import typing

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import signal
from timeit import default_timer
from tqdm import tqdm
from dataclasses import dataclass
from types import MappingProxyType

from utility import codes, noise, misc
from utility.simulation.simulators import GenericMultithreadedSimulator

from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder


def count_bit_errors(d: np.array, d_hat: np.array) -> int:
    return np.sum(d != d_hat)


def task_func(params):
    """Function called by the GenericMultithreadedSimulator instance.

    Calculate the BER, FER, and DFR  for a given SNR and gamma.
    """
    signal.signal(signal.SIGINT, signal.SIG_IGN)

    decoder = params["decoder"]
    max_iterations = params["max_iterations"]
    SNR = params["SNR"]
    n = params["n"]
    k = params["k"]

    c = np.zeros(n)
    x_bpsk = c + 1

    total_bit_errors = 0
    total_frame_errors = 0
    dec_fails = 0

    num_iterations = 0

    for i in range(max_iterations):
        x = noise.add_awgn(x_bpsk, SNR, n, k)
        x_hat, k_max = decoder.decode(x)

        bit_errors = count_bit_errors(x_hat, c)
        if bit_errors > 0:
            total_bit_errors += bit_errors
            total_frame_errors += 1

        num_iterations += 1

        if k_max == -1:
            dec_fails += 1

        if total_frame_errors > 100:
            break

    BER = total_bit_errors / (num_iterations * n)
    FER = total_frame_errors / num_iterations
    DFR = dec_fails / (num_iterations + dec_fails)

    return {"BER": BER, "FER": FER, "DFR": DFR,
            "num_iterations": num_iterations}


def simulate(H_file, SNRs, max_iterations, omega, K, gammas):
    sim = GenericMultithreadedSimulator()

    # Define fixed simulation params

    H = codes.read_alist_file(f"res/{H_file}")
    n_min_k, n = H.shape
    k = n - n_min_k

    # Define params different for each task

    task_params = []
    for i, SNR in enumerate(SNRs):
        for j, gamma in enumerate(gammas):
            decoder = ProximalDecoder(H=H.astype('int32'), K=K, omega=omega,
                                      gamma=gamma)

            task_params.append(
                {"decoder": decoder, "max_iterations": max_iterations,
                 "SNR": SNR, "gamma": gamma, "n": n, "k": k})

    # Set up simulation

    sim.task_params = task_params
    sim.task_func = task_func

    sim.start_or_continue()

    return sim.current_results


def main():
    # Set up simulation params

    sim_name = "2d_BER_FER_DFR"

    # H_file = "BCH_7_4.alist"
    # H_file = "BCH_31_11.alist"
    # H_file = "BCH_31_26.alist"
    # H_file = "96.3.965.alist"
    H_file = "204.33.486.alist"
    # H_file = "204.33.484.alist"
    # H_file = "204.55.187.alist"
    # H_file = "408.33.844.alist"

    SNRs = np.arange(1, 6, 0.5)
    max_iterations = 20000
    omega = 0.05
    K = 100
    gammas = np.arange(0.0, 0.17, 0.01)

    # Run simulation

    start_time = default_timer()
    results = simulate(H_file, SNRs, max_iterations, omega, K, gammas)
    end_time = default_timer()

    print(f"duration: {end_time - start_time}")

    df = misc.pgf_reformat_data_3d(results=results, x_param_name="SNR",
                                   y_param_name="gamma",
                                   z_param_names=["BER", "FER", "DFR",
                                                  "num_iterations"])

    # df.sort_values(by=["gamma", "SNR"]).to_csv(
    #     f"sim_results/{sim_name}_{misc.slugify(H_file)}.csv", index=False)

    sns.set_theme()
    ax = sns.lineplot(data=df, x="SNR", y="BER", hue="gamma")
    ax.set_yscale('log')
    ax.set_ylim((5e-5, 2e-0))
    plt.show()


if __name__ == "__main__":
    main()