Python AnalogSignal.lazy_shape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 lazy_shape [as 别名]
 def _read_analogsignalarray(self, node, parent):
     attributes = self._get_standard_attributes(node)
     # todo: handle channel_index
     sampling_rate = self._get_quantity(node["sampling_rate"])
     t_start = self._get_quantity(node["t_start"])
     signal = AnalogSignal(self._get_quantity(node["signal"]),
                           sampling_rate=sampling_rate, t_start=t_start,
                           **attributes)
     if self._lazy:
         signal.lazy_shape = node["signal"].shape
         if len(signal.lazy_shape) == 1:
             signal.lazy_shape = (signal.lazy_shape[0], 1)
     signal.segment = parent
     self.object_refs[node.attrs["object_ref"]] = signal
     return signal

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import lazy_shape [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 lazy_shape [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 lazy_shape [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 lazy_shape [as 别名]
    def read_segment(self, n_start, n_stop, chlist=None, lazy=False, cascade=True):
        """Reads a Segment from the file and stores in database.

        The Segment will contain one AnalogSignal for each channel
        and will go from n_start to n_stop (in samples).

        Arguments:
            n_start : time in samples that the Segment begins
            n_stop : time in samples that the Segment ends

        Python indexing is used, so n_stop is not inclusive.

        Returns a Segment object containing the data.
        """
        # If no channel numbers provided, get all of them
        if chlist is None:
            chlist = self.loader.get_neural_channel_numbers()

        # Conversion from bits to full_range units
        conversion = self.full_range / 2**(8*self.header.sample_width)

        # Create the Segment
        seg = Segment(file_origin=self.filename)
        t_start = float(n_start) / self.header.f_samp
        t_stop = float(n_stop) / self.header.f_samp
        seg.annotate(t_start=t_start)
        seg.annotate(t_stop=t_stop)

        # Load data from each channel and store
        for ch in chlist:
            if lazy:
                sig = np.array([]) * conversion
            else:
                # Get the data from the loader
                sig = np.array(\
                    self.loader._get_channel(ch)[n_start:n_stop]) * conversion

            # Create an AnalogSignal with the data in it
            anasig = AnalogSignal(signal=sig,
                sampling_rate=self.header.f_samp*pq.Hz,
                t_start=t_start*pq.s, file_origin=self.filename,
                description='Channel %d from %f to %f' % (ch, t_start, t_stop),
                channel_index=int(ch))

            if lazy:
                anasig.lazy_shape = n_stop-n_start


            # Link the signal to the segment
            seg.analogsignals.append(anasig)

            # Link the signal to the recording channel from which it came
            #rc = self.channel_number_to_recording_channel[ch]
            #rc.analogsignals.append(anasig)

        return seg

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import lazy_shape [as 别名]
    def read_block(self, lazy=False, cascade=True):

        if self.filename is not None:
            self.stfio_rec = stfio.read(self.filename)

        bl = Block()
        bl.description = self.stfio_rec.file_description
        bl.annotate(comment=self.stfio_rec.comment)
        try:
            bl.rec_datetime = self.stfio_rec.datetime
        except:
            bl.rec_datetime = None

        if not cascade:
            return bl

        dt = np.round(self.stfio_rec.dt * 1e-3, 9) * pq.s  # ms to s
        sampling_rate = 1.0/dt
        t_start = 0 * pq.s

        # iterate over sections first:
        for j, recseg in enumerate(self.stfio_rec[0]):
            seg = Segment(index=j)
            length = len(recseg)

            # iterate over channels:
            for i, recsig in enumerate(self.stfio_rec):
                name = recsig.name
                unit = recsig.yunits
                try:
                    pq.Quantity(1, unit)
                except:
                    unit = ''

                if lazy:
                    signal = pq.Quantity([], unit)
                else:
                    signal = pq.Quantity(recsig[j], unit)
                anaSig = AnalogSignal(signal, sampling_rate=sampling_rate,
                                      t_start=t_start, name=str(name),
                                      channel_index=i)
                if lazy:
                    anaSig.lazy_shape = length
                seg.analogsignals.append(anaSig)

            bl.segments.append(seg)
            t_start = t_start + length * dt

        bl.create_many_to_one_relationship()

