From 4f110cc7cb1e745d7843e098bfd5b3ef4b333033 Mon Sep 17 00:00:00 2001 From: Tr1xt4n Date: Fri, 14 Apr 2023 21:01:23 +0200 Subject: [PATCH] plot legend --- plot/main.py | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/plot/main.py b/plot/main.py index e4c3667..78b972f 100644 --- a/plot/main.py +++ b/plot/main.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd -nb_files = os.listdir(".." + os.sep + "export") +nb_files = os.listdir("PF") size = len(nb_files) @@ -29,7 +29,7 @@ def rb_available(arr: list[tuple[int, np.ndarray]]) -> np.ndarray: nb = 0 for nb_users, data in arr: available[nb, 0] = nb_users - available[nb, 1] = (data.shape[0] / (200 * 10000)) * 100 + available[nb, 1] = (data.shape[0] / (200 * 20000)) * 100 nb += 1 return available @@ -64,12 +64,12 @@ def rb_allocate_distance(arr: list[tuple[int, np.ndarray]], distance) -> np.ndar np_arr: list[tuple[int, np.ndarray]] = list() for i in nb_files: - np_arr.append((int(i.split(".")[0]), pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy())) + 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, 150) +allocate_lp1 = rb_allocate_distance(np_arr, 100) allocate_lp2 = rb_allocate_distance(np_arr, 500) allocate_total = allocate_lp1[:, 1] + allocate_lp2[:, 1] @@ -83,31 +83,34 @@ 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='Efficacité spectrale', title='Efficacité spectrale') +ax[0, 0].set(xlabel='number of users', ylabel='% Spectral efficiency', title='Spectral efficiency PF') ax[0, 0].grid() -ax[0, 0].set_ylim([24, 32]) +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 utilisés', title='Pourcentage de RB utilisés') +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, 205]) +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='delays(ms)', title='Delay') +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)) +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], (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].set(xlabel='number of users', ylabel='RB utilisés proche/loin/total', title='RB utilisés distance') +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, 100]) +ax[1, 1].set_ylim([0, 105]) +ax[1, 1].legend(loc="upper left") -plt.show() \ No newline at end of file +plt.show()