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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 21 06:03:32 2018
@author: David Nolte
Introduction to Modern Dynamics, 2nd edition (Oxford University Press, 2019)
Damped-driven pendulum
"""
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
plt.close('all')
print('DampedDriven.py')
# model_case 1 = Pendulum
# model_case 2 = Double Well
print(' ')
print('DampedDriven.py')
print('Case: 1 = Pendulum 2 = Double Well')
model_case = int(input('Enter the Model Case (1-2)'))
if model_case == 1:
F = 0.6 # 0.6
delt = 0.25 # 0.1
w = 0.7 # 0.7
def flow_deriv(x_y_z,tspan):
x, y, z = x_y_z
a = y
b = F*np.cos(w*tspan) - np.sin(x) - delt*y
c = w
return[a,b,c]
else:
alpha = -1 # -1
beta = 1 # 1
delta = 0.3 # 0.3
gam = 0.15 # 0.15
w = 1
def flow_deriv(x_y_z,tspan):
x, y, z = x_y_z
a = y
b = delta*np.cos(w*tspan) - alpha*x - beta*x**3 - gam*y
c = w
return[a,b,c]
T = 2*np.pi/w
# initial conditions
px1 = .1
xp1 = .1
w1 = 0
x_y_z = [xp1, px1, w1]
# Settle-down Solve for the trajectories
t = np.linspace(0, 2000, 40000)
x_t = integrate.odeint(flow_deriv, x_y_z, t)
x0 = x_t[39999,0:3]
tspan = np.linspace(1,20000,400000)
x_t = integrate.odeint(flow_deriv, x0, tspan)
siztmp = np.shape(x_t)
siz = siztmp[0]
if model_case == 1:
y1 = np.mod(x_t[:,0]-np.pi,2*np.pi)-np.pi
y2 = x_t[:,1]
y3 = x_t[:,2]
else:
y1 = x_t[:,0]
y2 = x_t[:,1]
y3 = x_t[:,2]
plt.figure(1)
lines = plt.plot(y1,y2,'ko',ms=1)
plt.setp(lines, linewidth=0.5)
plt.show()
repnum = 5000
px = np.zeros(shape=(2*repnum,))
xvar = np.zeros(shape=(2*repnum,))
cnt = -1
testwt = np.mod(tspan,T)-0.5*T;
last = testwt[1]
for loop in range(2,siz):
if (last < 0)and(testwt[loop] > 0): # check for trajectory intersection with Poincare section
cnt = cnt+1
del1 = -testwt[loop-1]/(testwt[loop] - testwt[loop-1])
px[cnt] = (y2[loop]-y2[loop-1])*del1 + y2[loop-1]
xvar[cnt] = (y1[loop]-y1[loop-1])*del1 + y1[loop-1]
last = testwt[loop]
else:
last = testwt[loop]
plt.figure(2)
lines = plt.plot(xvar,px,'ko',ms=1)
plt.show()
if model_case == 1:
plt.savefig('DrivenPendulum')
else:
plt.savefig('DrivenDoubleWell')