        return bl

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import lazy_shape [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 lazy_shape [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 lazy_shape [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 lazy_shape [as 别名]
    def read_analogsignal(self, lazy=False, cascade=True):
        if not HAVE_IGOR:
            raise Exception("igor package not installed. Try `pip install igor`")
        data = bw.load(self.filename)
        version = data['version']
        if version > 3:
            raise IOError("Igor binary wave file format version {0} is not supported.".format(version))
        content = data['wave']
        if "padding" in content:
            assert content['padding'].size == 0, "Cannot handle non-empty padding"
        if lazy:
            # not really lazy, since the `igor` module loads the data anyway
            signal = np.array((), dtype=content['wData'].dtype)
        else:
            signal = content['wData']
        note = content['note']
        header = content['wave_header']
        name = header['bname']
        assert header['botFullScale'] == 0
        assert header['topFullScale'] == 0
        units = "".join(header['dataUnits'])
        time_units = "".join(header['xUnits']) or "s"
        t_start = pq.Quantity(header['hsB'], time_units)
        sampling_period = pq.Quantity(header['hsA'], time_units)
        if self.parse_notes:
            try:
                annotations = self.parse_notes(note)
            except ValueError:
                warn("Couldn't parse notes field.")
                annotations = {'note': note}
        else:
            annotations = {'note': note}

        signal = AnalogSignal(signal, units=units, copy=False, t_start=t_start,
                              sampling_period=sampling_period, name=name,
                              file_origin=self.filename, **annotations)
        if lazy:
            signal.lazy_shape = content['wData'].shape
        return signal

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

#.........这里部分代码省略.........
                    float_signal = self.rescale_signal_raw_to_float(
                        raw_signal,
                        dtype='float32',
                        channel_indexes=channel_indexes)

                for i, (ind_within, ind_abs) in self._make_signal_channel_subgroups(
                        channel_indexes,
                        signal_group_mode=signal_group_mode).items():
                    units = np.unique(signal_channels[ind_abs]['units'])
                    assert len(units) == 1
                    units = ensure_signal_units(units[0])

                    if signal_group_mode == 'split-all':
                        # in that case annotations by channel is OK
                        chan_index = ind_abs[0]
                        d = self.raw_annotations['blocks'][block_index]['segments'][seg_index][
                            'signals'][chan_index]
                        annotations = dict(d)
                        if 'name' not in annotations:
                            annotations['name'] = signal_channels['name'][chan_index]
                    else:
                        # when channel are grouped by same unit
                        # annotations have channel_names and channel_ids array
                        # this will be moved in array annotations soon
                        annotations = {}
                        annotations['name'] = 'Channel bundle ({}) '.format(
                            ','.join(signal_channels[ind_abs]['name']))
                        annotations['channel_names'] = signal_channels[ind_abs]['name']
                        annotations['channel_ids'] = signal_channels[ind_abs]['id']
                    annotations = check_annotations(annotations)
                    if lazy:
                        anasig = AnalogSignal(np.array([]), units=units, copy=False,
                                              sampling_rate=sr, t_start=sig_t_start, **annotations)
                        anasig.lazy_shape = (sig_size, len(ind_within))
                    else:
                        anasig = AnalogSignal(float_signal[:, ind_within], units=units, copy=False,
                                              sampling_rate=sr, t_start=sig_t_start, **annotations)
                    seg.analogsignals.append(anasig)

        # SpikeTrain and waveforms (optional)
        unit_channels = self.header['unit_channels']
        for unit_index in range(len(unit_channels)):
            if not lazy and load_waveforms:
                raw_waveforms = self.get_spike_raw_waveforms(block_index=block_index,
                                                             seg_index=seg_index,
                                                             unit_index=unit_index,
                                                             t_start=t_start_, t_stop=t_stop_)
                float_waveforms = self.rescale_waveforms_to_float(raw_waveforms, dtype='float32',
                                                                  unit_index=unit_index)
                wf_units = ensure_signal_units(unit_channels['wf_units'][unit_index])
                waveforms = pq.Quantity(float_waveforms, units=wf_units,
                                        dtype='float32', copy=False)
                wf_sampling_rate = unit_channels['wf_sampling_rate'][unit_index]
                wf_left_sweep = unit_channels['wf_left_sweep'][unit_index]
                if wf_left_sweep > 0:
                    wf_left_sweep = float(wf_left_sweep) / wf_sampling_rate * pq.s
                else:
                    wf_left_sweep = None
                wf_sampling_rate = wf_sampling_rate * pq.Hz
            else:
                waveforms = None
                wf_left_sweep = None
                wf_sampling_rate = None

            d = self.raw_annotations['blocks'][block_index]['segments'][seg_index]['units'][
                unit_index]

