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

nb_files = os.listdir(".." + os.sep + "export")

size = len(nb_files)


def mean_mkn() -> np.ndarray:
    averages_mkn = np.empty((size, 2))
    nb = 0
    for i in nb_files:
        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
        rb = data[:, 4]

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


def rb_available() -> np.ndarray:
    available = np.zeros((size, 2))
    nb = 0
    for i in nb_files:
        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
        nb_users = i.split(".")[0]
        available[nb, 0] = int(nb_users)
        available[nb, 1] = (data.shape[0] / (200 * 10000)) * 100
        nb += 1
    return available

def delay() -> np.ndarray:
    delays = np.zeros((size, 2))
    nb = 0
    for i in nb_files:
        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
        nb_users = i.split(".")[0]
        d = data[:, 5]
        for x in d:
            delays[nb, 0] = int(nb_users)
            delays[nb, 1] = float(x)
        nb += 1
    return delays

averages = mean_mkn()
available = rb_available()
# Data for plotting
averages.sort(axis=0)

print(averages)
fig, ax = plt.subplots()
ax.scatter(averages[:, 0], averages[:, 1])

ax.set(xlabel='number of users', ylabel='Efficacité spectrale', title='Efficacité spectrale')
ax.grid()

# fig.savefig("test.png")
plt.show()

fig, ax = plt.subplots()
ax.scatter(available[:, 0], available[:, 1])
ax.set(xlabel='number of users', ylabel='RB utilisés', title='Pourcentage de RB utilisés')
ax.grid()
plt.show()