80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
import os
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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nb_files = os.listdir(".." + os.sep + "export")
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size = len(nb_files)
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def mean_mkn() -> np.ndarray:
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averages_mkn = np.empty((size, 2))
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nb = 0
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for i in nb_files:
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data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
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rb = data[:, 4]
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total = 0.0
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for x in rb:
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total = total + x
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average = total / len(rb)
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nb_users = i.split(".")[0]
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averages_mkn[nb, 0] = int(nb_users)
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averages_mkn[nb, 1] = average
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nb += 1
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return averages_mkn
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def rb_available() -> np.ndarray:
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available = np.zeros((size, 2))
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nb = 0
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for i in nb_files:
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data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
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nb_users = i.split(".")[0]
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available[nb, 0] = int(nb_users)
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available[nb, 1] = data.shape[0] / (200 * 10000)
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nb += 1
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"""for j in range(0, 2):
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for k in range(0, 10000):
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nb_users = i.split(".")[0]
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available[nb, 0] = int(nb_users)
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if j == data[nb, 1] and k == data[:, 2]:
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available[nb, 1] += 1
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"""
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return available
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def delay() -> np.ndarray:
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delays = np.zeros((size, 2))
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nb = 0
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for i in nb_files:
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data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
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nb_users = i.split(".")[0]
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d = data[:, 5]
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for x in d:
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delays[nb, 0] = int(nb_users)
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delays[nb, 1] = float(x)
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nb += 1
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return delays
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averages = mean_mkn()
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available = rb_available()
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# Data for plotting
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averages.sort(axis=0)
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print(averages)
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fig, ax = plt.subplots()
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ax.scatter(averages[:, 0], averages[:, 1])
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ax.set(xlabel='number of users', ylabel='Efficacité spectrale', title='Efficacité spectrale')
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ax.grid()
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# fig.savefig("test.png")
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plt.show()
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fig, ax = plt.subplots()
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ax.scatter(available[:, 0], available[:, 1])
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ax.set(xlabel='number of users', ylabel='RB utilisés', title='Pourcentage de RB utilisés')
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ax.grid()
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plt.show() |