r_zero = 1 # Functional Cost # b_1 = 1.0 b_2 = 100 b_3 = 1000 u_1_lower = 0.00 u_1_upper = 0.1 u_2_lower = 0.00 u_2_upper = 0.6 name_file_1 = 'figure_1_sir_log.eps' name_file_2 = 'figure_2_sir_log.eps' name_file_3 = 'figure_3_sir_log.eps' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(k, mu, delta, beta, gamma, omega, epsilon, m, b_1, b_2, b_3, s_zero, i_zero, r_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) # [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() n_whole = fbsm.n_whole ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) ax1.plot(t, x_wc[:, 1], label="Without control", color='darkgreen') ax1.plot(t, x[:, 1], label="Optimal controlled", color='orange') ax1.set_ylabel(r'Infected individuals')
N_p = s_y_p_zero+ s_a_p_zero+ l_y_p_zero + l_a_p_zero+i_y_p_zero+i_a_p_zero # Functional Cost A_1 = 0.5 A_2 = 0.2 A_3 = 0.2 A_4 = 1.2 c_1 = 1.0 c_2 = 1.1 c_3 = 1.2 c_4 = 1.3 name_file_1 = 'New_System_four_control.pdf' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(beta_y_p, r_y_1, r_y_2, r_a, alpha, beta_a_p, b_y, b_a, beta_y_v, beta_a_v, gamma, theta, mu, A_1, A_2, A_3, A_4, c_1, c_2, c_3, c_4, s_y_p_zero, s_a_p_zero, l_y_p_zero, l_a_p_zero, i_y_p_zero, i_a_p_zero, s_v_zero, i_v_zero) t = fbsm.t x_wc_1 = fbsm.runge_kutta_forward(fbsm.u) # [x, lambda_, u] = fbsm.forward_backward_sweep() cost = fbsm.control_cost(fbsm.x,u) ######################################################################################################################## ############################################## R_0 Computation ####################################################### ########################################################################################################################
from forward_backward_sweep import ForwardBackwardSweep import matplotlib.pyplot as plt a = 1.0 c = 4.0 x_zero = 1.0 m_1 = -1.0 m_2 = 2.0 fbsm = ForwardBackwardSweep() fbsm.eps = 0.001 fbsm.set_parameters(a, c, x_zero, m_1, m_2) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() plt.figure() plt.plot(t, x_wc, '-', ms=3, lw=1, alpha=0.7, color='green', label='State without control') plt.plot(t, x, '--', ms=3, lw=1,
n_whole = 30000.0 b_1 = 50.0 b_2 = 500.0 s_zero = 76.0 / 120.0 l_zero = 36.0 / 120.0 i_zero = 4.0 / 120.0 l_r_zero = 2.0 / 120.0 i_r_zero = 1.0 / 120.0 r_zero = 1.0 / 120.0 lambda_recruitment = n_whole * mu name_file_1 = 'figure_1_two_strain_tbm.eps' name_file_2 = 'figure_2_two_strain_tbm.eps' name_file_3 = 'figure_3_two_strain_tbm.eps' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(beta_1, beta_2, beta_3, mu, d_1, d_2, k_1, k_2, r_1, r_2, p, q, n_whole, lambda_recruitment, b_1, b_2, s_zero, l_zero, i_zero, l_r_zero, i_r_zero, r_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() n_whole = fbsm.n_whole ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) ax2 = plt.subplot2grid((2, 2), (0, 1)) ax3 = plt.subplot2grid((2, 2), (1, 1)) # ax1.plot(t, (x_wc[:, 3] + x_wc[:, 4]) / n_whole,
i_p_zero = 0.0008 s_v_zero = 0.84 i_v_zero = 0.16 # Functional Cost A_1 = .5 A_2 = 0.3 A_3 = 0.0 c_3 = 0.1 name_file_1 = 'figure_1_sir_log.eps' name_file_2 = 'figure_2_sir_log.eps' name_file_3 = 'figure_3_sir_log.eps' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(beta, a, b, psi, gamma, theta, mu, A_1, A_2, A_3, c_3, s_p_zero, l_p_zero, i_p_zero, s_v_zero, i_v_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) # [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() # n_whole = fbsm.n_whole ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) ax1.plot(t, x_wc[:, 2], label="Without control",
t_f = 20.0 b = 0.525 d = 0.5 c = 0.00115 e = 0.5 g = 0.1 a = 0.2 a_w = 2.0 # Initial conditions s_zero = 1000.0 e_zero = 100.0 i_zero = 50.0 r_zero = 15.0 n_zero = s_zero + e_zero + i_zero + r_zero fbsm = ForwardBackwardSweep() fbsm.set_parameters(t_0, t_f, b, d, c, e, g, a, a_w, s_zero, e_zero, i_zero, r_zero, n_zero) t = fbsm.t n_max = fbsm.n_max u = np.zeros(n_max) x_wc = fbsm.runge_kutta_forward(u) [x, lambda_, u] = fbsm.forward_backward_sweep() # plotting name_file_1 = 'epidemics_lenhart_lab7.eps' mpl.style.use('ggplot') # plt.ion() plt.show()
