"""Starting script to run a network simulation of the clustered network
by Litwin-Kumar and Doiron (Nature neuroscience 2012).

The network has been implemented using the *pypet* network framework.

"""

import os  # To allow path names work under Windows and Linux

import brian2
import numpy as np

brian2.prefs.codegen.target = "numpy"

from clusternet import (
    CNConnections,
    CNFanoFactorComputer,
    CNMonitorAnalysis,
    CNNetworkRunner,
    CNNeuronGroup,
)

from pypet.brian2.network import NetworkManager
from pypet.environment import Environment


def main():
    filename = os.path.join("hdf5", "Clustered_Network.hdf5")
    env = Environment(
        trajectory="Clustered_Network",
        add_time=False,
        filename=filename,
        continuable=False,
        lazy_debug=False,
        multiproc=True,
        ncores=4,
        use_pool=False,  # We cannot use a pool, our network cannot be pickled
        wrap_mode="QUEUE",
        overwrite_file=True,
    )

    # Get the trajectory container
    traj = env.trajectory

    # We introduce a `meta` parameter that we can use to easily rescale our network
    scale = 1.0  # To obtain the results from the paper scale this to 1.0
    # Be aware that your machine will need a lot of memory then!
    traj.f_add_parameter(
        "simulation.scale",
        scale,
        comment="Meta parameter that can scale default settings. "
        "Rescales number of neurons and connections strenghts, but "
        "not the clustersize.",
    )

    # We create a Manager and pass all our components to the Manager.
    # Note the order, CNNeuronGroups are scheduled before CNConnections,
    # and the Fano Factor computation depends on the CNMonitorAnalysis
    clustered_network_manager = NetworkManager(
        network_runner=CNNetworkRunner(),
        component_list=(CNNeuronGroup(), CNConnections()),
        analyser_list=(CNMonitorAnalysis(), CNFanoFactorComputer()),
    )

    # Add original parameters (but scaled according to `scale`)
    clustered_network_manager.add_parameters(traj)

    # We need `tolist` here since our parameter is a python float and not a
    # numpy float.
    explore_list = np.arange(1.0, 3.5, 0.4).tolist()
    # Explore different values of `R_ee`
    traj.f_explore({"R_ee": explore_list})

    # Pre-build network components
    clustered_network_manager.pre_build(traj)

    # Run the network simulation
    traj.f_store()  # Let's store the parameters already before the run
    env.run(clustered_network_manager.run_network)

    # Finally disable logging and close all log-files
    env.disable_logging()


if __name__ == "__main__":
    main()