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import lazy_shape [as 别名]
    def read_segment(self, lazy=False, cascade=True):
        fid = open(self.filename, 'rb')
        global_header = HeaderReader(fid, GlobalHeader).read_f(offset=0)
        # ~ print globalHeader
        #~ print 'version' , globalHeader['version']
        seg = Segment()
        seg.file_origin = os.path.basename(self.filename)
        seg.annotate(neuroexplorer_version=global_header['version'])
        seg.annotate(comment=global_header['comment'])

        if not cascade:
            return seg

        offset = 544
        for i in range(global_header['nvar']):
            entity_header = HeaderReader(fid, EntityHeader).read_f(
                offset=offset + i * 208)
            entity_header['name'] = entity_header['name'].replace('\x00', '')

            #print 'i',i, entityHeader['type']

            if entity_header['type'] == 0:
                # neuron
                if lazy:
                    spike_times = [] * pq.s
                else:
                    spike_times = np.memmap(self.filename, np.dtype('i4'), 'r',
                                            shape=(entity_header['n']),
                                            offset=entity_header['offset'])
                    spike_times = spike_times.astype('f8') / global_header[
                        'freq'] * pq.s
                sptr = SpikeTrain(
                    times=spike_times,
                    t_start=global_header['tbeg'] /
                    global_header['freq'] * pq.s,
                    t_stop=global_header['tend'] /
                    global_header['freq'] * pq.s,
                    name=entity_header['name'])
                if lazy:
                    sptr.lazy_shape = entity_header['n']
                sptr.annotate(channel_index=entity_header['WireNumber'])
                seg.spiketrains.append(sptr)

            if entity_header['type'] == 1:
                # event
                if lazy:
                    event_times = [] * pq.s
                else:
                    event_times = np.memmap(self.filename, np.dtype('i4'), 'r',
                                            shape=(entity_header['n']),
                                            offset=entity_header['offset'])
                    event_times = event_times.astype('f8') / global_header[
                        'freq'] * pq.s
                labels = np.array([''] * event_times.size, dtype='S')
                evar = Event(times=event_times, labels=labels,
                             channel_name=entity_header['name'])
                if lazy:
                    evar.lazy_shape = entity_header['n']
                seg.events.append(evar)

            if entity_header['type'] == 2:
                # interval
                if lazy:
                    start_times = [] * pq.s
                    stop_times = [] * pq.s
                else:
                    start_times = np.memmap(self.filename, np.dtype('i4'), 'r',
                                            shape=(entity_header['n']),
                                            offset=entity_header['offset'])
                    start_times = start_times.astype('f8') / global_header[
                        'freq'] * pq.s
                    stop_times = np.memmap(self.filename, np.dtype('i4'), 'r',
                                           shape=(entity_header['n']),
                                           offset=entity_header['offset'] +
                                           entity_header['n'] * 4)
                    stop_times = stop_times.astype('f') / global_header[
                        'freq'] * pq.s
                epar = Epoch(times=start_times,
                             durations=stop_times - start_times,
                             labels=np.array([''] * start_times.size,
                                             dtype='S'),
                             channel_name=entity_header['name'])
                if lazy:
                    epar.lazy_shape = entity_header['n']
                seg.epochs.append(epar)

            if entity_header['type'] == 3:
                # spiketrain and wavefoms
                if lazy:
                    spike_times = [] * pq.s
                    waveforms = None
                else:

                    spike_times = np.memmap(self.filename, np.dtype('i4'), 'r',
                                            shape=(entity_header['n']),
                                            offset=entity_header['offset'])
                    spike_times = spike_times.astype('f8') / global_header[
                        'freq'] * pq.s

                    waveforms = np.memmap(self.filename, np.dtype('i2'), 'r',
#.........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 lazy_shape [as 别名]

