plt.savefig('vib_%d_%d_u.eps' % (timesteps_per_period, num_periods))
#f = RHS(b=0.4, A_F=1, w_F=2)
f = RHS(b=0.4, A_F=0, w_F=2)
f = RHS(b=0.4, A_F=1, w_F=np.pi)
f = RHS(b=0.4, A_F=20,
w_F=2 * np.pi) # qualitatively wrong FE, almost ok BE, smaller T
#f = RHS(b=0.4, A_F=20, w_F=0.5*np.pi) # cool, FE almost there, BE good
# Define different sets of experiments
solvers_theta = [
odespy.ForwardEuler(f),
# Implicit methods must use Newton solver to converge
odespy.BackwardEuler(f, nonlinear_solver='Newton'),
odespy.CrankNicolson(f, nonlinear_solver='Newton'),
]
solvers_RK = [odespy.RK2(f), odespy.RK4(f)]
solvers_accurate = [
odespy.RK4(f),
odespy.CrankNicolson(f, nonlinear_solver='Newton'),
odespy.DormandPrince(f, atol=0.001, rtol=0.02)
]
solvers_CN = [odespy.CrankNicolson(f, nonlinear_solver='Newton')]
if __name__ == '__main__':
timesteps_per_period = 20
solver_collection = 'theta'
num_periods = 1
try:
(RHS(b=0.4, F=lambda t: 10 * sin(0.5 * pi * t)),
'Medium damping, large forcing w/smaller frequency'),
(RHS(b=1.2, F=lambda t: 10 * sin(0.5 * pi * t)),
'Strong damping, large forcing w/smaller frequency'),
(RHS(b=0.4, F=lambda t: 1 * sin(2 * pi * t)),
'Medium damping, medium forcing w/larger frequency'),
(RHS(b=0.4, F=lambda t: 10 * sin(2 * pi * t)),
'Medium damping, large forcing w/larger frequency'),
(RHS(b=1.2, F=lambda t: 10 * sin(2 * pi * t)),
'Strong damping, large forcing w/larger frequency'),
]
for rhs, title in ODEs:
solvers = [
odespy.ForwardEuler(rhs),
# Implicit methods must use Newton solver to converge
odespy.BackwardEuler(rhs, nonlinear_solver='Newton'),
odespy.CrankNicolson(rhs, nonlinear_solver='Newton'),
VibSolverWrapper4Odespy(rhs),
]
T = 20 # Period is 1
dt = 0.05 # 20 steps per period
filename = 'FEBNCN_' + title.replace(', ', '_').replace('w/', '')
title = title + ' (dt=%g)' % dt
plt.figure()
run_solvers_and_plot(solvers, rhs, T, dt, title=title, filename=filename)
plt.show()
raw_input()