Buckets:
| import numpy as np | |
| def tilde_w(w, dt): | |
| return (2./dt)*np.arcsin(w*dt/2.) | |
| def plot_frequency_approximations(): | |
| w = 1 # relevant value in a scaled problem | |
| stability_limit = 2./w | |
| dt = np.linspace(0.2, stability_limit, 111) # time steps | |
| series_approx = w + (1./24)*dt**2*w**3 | |
| P = 2*np.pi/w # one period | |
| num_timesteps_per_period = P/dt # more instructive | |
| import scitools.std as plt | |
| plt.plot(num_timesteps_per_period, tilde_w(w, dt), 'r-', | |
| num_timesteps_per_period, series_approx, 'b--', | |
| legend=('exact discrete frequency', '2nd-order expansion'), | |
| xlabel='no of time steps per period', | |
| ylabel='numerical frequency') | |
| plt.savefig('discrete_freq.png') | |
| plt.savefig('discrete_freq.pdf') | |
| plot_frequency_approximations() | |
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