import json

import numpy as np
from matplotlib import pyplot as plt

from processing.post_process import get_avg_list, get_iperf_lists


def plot_median_rsrp(filename, sort=False):
    # Load the JSON file
    with open(filename, "r") as file:
        data = json.load(file)

    # Extract distance and RSRP values (convert RSRP values to integers)
    distances, rsrps = get_avg_list(
        data=data, entry_type="RSRP", default_disconnect=-169
    )

    if sort:
        indices = np.argsort(distances)
        distances = [distances[i] for i in indices]
        rsrps = [rsrps[i] for i in indices]

    # Plot the data
    plt.figure(figsize=(10, 6))
    plt.plot(
        distances,
        rsrps,
        label="Avg RSRP RX",
        marker="o",
        color="mediumblue",
    )

    plt.title("RSRP vs Distance")
    plt.xlabel("Distance (m)")
    plt.ylabel("RSRP (dBm)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()

    # Show the plot
    plt.show()


def plot_median_rsrq(filename, sort=False):
    # Load the JSON file
    with open(filename, "r") as file:
        data = json.load(file)

    # Extract distance and RSRQ values (convert RSRQ values to integers)
    distances, rsrqs = get_avg_list(
        data=data, entry_type="RSRQ", default_disconnect=-20
    )

    if sort:
        indices = np.argsort(distances)
        distances = [distances[i] for i in indices]
        rsrqs = [rsrqs[i] for i in indices]

    # Plot the data
    plt.figure(figsize=(10, 6))
    plt.plot(distances, rsrqs, label="Avg RSRQ RX", marker="o", color="mediumblue")

    plt.title("RSRQ vs Distance")
    plt.xlabel("Distance (m)")
    plt.ylabel("RSRQ (dBm)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()

    # Show the plot
    plt.show()


def plot_double_iperf(filename, ip_address, sort=False):
    # Load the JSON file
    with open(filename, "r") as file:
        data = json.load(file)

    distances, reverse_distances, sender, reverse_sender, receiver, reverse_receiver = (
        get_iperf_lists(data, ip_address)
    )

    if sort:
        try:
            indices = np.argsort(distances)
            distances = [distances[i] for i in indices]
            sender = [sender[i] for i in indices]
            receiver = [receiver[i] for i in indices]

            reverse_indices = np.argsort(reverse_distances)
            reverse_distances = [reverse_distances[i] for i in reverse_indices]
            reverse_sender = [reverse_sender[i] for i in reverse_indices]
            reverse_receiver = [reverse_receiver[i] for i in reverse_indices]
        except IndexError:
            pass

    # Plot the data
    plt.figure(figsize=(10, 6))
    plt.plot(
        distances,
        receiver,
        label="Uplink Bitrate",
        marker="s",
        color="red",
    )
    plt.plot(
        reverse_distances,
        reverse_receiver,
        label="Downlink Bitrate",
        marker="d",
        color="mediumblue",
    )

    name = ip_address
    if ip_address == "10.45.0.1":
        name = "End to Relay"
    elif ip_address == "10.46.0.1":
        name = "End to Ground"

    plt.title(f"IPERF vs Distance ({name})")
    plt.xlabel("Distance (m)")
    plt.ylabel("Bitrate (Mbits/s)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()

    # Show the plot
    plt.show()


if __name__ == "__main__":
    # python -m processing.report_plots

    filenames = [
        "/home/madrigal/repos/range-testing/data/boat_relay_oct_9/w_locations/test_1760031451.json",
    ]

    for filename in filenames:
        plot_double_iperf(filename=filename, ip_address="10.46.0.1", sort=True)
        plot_double_iperf(filename=filename, ip_address="10.45.0.1", sort=True)
        plot_median_rsrp(filename=filename, sort=True)
        plot_median_rsrq(filename=filename, sort=True)