import math
import re
import statistics
from collections import defaultdict

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.artist import setp

import util


def load_results(filename) -> dict:
    results = defaultdict(list)
    with open(f"results/{filename}", "r") as file:
        for line in file:
            if "error" in line:
                continue
            values = line.split()
            keys = [
                ("num_servers", int),
                ("database_size", int),
                ("block_size", int),
                ("protocol_name", str),
                ("total_cpu_time", int),
                ("bits_sent", int),
                ("bits_received", int)
            ]
            d = dict([(a[0], a[1](b)) for a, b in zip(keys, values)])
            results[(d["num_servers"], d["database_size"], d["block_size"], d["protocol_name"])].append(d)
    return results


def clean_results(results) -> dict:
    cleaned_results = defaultdict(list)
    for test, result in results.items():
        cpu_time = statistics.mean(sorted([int(r["total_cpu_time"]) for r in result]))
        bits_sent = statistics.mean(sorted([int(r["bits_sent"]) for r in result]))
        bits_received = statistics.mean(sorted([int(r["bits_received"]) for r in result]))
        cleaned_results[result[0]["protocol_name"]].append({
            **result[0],
            "total_cpu_time": cpu_time,
            "bits_sent": bits_sent,
            "bits_received": bits_received
        })
    return cleaned_results


def filter_results(results: dict, func: callable):
    return {protocol_name: [r for r in results if func(r)]
            for protocol_name, results in results.items()}


def save_fig(plt, title):
    clean_title = re.sub(r"\W", r"_", title)
    plt.savefig(f"plots/{clean_title}.pdf")


def with_bandwidth(result: dict, bandwidth=10):
    return max(1, result["total_cpu_time"] + ((result["bits_sent"] + result["bits_received"]) / (bandwidth * 1000)))  # 1000 bits/ms = 1 Mbit/s


def plot(all_results: dict, y_func: callable, x_func: callable, title=None, y_label=None, x_label=None,
         logx=False, logy=False, scatter=False):
    fig, ax = plt.subplots()
    for protocol_name, results in all_results.items():
        sorted_results = sorted(results, key=lambda r: x_func(r))
        if scatter:
            plot_func = ax.scatter
        else:
            plot_func = ax.plot
        plot_func(
            [x_func(r) for r in sorted_results],
            [y_func(r) for r in sorted_results],
            label=protocol_name.replace("_", " ")
        )

    #for results in all_results.values():
    #    for r in results:
    #        ax.annotate(f"{r['database_size']}, {r['block_size']}", (x_func(r), y_func(r)), fontsize=3)

    if logx:
        ax.set_xscale("log", basex=2)
    if logy:
        ax.set_yscale("log", basey=2)

    if x_label is not None:
        plt.xlabel(x_label)
    if y_label is not None:
        plt.ylabel(y_label)

    plt.legend(loc="upper left")
    #if title is not None:
    #    plt.title(title)

    save_fig(plt, title)
    #plt.show()


def plot_3x_with_simulated_bandwidth(all_results: dict, title: str):
    ax1 = plt.subplot(121)
    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)

    ax1.set_ylabel("Time (ms)")
    setp(ax2.get_yticklabels(), visible=False)
    ax1.set_xlabel("Total Database Size (bits)")
    ax2.set_xlabel("Total Database Size (bits)")

    for ax in (ax1, ax2):
        ax.tick_params("y")
        ax.set_xscale("log", basex=2)
        ax.set_yscale("log", basey=2)

    ax1.set_title("10 Mbit/s)")
    ax2.set_title("100 Mbit/s")

    for protocol_name, results in all_results.items():
        x_func = lambda r: r["database_size"] * r["block_size"]
        sorted_results = sorted(results, key=lambda r: x_func(r))
        ax1.plot(
            [x_func(r) for r in sorted_results],
            [with_bandwidth(r, 10) for r in sorted_results],
            label=protocol_name.replace("_", " ")
        )
        ax2.plot(
            [x_func(r) for r in sorted_results],
            [with_bandwidth(r, 100) for r in sorted_results],
            label=protocol_name.replace("_", " ")
        )

