117 lines
3.3 KiB
Python
117 lines
3.3 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("PF")
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size = len(nb_files)
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def mean_mkn(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
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averages_mkn = np.empty((size, 2))
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nb = 0
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for nb_users, data in arr:
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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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averages_mkn[nb, 0] = 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(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
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available = np.zeros((size, 2))
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nb = 0
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for nb_users, data in arr:
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available[nb, 0] = nb_users
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available[nb, 1] = (data.shape[0] / (200 * 20000)) * 100
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nb += 1
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return available
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def delay(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
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delays = np.zeros((size, 2))
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nb = 0
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for nb_users, data in arr:
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d = data[:, 5]
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for x in d:
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delays[nb, 0] = 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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def rb_allocate_distance(arr: list[tuple[int, np.ndarray]], distance) -> np.ndarray:
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allocate = np.zeros((size, 2))
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nb = 0
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arr.sort()
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for nb_users, data in arr:
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n = 0
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for x in data[:,6]:
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if int(x) == distance:
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n+=1
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allocate[nb, 0] = nb_users
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allocate[nb, 1] = n
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# print(n/data.shape[0])
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nb += 1
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return allocate
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np_arr: list[tuple[int, np.ndarray]] = list()
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for i in nb_files:
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np_arr.append((int(i.split(".")[0]), pd.read_csv("PF" + os.sep + i, delimiter=';').to_numpy()))
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averages = mean_mkn(np_arr)
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available = rb_available(np_arr)
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allocate_lp1 = rb_allocate_distance(np_arr, 100)
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allocate_lp2 = rb_allocate_distance(np_arr, 500)
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allocate_total = allocate_lp1[:, 1] + allocate_lp2[:, 1]
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delays = delay(np_arr)
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delays.sort(axis=0)
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# Data for plotting
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averages.sort(axis=0)
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available.sort(axis=0)
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del np_arr
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fig, ax = plt.subplots(2, 2)
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ax[0, 0].plot(averages[:, 0], averages[:, 1], marker="o")
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ax[0, 0].set(xlabel='number of users', ylabel='% Spectral efficiency', title='Spectral efficiency PF')
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ax[0, 0].grid()
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ax[0, 0].set_ylim([0, 40])
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ax[0, 1].plot(available[:, 0], available[:, 1], marker="o")
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ax[0, 1].set(xlabel='number of users', ylabel=' % RB used', title='Percentage of RB used PF')
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ax[0, 1].grid()
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ax[0, 1].set_ylim([0, 105])
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ax[1, 0].plot(delays[:, 0], delays[:, 1], marker="o")
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ax[1, 0].set(xlabel='number of users', ylabel='delay(ms)', title='Delay PF')
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ax[1, 0].grid()
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available.sort(axis=0)
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#ax[1, 1].scatter(allocate_lp1[:, 0], (allocate_lp1[:, 1]/(allocate_lp1[:, 1])+allocate_lp2[:, 1])*100)
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ax[1, 1].plot(available[:, 0], (allocate_lp1[:, 1]/(allocate_lp1[:, 1]+allocate_lp2[:, 1])*100), label="100 meters group")
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#ax[1, 1].scatter(allocate_lp2[:, 0], (allocate_lp2[:, 1]/(allocate_lp1[:, 1])+allocate_lp2[:, 1])*100)
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#ax[1, 1].plot(available[:, 0], available[:, 1], marker="o", label="RB used")
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ax[1, 1].plot(available[:, 0], (allocate_lp2[:, 1]/(allocate_lp1[:, 1]+allocate_lp2[:, 1])*100), label="500 meters group")
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ax[1, 1].set(xlabel='number of users', ylabel='% RB used', title='RB used depending on the distance PF')
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ax[1, 1].grid()
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ax[1, 1].set_ylim([0, 105])
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ax[1, 1].legend(loc="upper left")
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plt.show()
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