ana_sig.lazy_shape = data.shape[0]
            ana_sig.annotate(channel_name=labels[c])
            seg.analogsignals.append(ana_sig)

        # triggers
        f = open(self.filename + '.pos')
        times = []
        labels = []
        reject_codes = []
        for l in f.readlines():
            r = re.findall(' *(\d+) *(\d+) *(\d+) *', l)
            times.append(float(r[0][0]) / sampling_rate.magnitude)
            labels.append(str(r[0][1]))
            reject_codes.append(str(r[0][2]))
        if lazy:
            times = [] * pq.S
            labels = np.array([], dtype='S')
            reject_codes = []
        else:
            times = np.array(times) * pq.s
            labels = np.array(labels)
            reject_codes = np.array(reject_codes)
        ea = Event(times=times, labels=labels, reject_codes=reject_codes)
        if lazy:
            ea.lazy_shape = len(times)
        seg.events.append(ea)

        f.close()

        seg.create_many_to_one_relationship()
        return seg

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]

#.........这里部分代码省略.........

            # step 4: compute the length (number of samples) of the channels
            chan_len = np.zeros(len(list_data), dtype = np.int)
            for ind_chan, list_blocks in enumerate(list_data):
                for ind_block in list_blocks:
                    chan_len[ind_chan] += count_samples(
                                          file_blocks[ind_block]['m_length'])

            # step 5: find channels for which data are available
            ind_valid_chan = np.nonzero(chan_len)[0]

            # step 6: load the data
            # TODO give the possibility to load data as AnalogSignalArrays
            for ind_chan in ind_valid_chan:
                list_blocks = list_data[ind_chan]
                ind = 0 # index in the data vector

                # read time stamp for the beginning of the signal
                form = '<l' # reading format
                ind_block = list_blocks[0]
                count = count_samples(file_blocks[ind_block]['m_length'])
                fid.seek(file_blocks[ind_block]['pos']+6+count*2)
                buf = fid.read(struct.calcsize(form))
                val = struct.unpack(form , buf)
                start_index = val[0]

                # WARNING: in the following blocks are read supposing taht they
                # are all contiguous and sorted in time. I don't know if it's
                # always the case. Maybe we should use the time stamp of each
                # data block to choose where to put the read data in the array.
                if not lazy:
                    temp_array = np.empty(chan_len[ind_chan], dtype = np.int16)
                    # NOTE: we could directly create an empty AnalogSignal and
                    # load the data in it, but it is much faster to load data
                    # in a temporary numpy array and create the AnalogSignals
                    # from this temporary array
                    for ind_block in list_blocks:
                        count = count_samples(
                                file_blocks[ind_block]['m_length'])
                        fid.seek(file_blocks[ind_block]['pos']+6)
                        temp_array[ind:ind+count] = \
                            np.fromfile(fid, dtype = np.int16, count = count)
                        ind += count

                sampling_rate = \
                    file_blocks[list_chan[ind_chan]]['m_SampleRate'] * pq.kHz
                t_start = (start_index / sampling_rate).simplified
                if lazy:
                    ana_sig = AnalogSignal([],
                                           sampling_rate = sampling_rate,
                                           t_start = t_start,
                                           name = file_blocks\
                                               [list_chan[ind_chan]]['m_Name'],
                                           file_origin = \
                                               os.path.basename(self.filename),
                                           units = pq.dimensionless)
                    ana_sig.lazy_shape = chan_len[ind_chan]
                else:
                    ana_sig = AnalogSignal(temp_array,
                                           sampling_rate = sampling_rate,
                                           t_start = t_start,
                                           name = file_blocks\
                                               [list_chan[ind_chan]]['m_Name'],
                                           file_origin = \
                                               os.path.basename(self.filename),
                                           units = pq.dimensionless)
# todo apibreak: create ChannelIndex for each signals
#                ana_sig.channel_index = \
#                            file_blocks[list_chan[ind_chan]]['m_numChannel']
                ana_sig.annotate(channel_name = \
                            file_blocks[list_chan[ind_chan]]['m_Name'])
                ana_sig.annotate(channel_type = \
                            file_blocks[list_chan[ind_chan]]['type_subblock'])
                seg.analogsignals.append(ana_sig)

        fid.close()

        if file_blocks[0]['m_TypeBlock'] == 'h': # this should always be true
            blck.rec_datetime = datetime.datetime(\
                file_blocks[0]['m_date_year'],
                file_blocks[0]['m_date_month'],
                file_blocks[0]['m_date_day'],
                file_blocks[0]['m_time_hour'],
                file_blocks[0]['m_time_minute'],
                file_blocks[0]['m_time_second'],
                10000 * file_blocks[0]['m_time_hsecond'])
                # the 10000 is here to convert m_time_hsecond from centisecond
                # to microsecond
            version = file_blocks[0]['m_version']
            blck.annotate(alphamap_version = version)
            if cascade:
                seg.rec_datetime = blck.rec_datetime.replace()
                # I couldn't find a simple copy function for datetime,
                # using replace without arguments is a twisted way to make a
                # copy
                seg.annotate(alphamap_version = version)
        if cascade:
            blck.create_many_to_one_relationship()

        return blck

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import annotate [as 别名]

