# -*- coding: utf-8 -*-
#
# cross_check_mip_corrdet.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/>.

"""
Auto- and crosscorrelation functions for spike trains
-----------------------------------------------------

A time bin of size `tbin` is centered around the time difference it
represents. If the correlation function is calculated for `tau` in
`[-tau_max, tau_max]`, the pair events contributing to the left-most
bin are those for which `tau` in `[-tau_max-tbin/2, tau_max+tbin/2)` and
so on.

Correlate two spike trains with each other assumes spike times to be ordered in
time. `tau > 0` means spike2 is later than spike1

* tau_max: maximum time lag in ms correlation function
* tbin:    bin size
* spike1:  first spike train [tspike...]
* spike2:  second spike train [tspike...]

"""

import nest
import numpy as np


def corr_spikes_sorted(spike1, spike2, tbin, tau_max, resolution):
    tau_max_i = int(tau_max / resolution)
    tbin_i = int(tbin / resolution)

    cross = np.zeros(int(2 * tau_max_i / tbin_i + 1), "d")

    j0 = 0

    for spki in spike1:
        j = j0
        while j < len(spike2) and spike2[j] - spki < -tau_max_i - tbin_i / 2.0:
            j += 1
        j0 = j

        while j < len(spike2) and spike2[j] - spki < tau_max_i + tbin_i / 2.0:
            cross[int((spike2[j] - spki + tau_max_i + 0.5 * tbin_i) / tbin_i)] += 1.0
            j += 1

    return cross


nest.ResetKernel()

resolution = 0.1  # Computation step size in ms
T = 100000.0  # Total duration
delta_tau = 10.0
tau_max = 100.0  # ms correlation window
t_bin = 10.0  # ms bin size
pc = 0.5
nu = 100.0

nest.local_num_threads = 1
nest.resolution = resolution
nest.overwrite_files = True
nest.rng_seed = 12345

# Set up network, connect and simulate
mg = nest.Create("mip_generator")
mg.set(rate=nu, p_copy=pc)

cd = nest.Create("correlation_detector")
cd.set(tau_max=tau_max, delta_tau=delta_tau)

sr = nest.Create("spike_recorder", params={"time_in_steps": True})

pn1 = nest.Create("parrot_neuron")
pn2 = nest.Create("parrot_neuron")

nest.Connect(mg, pn1)
nest.Connect(mg, pn2)
nest.Connect(pn1, sr)
nest.Connect(pn2, sr)

nest.Connect(pn1, cd, syn_spec={"weight": 1.0, "receptor_type": 0})
nest.Connect(pn2, cd, syn_spec={"weight": 1.0, "receptor_type": 1})

nest.Simulate(T)

n_events_1, n_events_2 = cd.n_events

lmbd1 = (n_events_1 / (T - tau_max)) * 1000.0
lmbd2 = (n_events_2 / (T - tau_max)) * 1000.0

spikes = sr.get("events", "senders")

sp1 = spikes[spikes == 4]
sp2 = spikes[spikes == 5]

# Find crosscorrelation
cross = corr_spikes_sorted(sp1, sp2, t_bin, tau_max, resolution)

print("Crosscorrelation:")
print(cross)
print("Sum of crosscorrelation:")
print(sum(cross))