# # # beta = 0.01 a = 0.1 b = 0.075 Lambda = 0.003 gamma = 0.06 theta = 0.2 mu = 0.3 # # # Initial conditions s_p_zero = 0.9992 l_p_zero = 0.0 i_p_zero = 0.0008 s_v_zero = 0.84 i_v_zero = 0.16 # Functional Cost # A_1 = 1.0 A_2 = 1.0 A_3 = 1.0 c_1 = 0.1 #u_1_lower = 0.00 #u_1_upper = 0.1 #u_2_lower = 0.00 #u_2_upper = 0.6 fbsm = ForwardBackwardSweep() [x, lambda_, u] = fbsm.forward_backward_sweep()
import matplotlib.pyplot as plt import numpy as np from forward_backward_sweep import ForwardBackwardSweep a = 1.0 b = 1.0 c = 4.0 x_zero = 1.0 lambda_final = 0.0 fbsm = ForwardBackwardSweep() fbsm.set_parameters(a, b, c, x_zero, lambda_final) x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() t = fbsm.t plt.plot(t, x_wc, '-', color='orange', label='state without control') plt.plot(t, x, 'g-', label='controlled state') plt.ylabel(r'x(t)') plt.xlabel(r'$t$') plt.legend(loc=0) plt.show()
i_v_zero = 0.16 # Functional Cost # b_1 = 1.0 b_2 = 100 u_1_lower = 0.00 u_1_upper = 0.1 u_2_lower = 0.00 u_2_upper = 0.6 name_file_1 = 'figure_1_sir_log.eps' name_file_2 = 'figure_2_sir_log.eps' name_file_3 = 'figure_3_sir_log.eps' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(beta, a, b, Lambda, g, theta, mu, b_1, b_2, s_p_zero, l_p_zero, i_p_zero, s_v_zero, i_v_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) # [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() n_whole = fbsm.n_whole ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) ax1.plot(t, x_wc[:, 1], label="Without control",
from forward_backward_sweep import ForwardBackwardSweep import matplotlib.pyplot as plt r = 1.0 a = 1.0 b = 12.0 c = 1.0 x_zero = 1.0 lambda_final = c fbsm = ForwardBackwardSweep() fbsm.eps = 0.001 fbsm.lambda_final = lambda_final fbsm.set_parameters(r, a, b, c, x_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() plt.figure() plt.plot(t, x_wc, '-', ms=3, lw=1, alpha=0.7, color='green', label='State without control') plt.plot(t, x,
j_zero = 326 r_zero = 20 n_whole = s_zero + e_zero + q_zero + i_zero + j_zero + r_zero # functional cost b_1 = 1.0 b_2 = 1.0 b_3 = 1.0 b_4 = 1.0 c_1 = 300.0 c_2 = 600.0 name_file_1 = 'figure_1_sars.eps' name_file_2 = 'figure_2_sars.eps' name_file_3 = 'figure_3_sars.eps' # fbsm = ForwardBackwardSweep() fbsm.set_parameters(beta, e_e, e_q, e_j, mu, p, k_1, k_2, d_1, d_2, sigma_1, sigma_2, n_whole, b_1, b_2, b_3, b_4, c_1, c_2, s_zero, e_zero, q_zero, i_zero, j_zero, r_zero) # t = fbsm.t x_wcc = fbsm.runge_kutta_forward(fbsm.u) constant_cost = fbsm.control_cost(x_wcc, fbsm.u) # [x, lambda_, u] = fbsm.forward_backward_sweep() optimal_cost = fbsm.control_cost(x, u) # mpl.style.use('ggplot') # plt.ion() n_whole = fbsm.n_whole ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
import matplotlib as mpl s = 10.0 m_1 = 0.02 m_2 = 0.5 m_3 = 4.4 r = 0.03 k = 0.000024 n_weight = 300.0 a = 0.2 t_cell_max = 1500 t_cell_zero = 806.4 t_cell_infected_zero = 0.04 virus_particle_zero = 1.5 name_file_1 = 'hiv_chemotherapy_fig_01.eps' fbsm = ForwardBackwardSweep() fbsm.set_parameters(s, m_1, m_2, m_3, r, k, n_weight, a, t_cell_max, t_cell_zero, t_cell_infected_zero, virus_particle_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() plt.show() ax1 = plt.subplot2grid((2, 2), (0, 0)) ax2 = plt.subplot2grid((2, 2), (0, 1)) ax3 = plt.subplot2grid((2, 2), (1, 0)) ax4 = plt.subplot2grid((2, 2), (1, 1)) """ ax1.plot(t, x_wc[:, 0] + x_wc[:, 1], '-', ms=3,
from forward_backward_sweep import ForwardBackwardSweep import matplotlib.pyplot as plt import matplotlib as mpl r = 0.1 k = 0.75 m_p = 0.1 m_f = 0.1 c_p = 10000.0 c_f = 1.0 # initial conditions p_zero = 0.7 f_zero = 0.7 o_zero = 0.25 fbsm = ForwardBackwardSweep() fbsm.set_parameters(r, k, m_p, m_f, c_p, c_f, p_zero, f_zero, o_zero) t = fbsm.t x_wc = fbsm.runge_kutta_forward(fbsm.u) [x, lambda_, u] = fbsm.forward_backward_sweep() mpl.style.use('ggplot') # plt.ion() plt.show() ax1 = plt.subplot2grid((2, 3), (0, 0)) ax2 = plt.subplot2grid((2, 3), (0, 1)) ax3 = plt.subplot2grid((2, 3), (0, 2)) ax4 = plt.subplot2grid((2, 3), (1, 0)) ax5 = plt.subplot2grid((2, 3), (1, 1))