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doubled figs
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@Lynxtail
Lynxtail committed May 13, 2024
1 parent c4d6876 commit 9a5fb75
Showing 1 changed file with 58 additions and 25 deletions.
83 changes: 58 additions & 25 deletions make_plot.py
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import os.path
import matplotlib.pyplot as plt

# for _ in range(10):
# os.system('sbt ~run')
# os.system('sbt ~run')

excluded = ('.bloop', '.bsp', '.metals', '.vscode', 'project', 'src', 'target', '.git')
lambdas = []
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mean_n = []
mean_Lambda = []
print(lambdas)
lambdas = list(np.repeat(lambdas, 10))
for i in range(0, len(demands), 10):
mean_demands.append(np.mean(demands[i:i+10], axis=0))
mean_tau.append(np.mean(tau[i:i+10]))
mean_n_i.append(np.mean(n_i[i:i+10], axis=0))
mean_n.append(np.mean(n[i:i+10]))
mean_Lambda.append(np.mean(Lambda[i:i+10]))
plot_multiplier = 30
lambdas = list(np.repeat(lambdas, plot_multiplier))
for i in range(0, len(demands), plot_multiplier):
mean_demands.append(np.mean(demands[i:i+plot_multiplier], axis=0))
mean_tau.append(np.mean(tau[i:i+plot_multiplier]))
mean_n_i.append(np.mean(n_i[i:i+plot_multiplier], axis=0))
mean_n.append(np.mean(n[i:i+plot_multiplier]))
mean_Lambda.append(np.mean(Lambda[i:i+plot_multiplier]))
print(f'lambda_0: {lambdas[i]}')
print(f'Требования: {mean_demands[-1]}')
print(f'tau: {mean_tau[-1]}')
print(f'tau_0: {mean_tau[-1]}')
print(f'n_i: {mean_n_i[-1]}')
print(f'n: {mean_n[-1]}')
print(f'Lambda: {mean_Lambda[-1]}')
print('_'*30, end='\n\n')

print(f'\nТребования: {np.mean(mean_demands, axis=0)}')
print(f'tau: {np.mean(mean_tau)}')
print(f'tau_0: {np.mean(mean_tau)}')
print(f'n_i: {np.mean(mean_n_i, axis=0)}')
print(f'n: {np.mean(mean_n)}')
print(f'Lambda: {np.mean(mean_Lambda)}')

mean_tau = list(np.repeat(mean_tau, 10))
mean_n = list(np.repeat(mean_n, 10))
lambda_0 = np.array((96.15385, 192.30769, 288.46154, 384.61538, 480.76923, 576.92308))
lambda_0_for_classic_network = np.array((96.15385, 192.30769, 288.46154, 384.61538, 480.76923, 576.92308))
tau_for_plot = np.array((0.0021, 0.00223, 0.00238, 0.00256, 0.00278, 0.00309))
tau_for_classic_network = np.array((0.00228, 0.00236, 0.00245, 0.00254, 0.00264, 0.00275))
n_for_plot = np.array((0.20186, 0.4281, 0.68532, 0.98351, 1.3389, 1.78088))
n_for_classic_network = np.array((0.21947, 0.454, 0.70548, 0.97618, 1.26886, 1.58694))

lambda_0 = (96.15385, 192.30769, 288.46154, 384.61538, 480.76923, 576.92308)
lambda_0_for_classic_network = (96.15385, 192.30769, 288.46154, 384.61538, 480.76923, 576.92308)
tau_for_plot = (0.0021, 0.00223, 0.00238, 0.00256, 0.00278, 0.00309)
tau_for_classic_network = (0.00228, 0.00236, 0.00245, 0.00254, 0.00264, 0.00275)
n_for_plot = (0.20186, 0.4281, 0.68532, 0.98351, 1.3389, 1.78088)
n_for_classic_network = (0.21947, 0.454, 0.70548, 0.97618, 1.26886, 1.58694)
dx = max(abs(mean_tau - tau_for_plot))
i_dx = abs(mean_tau - tau_for_plot).argmax()
x = mean_tau[i_dx]
print(f'\nМаксимальная погрешность tau_0: {dx / x * 100}%')

