import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

nb_files = os.listdir("PF")

size = len(nb_files)


def mean_mkn(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
    averages_mkn = np.empty((size, 2))
    nb = 0
    for nb_users, data in arr:
        rb = data[:, 4]

        total = 0.0
        for x in rb:
            total = total + x
        average = total / len(rb)
        averages_mkn[nb, 0] = nb_users
        averages_mkn[nb, 1] = average
        nb += 1
    return averages_mkn


def rb_available(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
    available = np.zeros((size, 2))
    nb = 0
    for nb_users, data in arr:
        available[nb, 0] = nb_users
        available[nb, 1] = (data.shape[0] / (200 * 20000)) * 100
        nb += 1
    return available


def delay(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
    delays = np.zeros((size, 2))
    nb = 0
    for nb_users, data in arr:
        d = data[:, 5]
        for x in d:
            delays[nb, 0] = nb_users
            delays[nb, 1] = float(x)
        nb += 1
    return delays


def rb_allocate_distance(arr: list[tuple[int, np.ndarray]], distance) -> np.ndarray:
    allocate = np.zeros((size, 2))
    nb = 0
    arr.sort()
    for nb_users, data in arr:
        n = 0
        for x in data[:,6]:
            if int(x) == distance:
                n+=1
        allocate[nb, 0] = nb_users
        allocate[nb, 1] = n
        # print(n/data.shape[0])
        nb += 1
    return allocate


np_arr: list[tuple[int, np.ndarray]] = list()
for i in nb_files:
    np_arr.append((int(i.split(".")[0]), pd.read_csv("PF" + os.sep + i, delimiter=';').to_numpy()))

averages = mean_mkn(np_arr)
available = rb_available(np_arr)

allocate_lp1 = rb_allocate_distance(np_arr, 100)
allocate_lp2 = rb_allocate_distance(np_arr, 500)
allocate_total = allocate_lp1[:, 1] + allocate_lp2[:, 1]

delays = delay(np_arr)
delays.sort(axis=0)
# Data for plotting
averages.sort(axis=0)
available.sort(axis=0)

del np_arr

fig, ax = plt.subplots(2, 2)
ax[0, 0].plot(averages[:, 0], averages[:, 1], marker="o")
ax[0, 0].set(xlabel='number of users', ylabel='% Spectral efficiency', title='Spectral efficiency PF')
ax[0, 0].grid()
ax[0, 0].set_ylim([0, 40])

ax[0, 1].plot(available[:, 0], available[:, 1], marker="o")
ax[0, 1].set(xlabel='number of users', ylabel=' % RB used', title='Percentage of RB used PF')
ax[0, 1].grid()
ax[0, 1].set_ylim([0, 105])

ax[1, 0].plot(delays[:, 0], delays[:, 1], marker="o")
ax[1, 0].set(xlabel='number of users', ylabel='delay(ms)', title='Delay PF')
ax[1, 0].grid()

available.sort(axis=0)

#ax[1, 1].scatter(allocate_lp1[:, 0], (allocate_lp1[:, 1]/(allocate_lp1[:, 1])+allocate_lp2[:, 1])*100)

ax[1, 1].plot(available[:, 0], (allocate_lp1[:, 1]/(allocate_lp1[:, 1]+allocate_lp2[:, 1])*100), label="100 meters group")

#ax[1, 1].scatter(allocate_lp2[:, 0], (allocate_lp2[:, 1]/(allocate_lp1[:, 1])+allocate_lp2[:, 1])*100)

#ax[1, 1].plot(available[:, 0], available[:, 1], marker="o", label="RB used")

ax[1, 1].plot(available[:, 0], (allocate_lp2[:, 1]/(allocate_lp1[:, 1]+allocate_lp2[:, 1])*100), label="500 meters group")

ax[1, 1].set(xlabel='number of users', ylabel='% RB used', title='RB used depending on the distance PF')
ax[1, 1].grid()
ax[1, 1].set_ylim([0, 105])
ax[1, 1].legend(loc="upper left")

plt.show()