Source code for bmtk.simulator.bionet.virtualcell

# Copyright 2017. Allen Institute. All rights reserved
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from neuron import h
import numpy as np
import pandas as pd

from bmtk.simulator.bionet.io_tools import io
from bmtk.utils.reports.spike_trains.spike_trains import SpikeTrains


class VirtualCell(object):
    """Representation of a Virtual/External node"""

    def __init__(self, node, population, spike_train_dataset):
        # VirtualCell is currently not a subclass of bionet.Cell class b/c the parent has a bunch of properties that
        # just don't apply to a virtual cell. May want to make bionet.Cell more generic in the future.
        self._node_id = node.node_id
        self._node = node
        self._population = population
        self._hobj = None
        self._spike_train_dataset = spike_train_dataset
        self._train_vec = []
        self.set_stim(node, self._spike_train_dataset)
        
    @property
    def node_id(self):
        return self._node_id

    @property
    def hobj(self):
        return self._hobj

    def set_stim(self, stim_prop, spike_train):
        """Gets the spike trains for each individual cell."""
        if isinstance(spike_train, SpikeTrains) or hasattr(spike_train, 'get_times'):
            spikes = spike_train.get_times(node_id=self.node_id)
        elif isinstance(spike_train, (list, np.ndarray, pd.Series)):
            spikes = spike_train
        elif spike_train is None:
            spikes = []
        else:
            spikes = None

        if spikes is None:
            spikes = []

        if np.any(np.array(spikes) < 0.0):
            # NRN will fail if VecStim contains negative spike-time, throw an exception and log info for user
            io.log_exception('spike train {} contains negative number, unable to run virtual cell in NEURON'.format(
                spikes
            ))

        spikes = np.sort(spikes)  # sort the spikes for NEURON, will throw a segfault if not sorted

        self._train_vec = h.Vector(spikes)
        vecstim = h.VecStim()
        vecstim.play(self._train_vec)
        self._hobj = vecstim

    def __getitem__(self, item):
        return self._node[item]