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https://codeberg.org/Yael-II/MSc2-Project-Chaos.git
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115 lines
2.8 KiB
Python
115 lines
2.8 KiB
Python
import time
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import potentials as pot
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import energies as ene
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import integrator as itg
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import initial_conditions as init
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import poincare_sections as pcs
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if "YII_light_1" in plt.style.available:
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plt.style.use("YII_light_1")
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# Loading matplotlib style...
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# PARAM
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N_grid = 1000
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N_inter = int(1e5)
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N_part = 200
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#all_E = np.array([1/100, 1/12, 1/10, 1/8, 1/6])
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all_E = np.linspace(1/100, 1/6, 20)
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h = 0.005
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mu_c = 1e-4
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d_12 = 1e-7
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# INIT
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W_grid = init.mesh_grid(N_grid, xmin=-1, xmax=1, ymin=-1, ymax=1)
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X_grid = W_grid[0]
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Y_grid = W_grid[1]
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potential = pot.hh_potential(W_grid, position_only=True)
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# MAIN
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def gen_mu(E):
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pot_valid = np.ma.masked_where(potential > E, potential)
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W_1 = init.n_energy_part(pot.hh_potential, N_part, E)
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W_2 = np.zeros_like(W_1)
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alpha = np.random.uniform(0, 2*np.pi, N_part)
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W_2[0, 0] = W_1[0, 0] + d_12*np.cos(alpha)
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W_2[0, 1] = W_1[0, 1] + d_12*np.sin(alpha)
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W_2[1, 0] = W_1[1, 0]
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W_2[1, 1] = W_1[1, 1]
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t_1, positions_1 = itg.rk4(0, W_1, h, N_inter, pot.hh_evolution)
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x_1 = positions_1[:, 0, 0]
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y_1 = positions_1[:, 0, 1]
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u_1 = positions_1[:, 1, 0]
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v_1 = positions_1[:, 1, 1]
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t_2, positions_2 = itg.rk4(0, W_2, h, N_inter, pot.hh_evolution)
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x_2 = positions_2[:, 0, 0]
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y_2 = positions_2[:, 0, 1]
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u_2 = positions_2[:, 1, 0]
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v_2 = positions_2[:, 1, 1]
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dist_sq = (x_2[-25:] - x_1[-25:])**2 \
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+ (y_2[-25:] - y_1[-25:])**2 \
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+ (u_2[-25:] - u_1[-25:])**2 \
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+ (v_2[-25:] - v_1[-25:])**2
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mu = np.sum(dist_sq, axis=0)
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return mu
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A = np.zeros_like(all_E)
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MU = []
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ALL_E = []
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for i in range(len(all_E)):
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mu = gen_mu(all_E[i])
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n_total = N_part
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n_ergo = np.sum(mu < mu_c) # count how many times the condition is verified
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A[i] = n_ergo/n_total
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MU.append(mu)
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ALL_E.append([all_E[i]]*len(mu))
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MU = np.array(MU)
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ALL_E = np.array(ALL_E)
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# def lin(x, a, b):
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# return a*x + b
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# w_chaos = np.argwhere(A < 1).flatten()
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# X = all_E[w_chaos]
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# Y = A[w_chaos]
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# popt, pcov = curve_fit(lin, X, Y)
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# a, b = popt
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# da, db = np.sqrt(np.diag(pcov))
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fig, ax = plt.subplots(1)
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ax.scatter(ALL_E, MU, s=1,
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color="k", alpha=0.1, label="Data")
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ax.scatter(all_E, np.mean(MU, axis=1), s=10,
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color="C3", marker="*", label="Mean")
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ax.scatter(all_E, np.median(MU, axis=1), s=10,
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color="C2", marker="s", label="Median")
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ax.hlines(mu_c, np.min(all_E), np.max(all_E), color="C4")
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ax.legend()
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ax.set_yscale("log")
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ax.set_xlabel("energy E")
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ax.set_ylabel("quantity mu")
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fig.tight_layout()
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fig.savefig("mu")
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fig, ax = plt.subplots(1)
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ax.scatter(all_E, A, color="C0", s=5, marker="o", label="Data")
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ax.set_xlabel("energy E")
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ax.set_ylabel("area A")
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ax.legend()
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fig.tight_layout()
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fig.savefig("area")
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plt.show()
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