    ax1.legend(loc="upper left")
    # fig.subplots_adjust(wspace=0)
    save_fig(plt, title)
    #plt.show()


def plot_send_receive(all_results: dict, title: str):
    ax1 = plt.subplot(121)
    ax2 = plt.subplot(122, sharex=ax1)

    ax1.set_ylabel("Sent (bits)")
    ax2.set_ylabel("Received (bits)")
    setp(ax2.get_yticklabels(), visible=False)
    ax2.yaxis.set_label_position("left")

    for ax in (ax1, ax2):
        ax.set_xlabel("Total Database Size (bits)")
        ax.tick_params("y")
        ax.set_xscale("log", basex=2)
        ax.set_yscale("log", basey=2)

    for protocol_name, results in all_results.items():
        x_func = lambda r: r["database_size"] * r["block_size"]
        sorted_results = sorted(results, key=lambda r: x_func(r))
        ax1.plot(
            [x_func(r) for r in sorted_results],
            [max(1, r["bits_sent"]) for r in sorted_results],
            label=protocol_name.replace("_", " ")
        )
        ax2.plot(
            [x_func(r) for r in sorted_results],
            [max(1, r["bits_received"]) for r in sorted_results],
            label=protocol_name.replace("_", " ")
        )

    ax1.legend(loc="upper left")
    # fig.subplots_adjust(wspace=0)
    save_fig(plt, title)
    #plt.show()


def matrixify(results: list, x_func: callable, y_func: callable, z_func: callable):
    x_labels = list(sorted(set(x_func(r) for r in results)))
    y_labels = list(sorted(set(y_func(r) for r in results)))

    data = {y: {x: 0 for x in x_labels}
            for y in y_labels}

    for r in results:
        data[y_func(r)][x_func(r)] = z_func(r)

    return np.array([list(y.values()) for y in data.values()]), x_labels, y_labels


def plot_scheme_heatmap(results: list, title: str, bandwidth: int):
    data, x_labels, y_labels = matrixify(
        results,
        x_func=lambda r: r["database_size"],
        y_func=lambda r: r["block_size"],
        z_func=lambda r: with_bandwidth(r, bandwidth) #/ r["block_size"]
    )

    im, cbar = util.heatmap(
        data,
        [f"$2^{{{int(math.log2(y))}}}$" for y in y_labels],
        [f"$2^{{{int(math.log2(x))}}}$" for x in x_labels],
        xlabel="Database Size (#records)",
        ylabel="Block Size (bits)",
        cbarlabel="Time (ms)",
        logcolor=True,
        origin="lower",
    )
    save_fig(plt, title)


def plot_old_vs_new_heatmap(all_results: dict, old_func: callable, new_func: callable, title: str):
    data_old, x_labels, y_labels = matrixify(
        old_func(all_results),
        x_func=lambda r: r["database_size"],
        y_func=lambda r: r["block_size"],
        z_func=lambda r: with_bandwidth(r, 10) #/ r["block_size"]
    )
    data_new, x_labels, y_labels = matrixify(
        new_func(all_results),
        x_func=lambda r: r["database_size"],
        y_func=lambda r: r["block_size"],
        z_func=lambda r: with_bandwidth(r, 10) #/ r["block_size"]
    )

    def calc(i, j):
        try:
            if (data_new[i, j], data_old[i, j]) == (0, 0):
                return 0
            diff = data_new[i, j] - data_old[i, j]
            if diff == 0:
                return 1
            return diff
        except IndexError:
            return 0

    im, cbar = util.heatmap(
        np.array([[calc(i, j) for j, y in enumerate(x)] for i, x in enumerate(data_new)]),
        [f"$2^{{{int(math.log2(y))}}}$" for y in y_labels],
        [f"$2^{{{int(math.log2(x))}}}$" for x in x_labels],
        xlabel="Database Size (#records)",
        ylabel="Block Size (bits)",
        cbarlabel="Time Difference (ms)",
        sym_logcolor=True,
        origin="lower",
    )
    save_fig(plt, title)


