Python SpikeTrain.annotations['cluster']方法代码示例

# 需要导入模块: from neo.core import SpikeTrain [as 别名]
# 或者: from neo.core.SpikeTrain import annotations['cluster'] [as 别名]
    def read_block(self, lazy=False):
        """Returns a Block containing spike information.

        There is no obvious way to infer the segment boundaries from
        raw spike times, so for now all spike times are returned in one
        big segment. The way around this would be to specify the segment
        boundaries, and then change this code to put the spikes in the right
        segments.
        """
        assert not lazy, 'Do not support lazy'

        # Create block and segment to hold all the data
        block = Block()
        # Search data directory for KlustaKwik files.
        # If nothing found, return empty block
        self._fetfiles = self._fp.read_filenames('fet')
        self._clufiles = self._fp.read_filenames('clu')
        if len(self._fetfiles) == 0:
            return block

        # Create a single segment to hold all of the data
        seg = Segment(name='seg0', index=0, file_origin=self.filename)
        block.segments.append(seg)

        # Load spike times from each group and store in a dict, keyed
        # by group number
        self.spiketrains = dict()
        for group in sorted(self._fetfiles.keys()):
            # Load spike times
            fetfile = self._fetfiles[group]
            spks, features = self._load_spike_times(fetfile)

            # Load cluster ids or generate
            if group in self._clufiles:
                clufile = self._clufiles[group]
                uids = self._load_unit_id(clufile)
            else:
                # unclustered data, assume all zeros
                uids = np.zeros(spks.shape, dtype=np.int32)

            # error check
            if len(spks) != len(uids):
                raise ValueError("lengths of fet and clu files are different")

            # Create Unit for each cluster
            unique_unit_ids = np.unique(uids)
            for unit_id in sorted(unique_unit_ids):
                # Initialize the unit
                u = Unit(name=('unit %d from group %d' % (unit_id, group)),
                         index=unit_id, group=group)

                # Initialize a new SpikeTrain for the spikes from this unit
                st = SpikeTrain(
                    times=spks[uids == unit_id] / self.sampling_rate,
                    units='sec', t_start=0.0,
                    t_stop=spks.max() / self.sampling_rate,
                    name=('unit %d from group %d' % (unit_id, group)))
                st.annotations['cluster'] = unit_id
                st.annotations['group'] = group

                # put features in
                if len(features) != 0:
                    st.annotations['waveform_features'] = features

                # Link
                u.spiketrains.append(st)
                seg.spiketrains.append(st)

        block.create_many_to_one_relationship()
        return block