ALLEN INSTITUTE

# Source code for bmtk.simulator.bionet.cell

    # Copyright 2017. Allen Institute. All rights reserved
    #
    # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
    # following conditions are met:
    #
    # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
    # disclaimer.
    #
    # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
    # disclaimer in the documentation and/or other materials provided with the distribution.
    #
    # 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
    # products derived from this software without specific prior written permission.
    #
    # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
    # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
    # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
    # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
    # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
    # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
    # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
    #
    from neuron import h
    import numpy as np

    pc = h.ParallelContext()  # object to access MPI methods
    MPI_RANK = int(pc.id())

    class Cell(object):
        """A abstract base class for any cell object.

        A base class for implementation of a cell-type objects like biophysical cells, LIF cells, etc. Do not instantiate
        a Cell object directly. Cell classes act as wrapper around HOC cell object with extra functionality for setting
        positions, synapses, and other parameters depending on the desired cell class.
        """
        def __init__(self, node, population_name, network=None):
            self._node = node
            self._network = network

            self._gid = network.gid_pool.get_gid(name=population_name, node_id=node.node_id)
            self._node_id = node.node_id
            self._props = node
            self._netcons = []  # list of NEURON network connection object attached to this cell

            self._pos_soma = []
            self.set_soma_position()

            # register the cell
            pc.set_gid2node(self.gid, MPI_RANK)

            # Load the NEURON HOC object
            self._hobj = node.load_cell()

            self._edge_props = []

        @property
        def node(self):
            return self._node

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

        @property
        def gid(self):
            return self._gid

        @property
        def node_id(self):
            return self._node_id

        @property
        def group_id(self):
            return self._node.group_id

        @property
        def network_name(self):
            return self._node.network

        @property
        def netcons(self):
            return self._netcons

        @property
        def soma_position(self):
            return self._pos_soma

        def set_soma_position(self):
            positions = self._node.position
            if positions is not None:
                self._pos_soma = positions.reshape(3, 1)

        def init_connections(self):
            self.rand_streams = []
            self.prng = np.random.RandomState(self.gid)  # generate random stream based on gid

        def scale_weights(self, factor):
            for nc in self.netcons:
                weight = nc.weight[0]
                nc.weight[0] = weight*factor

        def get_connection_info(self):
            return []

        def set_syn_connections(self, edge_prop, src_node, stim=None):
            raise NotImplementedError

        def get_section(self, sec_name, sec_index):
            raise NotImplementedError

        def __contains__(self, node_prop):
            return node_prop in self._node

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