def main():
    # Simple CPU Time
    plot(
        filter_results(clean_results(load_results("results_combined.log")), lambda r: r["block_size"] == 1),
        y_label="Time (ms)",
        x_label="Total Database Size (bits)",
        title="Computation Time - 1-bit Block Size",
        y_func=lambda r: max(1, r["total_cpu_time"]),
        x_func=lambda r: r["database_size"] * r["block_size"],
        logx=True,
        logy=True
    )
    plt.close()
    # ... with simulated bandwidth, e.g. estimated total real time
    plot_3x_with_simulated_bandwidth(
        filter_results(clean_results(load_results("results_combined.log")), lambda r: r["block_size"] == 1),
        title="Total Time with Simulated Bandwidth - 1-bit Block Size"
    )

    # CPU Time per bit as a function of block/database-ratio
    #plot(
    #    filter_results(clean_results(load_results("results_combined.log")),
    #                   lambda r: r["protocol_name"] != "Interpolation" and r["database_size"] * r["block_size"] > 1024),
    #    y_label="Time (ms)",
    #    x_label="Block Size / Database Size (ratio)",
    #    title="Computation Time per bit - Block Size / Database Size Ratio",
    #    y_func=lambda r: max(1, r["total_cpu_time"] / (r["database_size"] * r["block_size"])),
    #    x_func=lambda r: r["block_size"] / r["database_size"],
    #    logx=True
    #)
    plt.close()

    # Simple Network Traffic
    plot_send_receive(
        filter_results(clean_results(load_results("results_combined.log")), lambda r: r["block_size"] == 1),
        title="Network Traffic - 1-bit Block Size"
    )

    # Scatter-plot of total real-time (cpu + simulated bandwidth), varying both block size and database size
    #plot(
    #    clean_results(load_results("results_fast_var-bs_var-db.log")),
    #    y_label="Time (ms)",
    #    x_label="Total Database Size (bits)",
    #    title="Total Time with Simulated Bandwidth - Varying Block and Database Size",
    #    y_func=lambda r: max(1, r["total_cpu_time"] + ((r["bits_sent"]+r["bits_received"])/(10*1000))),  # 1000 bits/ms = 1 Mbit/s
    #    x_func=lambda r: r["database_size"] * r["block_size"],
    #    scatter=True
    #)
    plt.close()

    # 2D Heatmap of CPU time for Simple/XOR/Balanced XOR - varying both database size and block size
    plot_scheme_heatmap(
        clean_results(load_results("results_fast_var-bs_var-db.log"))["Send_All"],
        title="Total Simulated Time Heatmap: Send All - Varying Database Size and Block Size - 10Mbit/s",
        bandwidth=10
    )
    plt.close()
    plot_scheme_heatmap(
        clean_results(load_results("results_fast_var-bs_var-db.log"))["XOR"],
        title="Total Simulated Time Heatmap: XOR - Varying Database Size and Block Size - 10Mbit/s",
        bandwidth=10
    )
    plt.close()
    plot_scheme_heatmap(
        clean_results(load_results("results_fast_var-bs_var-db.log"))["Balanced_XOR"],
        title="Total Simulated Time Heatmap: Balanced XOR - Varying Database Size and Block Size - 10Mbit/s",
        bandwidth=10
    )
    plt.close()

    # 2D Heatmaps of Schemes Versus (CPU + simulated bandwidth), varying both block size and database size
    plot_old_vs_new_heatmap(
        clean_results(load_results("results_fast_var-bs_var-db.log")),
        old_func=lambda rs: rs["Send_All"],
        new_func=lambda rs: rs["Balanced_XOR"],
        title="Total Simulated Time Heatmap: Send All vs Balanced XOR - Varying Database Size and Block Size - 10 Mbit/s"
    )
    plt.close()
    plot_old_vs_new_heatmap(
        clean_results(load_results("results_fast_var-bs_var-db.log")),
        old_func=lambda rs: rs["XOR"],
        new_func=lambda rs: rs["Balanced_XOR"],
        title="Total Simulated Time Heatmap: XOR vs Balanced XOR - Varying Database Size and Block Size - 10 Mbit/s"
    )
    plt.close()


if __name__ == '__main__':
    main()