dx = max(abs(mean_n - n_for_plot))
i_dx = abs(mean_n - n_for_plot).argmax()
x = mean_n[i_dx]
print(f'Максимальная погрешность sum n_i: {dx / x * 100}%')


mean_tau = np.array(np.repeat(mean_tau, plot_multiplier))
mean_n = np.array(np.repeat(mean_n, plot_multiplier))

plt.figure(1)
plt.scatter(lambdas, tau, c='g')
plt.plot(lambdas, mean_tau, '-o', c='g', label='ИМ - ненадёжная сеть')
plt.grid(True)
plt.plot(lambda_0_for_classic_network, tau_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
# plt.plot(lambda_0_for_classic_network, tau_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
plt.plot(lambda_0, tau_for_plot, '-o', c='r', label='АМ - ненадёжная сеть')
plt.legend()
plt.title(f'Зависимость м.о. времени реакции сети\nот интенсивности входящего потока\nпри пороге ' + r"$\tau'_0$" + f'= 1')
plt.xlabel(r'$\lambda_0$, пакетов в секунду')
plt.ylabel(r'$\tau$, секунд')
plt.savefig('fig_tau.png', format='png', dpi=1000.)
plt.savefig('fig_tau.svg', format='svg', dpi=1000.)
plt.savefig('IM_AM_fig_tau.png', format='png', dpi=1000.)
plt.savefig('IM_AM_fig_tau.svg', format='svg', dpi=1000.)

plt.figure(2)
plt.scatter(lambdas, n, c='g')
plt.plot(lambdas, mean_n, '-o', c='g', label='ИМ - ненадёжная сеть')
plt.grid(True)
plt.plot(lambda_0_for_classic_network, n_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
# plt.plot(lambda_0_for_classic_network, n_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
plt.plot(lambda_0, n_for_plot, '-o', c='r', label='АМ - ненадёжная сеть')
plt.legend()
plt.title(f'Зависимость м.о. числа требований в сети\nот интенсивности входящего потока\nпри пороге ' + r"$\tau'_0$" + f'= 1')
plt.xlabel(r'$\lambda_0$, пакетов в секунду')
plt.ylabel(r'$\sum_i n_i$, пакетов')
plt.savefig('fig_n.png', format='png', dpi=1000.)
plt.savefig('fig_n.svg', format='svg', dpi=1000.)
plt.savefig('IM_AM_fig_n.png', format='png', dpi=1000.)
plt.savefig('IM_AM_fig_n.svg', format='svg', dpi=1000.)

plt.figure(3)
# plt.scatter(lambdas, tau, c='g')
# plt.plot(lambdas, mean_tau, '-o', c='g', label='ИМ - ненадёжная сеть')
plt.grid(True)
plt.plot(lambda_0_for_classic_network, tau_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
plt.plot(lambda_0, tau_for_plot, '-o', c='r', label='АМ - ненадёжная сеть')
plt.legend()
plt.title(f'Зависимость м.о. времени реакции сети\nот интенсивности входящего потока\nпри пороге ' + r"$\tau'_0$" + f'= 1')
plt.xlabel(r'$\lambda_0$, пакетов в секунду')
plt.ylabel(r'$\tau$, секунд')
plt.savefig('AM_fig_tau.png', format='png', dpi=1000.)
plt.savefig('AM_fig_tau.svg', format='svg', dpi=1000.)

plt.figure(4)
# plt.scatter(lambdas, n, c='g')
# plt.plot(lambdas, mean_n, '-o', c='g', label='ИМ - ненадёжная сеть')
plt.grid(True)
plt.plot(lambda_0_for_classic_network, n_for_classic_network, '-o', c='b', label='АМ - надёжная сеть')
plt.plot(lambda_0, n_for_plot, '-o', c='r', label='АМ - ненадёжная сеть')
plt.legend()
plt.title(f'Зависимость м.о. числа требований в сети\nот интенсивности входящего потока\nпри пороге ' + r"$\tau'_0$" + f'= 1')
plt.xlabel(r'$\lambda_0$, пакетов в секунду')
plt.ylabel(r'$\sum_i n_i$, пакетов')
plt.savefig('AM_fig_n.png', format='png', dpi=1000.)
plt.savefig('AM_fig_n.svg', format='svg', dpi=1000.)

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