# -*- coding: utf-8 -*-
#
# gap_junctions_two_neurons.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST.  If not, see <http://www.gnu.org/licenses/>.

"""
Gap Junctions: Two neuron example
---------------------------------

This script simulates two Hodgkin-Huxley neurons of type ``hh_psc_alpha_gap``
connected by a gap junction. Both neurons receive a constant current of
100.0 pA. The neurons are initialized with different membrane potentials and
synchronize over time due to the gap-junction connection.

"""

import matplotlib.pyplot as plt
import nest
import numpy

nest.ResetKernel()

###############################################################################
# First we set the resolution of the simulation, create two neurons and
# create a ``voltmeter`` for recording.

nest.resolution = 0.05

neuron = nest.Create("hh_psc_alpha_gap", 2)

vm = nest.Create("voltmeter", params={"interval": 0.1})

###############################################################################
# Then we set the constant current input, modify the inital membrane
# potential of one of the neurons and connect the neurons to the ``voltmeter``.

neuron.I_e = 100.0
neuron[0].V_m = -10.0

nest.Connect(vm, neuron, "all_to_all")

###############################################################################
# In order to create the ``gap_junction`` connection we employ the
# ``all_to_all`` connection rule: Gap junctions are bidirectional connections,
# therefore we need to connect `neuron[0]` to `neuron[1]` and `neuron[1]` to
# `neuron[0]`:

nest.Connect(
    neuron, neuron, {"rule": "all_to_all", "allow_autapses": False}, {"synapse_model": "gap_junction", "weight": 0.5}
)

###############################################################################
# Finally we start the simulation and plot the membrane potentials of both
# neurons.

nest.Simulate(351.0)

senders = vm.events["senders"]
times = vm.events["times"]
v_m_values = vm.events["V_m"]

plt.figure(1)
plt.plot(times[numpy.where(senders == 1)], v_m_values[numpy.where(senders == 1)], "r-")
plt.plot(times[numpy.where(senders == 2)], v_m_values[numpy.where(senders == 2)], "g-")
plt.xlabel("time (ms)")
plt.ylabel("membrane potential (mV)")
plt.show()