<jupyter_start><jupyter_text>Generate Data and Calculate Lyapunov Exponent<jupyter_code>from create_data import *
import numpy as np
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>1. Lorenz 96, F = 8<jupyter_code>lorenz_96(F = 8, output_dim = 40, tN = 2000, delta_t = 0.01)
train_lorenz, _ = load_data("Lorenz 96 F = 8", "data/lorenz8", 1)
mle = lyapunov_lorenz(10, 1e-8, train_lorenz.data, 0.01)
print(f"Maximal Lyapunov Exponent: {mle}")
train_lorenz, test_lorenz = load_data("Lorenz 96 F = 8", "data/lorenz8", 0.5)
plot_sample(train_lorenz, title = "Lorenz 96 F = 8", cmap = "GnBu", save_name = "Figure1-1_Lorenz96")<jupyter_output><empty_output><jupyter_text>Comparing impact of F<jupyter_code>lorenz_96(F = 4, output_dim = 40, tN = 30, delta_t = 0.01)
train_lorenz, test_lorenz = load_data("Lorenz 96 F = 4", "data/lorenz4", 1.)
plot_sample(train_lorenz, title = "Lorenz 96 F = 4", cmap = "GnBu", n = 2000, save_name = "Figure1-3a_Lorenz96F4")
train_lorenz, test_lorenz = load_data("Lorenz 96 F = 8", "data/lorenz8", 0.5)
plot_sample(train_lorenz, title = "Lorenz 96 F = 8", cmap = "GnBu", n =2000, save_name = "Figure1-3b_Lorenz96F8")
lorenz_96(F = 18, output_dim = 40, tN = 30, delta_t = 0.01)
train_lorenz, test_lorenz = load_data("Lorenz 96 F = 18", "data/lorenz18", 1.)
plot_sample(train_lorenz, title = "Lorenz 96 F = 18", cmap = "GnBu", n = 2000, save_name = "Figure1-3c_Lorenz96F18")<jupyter_output><empty_output><jupyter_text>2. KS Equation, L = 60<jupyter_code>ks(L = 60, nx = 240, tN = 50000, delta_t = 0.25, splits = 25)
train_ks, test_ks = load_data("KS L = 60", "data/ks60", 0.5)
plot_sample(train_ks, title = "KS Equation, L = 60", cmap = "Oranges", save_name = "Figure1-2_KSEquation")<jupyter_output><empty_output><jupyter_text>Comparing impact of L<jupyter_code>train_ks, test_ks = load_data("KS L = 60", "data/ks60", 0.5)
plot_sample(train_ks, title = "KS Equation, L = 60", cmap = "Oranges", n = 2000, save_name = "Figure1-4a_KS_L60")
ks(L = 100, nx = 240, tN = 600, delta_t = 0.25, splits = 25)
train_ks, test_ks = load_data("KS L = 100", "data/ks100", 1)
plot_sample(train_ks, title = "KS Equation, L = 100", cmap = "Oranges", n = 2000, save_name = "Figure1-4b_KS_L100")
ks(L = 200, nx = 240, tN = 600, delta_t = 0.25, splits = 25)
train_ks, test_ks = load_data("KS L = 200", "data/ks200", 1)
plot_sample(train_ks, title = "KS Equation, L = 200", cmap = "Oranges", n = 2000, save_name = "Figure1-4c_KS_L200")<jupyter_output><empty_output>
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