#.........这里部分代码省略.........
            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:
                ana_sig.lazy_shape = None
            ana_sig.annotate(ground=ground)

            seg.analogsignals.append(ana_sig)

        sampling_rate = np.mean(
            [ana_sig.sampling_rate for ana_sig in seg.analogsignals]) * pq.Hz

        # Read trigger and notes
        for zname, label_dtype in [('TRIGGER', 'u2'), ('NOTE', 'S40')]:
            zname2, pos, length = zones[zname]
            f.seek(pos, 0)
            triggers = np.fromstring(f.read(length), dtype=[('pos', 'u4'), (
                'label', label_dtype)])
            if not lazy:
                keep = (triggers['pos'] >= triggers['pos'][0]) & (
                    triggers['pos'] < rawdata.shape[0]) & (
                    triggers['pos'] != 0)
                triggers = triggers[keep]
                ea = Event(name=zname[0] + zname[1:].lower(),
                           labels=triggers['label'].astype('S'),
                           times=(triggers['pos'] / sampling_rate).rescale('s'))
            else:
                ea = Event(name=zname[0] + zname[1:].lower())
                ea.lazy_shape = triggers.size
            seg.events.append(ea)

        # Read Event A and B
        # Not so well  tested
        for zname in ['EVENT A', 'EVENT B']:
            zname2, pos, length = zones[zname]
            f.seek(pos, 0)
            epochs = np.fromstring(f.read(length),
                                   dtype=[('label', 'u4'), ('start', 'u4'),
                                          ('stop', 'u4'), ])
            ep = Epoch(name=zname[0] + zname[1:].lower())
            if not lazy:
                keep = (epochs['start'] > 0) & (
                    epochs['start'] < rawdata.shape[0]) & (
                    epochs['stop'] < rawdata.shape[0])
                epochs = epochs[keep]
                ep = Epoch(name=zname[0] + zname[1:].lower(),
                           labels=epochs['label'].astype('S'),
                           times=(epochs['start'] / sampling_rate).rescale('s'),
                           durations=((epochs['stop'] - epochs['start']) / sampling_rate).rescale('s'))
            else:
                ep = Epoch(name=zname[0] + zname[1:].lower())
                ep.lazy_shape = triggers.size
            seg.epochs.append(ep)

        seg.create_many_to_one_relationship()
        f.close()
        return seg

# 需要导入模块: from neo.core import AnalogSignal [as 别名]
# 或者: from neo.core.AnalogSignal import lazy_shape [as 别名]
    def read_segment(self, blockname=None, lazy=False, cascade=True, sortname=''):
        """
        Read a single segment from the tank. Note that TDT blocks are Neo
        segments, and TDT tanks are Neo blocks, so here the 'blockname' argument
        refers to the TDT block's name, which will be the Neo segment name.
        sortname is used to specify the external sortcode generated by offline spike sorting, if sortname=='PLX',
        there should be a ./sort/PLX/*.SortResult file in the tdt block, which stores the sortcode for every spike,
        default to '', which uses the original online sort
        """
        if not blockname:
            blockname = os.listdir(self.dirname)[0]

        if blockname == 'TempBlk': return None

        if not self.is_tdtblock(blockname): return None    # if not a tdt block

        subdir = os.path.join(self.dirname, blockname)
        if not os.path.isdir(subdir): return None

        seg = Segment(name=blockname)

        tankname = os.path.basename(self.dirname)

        #TSQ is the global index
        tsq_filename = os.path.join(subdir, tankname+'_'+blockname+'.tsq')
        dt = [('size','int32'),
                    ('evtype','int32'),
                    ('code','S4'),
                    ('channel','uint16'),
                    ('sortcode','uint16'),
                    ('timestamp','float64'),
                    ('eventoffset','int64'),
                    ('dataformat','int32'),
                    ('frequency','float32'),
                ]
        tsq = np.fromfile(tsq_filename, dtype=dt)

        #0x8801: 'EVTYPE_MARK' give the global_start
        global_t_start = tsq[tsq['evtype']==0x8801]['timestamp'][0]

        #TEV is the old data file
        try:
            tev_filename = os.path.join(subdir, tankname+'_'+blockname+'.tev')
            #tev_array = np.memmap(tev_filename, mode = 'r', dtype = 'uint8') # if memory problem use this instead
            tev_array = np.fromfile(tev_filename, dtype='uint8')
        except IOError:
            tev_filename = None