#.........这里部分代码省略.........
                        "must be a time quantity.")
    if window_stoptime <= window_starttime:
        raise ValueError("The start time of the window (window[0]) must be "
                         "earlier than the stop time of the window (window[1]).")

    # checks on signal
    if not isinstance(signal, AnalogSignal):
        raise TypeError(
            "Signal must be an AnalogSignal, not %s." % type(signal))
    if len(signal.shape) > 1:
        # num_signals: number of analog signals
        num_signals = signal.shape[1]
    else:
        raise ValueError("Empty analog signal, hence no averaging possible.")
    if window_stoptime - window_starttime > signal.t_stop - signal.t_start:
        raise ValueError("The chosen time window is larger than the "
                         "time duration of the signal.")

    # spiketrains type check
    if isinstance(spiketrains, (np.ndarray, SpikeTrain)):
        spiketrains = [spiketrains]
    elif isinstance(spiketrains, list):
        for st in spiketrains:
            if not isinstance(st, (np.ndarray, SpikeTrain)):
                raise TypeError(
                    "spiketrains must be a SpikeTrain, a numpy ndarray, or a "
                    "list of one of those, not %s." % type(spiketrains))
    else:
        raise TypeError(
            "spiketrains must be a SpikeTrain, a numpy ndarray, or a list of "
            "one of those, not %s." % type(spiketrains))

    # multiplying spiketrain in case only a single spiketrain is given
    if len(spiketrains) == 1 and num_signals != 1:
        template = spiketrains[0]
        spiketrains = []
        for i in range(num_signals):
            spiketrains.append(template)

    # checking for matching numbers of signals and spiketrains
    if num_signals != len(spiketrains):
        raise ValueError(
            "The number of signals and spiketrains has to be the same.")

    # checking the times of signal and spiketrains
    for i in range(num_signals):
        if spiketrains[i].t_start < signal.t_start:
            raise ValueError(
                "The spiketrain indexed by %i starts earlier than "
                "the analog signal." % i)
        if spiketrains[i].t_stop > signal.t_stop:
            raise ValueError(
                "The spiketrain indexed by %i stops later than "
                "the analog signal." % i)

    # *** Main algorithm: ***

    # window_bins: number of bins of the chosen averaging interval
    window_bins = int(np.ceil(((window_stoptime - window_starttime) *
        signal.sampling_rate).simplified))
    # result_sta: array containing finally the spike-triggered averaged signal
    result_sta = AnalogSignal(np.zeros((window_bins, num_signals)),
        sampling_rate=signal.sampling_rate, units=signal.units)
    # setting of correct times of the spike-triggered average
    # relative to the spike
    result_sta.t_start = window_starttime
    used_spikes = np.zeros(num_signals, dtype=int)
    unused_spikes = np.zeros(num_signals, dtype=int)
    total_used_spikes = 0

    for i in range(num_signals):
        # summing over all respective signal intervals around spiketimes
        for spiketime in spiketrains[i]:
            # checks for sufficient signal data around spiketime
            if (spiketime + window_starttime >= signal.t_start and
                    spiketime + window_stoptime <= signal.t_stop):
                # calculating the startbin in the analog signal of the
                # averaging window for spike
                startbin = int(np.floor(((spiketime + window_starttime -
                    signal.t_start) * signal.sampling_rate).simplified))
                # adds the signal in selected interval relative to the spike
                result_sta[:, i] += signal[
                    startbin: startbin + window_bins, i]
                # counting of the used spikes
                used_spikes[i] += 1
            else:
                # counting of the unused spikes
                unused_spikes[i] += 1

        # normalization
        result_sta[:, i] = result_sta[:, i] / used_spikes[i]

        total_used_spikes += used_spikes[i]

    if total_used_spikes == 0:
        warnings.warn(
            "No spike at all was either found or used for averaging")
    result_sta.annotate(used_spikes=used_spikes, unused_spikes=unused_spikes)

    return result_sta