        #if exists an external sortcode in ./sort/[sortname]/*.SortResult (generated after offline sortting)
        sortresult_filename = None
        if sortname is not '':
            try:
                for file in os.listdir(os.path.join(subdir, 'sort', sortname)):
                    if file.endswith(".SortResult"):
                        sortresult_filename = os.path.join(subdir, 'sort', sortname, file)

                        # get new sortcode
                        newsorcode = np.fromfile(sortresult_filename,'int8')[1024:]  # the first 1024 byte is file header
                        # update the sort code with the info from this file
                        tsq['sortcode'][1:-1]=newsorcode
                        # print('sortcode updated')
                        break
            except OSError:
                sortresult_filename = None
            except IOError:
                sortresult_filename = None


        for type_code, type_label in tdt_event_type:
            mask1 = tsq['evtype']==type_code
            codes = np.unique(tsq[mask1]['code'])

            for code in codes:
                mask2 = mask1 & (tsq['code']==code)
                channels = np.unique(tsq[mask2]['channel'])

                for channel in channels:
                    mask3 = mask2 & (tsq['channel']==channel)

                    if type_label in ['EVTYPE_STRON', 'EVTYPE_STROFF']:
                        if lazy:
                            times = [ ]*pq.s
                            labels = np.array([ ], dtype=str)
                        else:
                            times = (tsq[mask3]['timestamp'] - global_t_start) * pq.s
                            labels = tsq[mask3]['eventoffset'].view('float64').astype('S')
                        ea = Event(times=times,
                                   name=code,
                                   channel_index=int(channel),
                                   labels=labels)
                        if lazy:
                            ea.lazy_shape = np.sum(mask3)
                        seg.events.append(ea)

                    elif type_label == 'EVTYPE_SNIP':
                        sortcodes = np.unique(tsq[mask3]['sortcode'])
                        for sortcode in sortcodes:
                            mask4 = mask3 & (tsq['sortcode']==sortcode)
                            nb_spike = np.sum(mask4)
#.........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 lazy_shape [as 别名]
    def read_segment(self , lazy = False, cascade = True):
        seg  = Segment(
                                    file_origin = os.path.basename(self.filename),
                                    )

        if not cascade:
            return seg

        fid = open(self.filename , 'rb')

        headertext = fid.read(2048)
        if PY3K:
            headertext = headertext.decode('ascii')
        header = {}
        for line in headertext.split('\r\n'):
            if '=' not in line : continue
            #print '#' , line , '#'
            key,val = line.split('=')
            if key in ['NC', 'NR','NBH','NBA','NBD','ADCMAX','NP','NZ','ADCMAX' ] :
                val = int(val)
            elif key in ['AD', 'DT', ] :
                val = val.replace(',','.')
                val = float(val)
            header[key] = val

        if not lazy:
            data = np.memmap(self.filename , np.dtype('i2')  , 'r',
                  #shape = (header['NC'], header['NP']) ,
                  shape = (header['NP']/header['NC'],header['NC'], ) ,
                  offset = header['NBH'])

        for c in range(header['NC']):

            YCF = float(header['YCF%d'%c].replace(',','.'))
            YAG = float(header['YAG%d'%c].replace(',','.'))
            YZ = float(header['YZ%d'%c].replace(',','.'))

            ADCMAX = header['ADCMAX']
            AD = header['AD']
            DT = header['DT']

            if 'TU' in header:
                if header['TU'] == 'ms':
                    DT *= .001

            unit = header['YU%d'%c]
            try :
                unit = pq.Quantity(1., unit)
            except:
                unit = pq.Quantity(1., '')

            if lazy:
                signal = [ ] * unit
            else:
                signal = (data[:,header['YO%d'%c]].astype('f4')-YZ) *AD/( YCF*YAG*(ADCMAX+1)) * unit

            ana = AnalogSignal(signal,
                               sampling_rate=pq.Hz / DT,
                               t_start=0. * pq.s,
                               name=header['YN%d' % c],
                               channel_index=c)
            if lazy:
                ana.lazy_shape = header['NP']/header['NC']

            seg.analogsignals.append(ana)

        seg.create_many_to_one_relationship()
        return seg