# Initialize 1-jetlet
q = np.zeros([N, DIM])
p = np.zeros([N, DIM])
mu = np.zeros([N, DIM, DIM])
q1 = np.zeros([N, DIM, DIM])
for i in range(0, N):
q1[i] = np.eye(DIM)
# Initial 1-momentum of jetlets
mu[0] = -0.84 * jpf.spin + 0.89 * jpf.stretch
T = 15.0
Ei = jpf.Hamiltonian(q, p, mu)
pi = jpf.lin_momentum(q, p, mu)
Li = jpf.ang_momentum(q, p, mu)
print 'initial energy: %.3f' % Ei
print 'initial momentum: %.3f,%.3f %.3f' % (pi[0], pi[1], Li[0][1])
print 'initial J_R^1 momenta:'
Ki = np.zeros([N, DIM, DIM])
for i in range(0, N):
Ki[i] = jpf.Jr1_momentum(q, p, mu, q1, particles=[i])
print Ki[i]
state = jpf.weinstein_darboux_to_state(q, p, mu, q1)
step_max = 200
t_span = np.linspace(0., T, step_max)
y_span = odeint(jpf.ode_function, state, t_span, rtol=0.0000001)
np.save('state_data', y_span)
np.save('time_data', t_span)
import matplotlib.pyplot as plt
import numpy as np
import sys
N = jpf.N
DIM = jpf.DIM
SIGMA = jpf.SIGMA
y_data = np.load('./state_data.npy')
times = np.load('./time_data.npy')
N_timestep = y_data.shape[0]
momenta = np.zeros([N, 3, N_timestep])
for i in range(N_timestep):
q, p, mu, q1 = jpf.state_to_weinstein_darboux(y_data[i])
for j in range(N):
tmp = jpf.lin_momentum(q, p, mu, [j])
momenta[j][0][i] = tmp[0]
momenta[j][1][i] = tmp[1]
momenta[j][2][i] = jpf.ang_momentum(q, p, mu, [j])[0][1]
plt.figure()
plt.xlabel('t')
plt.plot(times, momenta[0][2], 'b-', times, momenta[1][2], 'r-', times,
momenta[2][2], 'g-')
#plt.axis([0,times[N_timestep-1],0,1])
plt.show()
# mu[2] = 0.2*jpf.spin
T = 30.45
#print 'testing various functions'
#print jpf.test_functions(1)
print 'parameters:'
print 'r0 = %.2f' % r0
print 'p0 = %.2f' % p0
print 'd = %.2f' % d
print 'omega = %.2f' % omega
Ei = jpf.Hamiltonian(q,p,mu)
pi = jpf.lin_momentum(q,p,mu)
Li = jpf.ang_momentum(q,p,mu)
print 'initial energy: %.3f' % Ei
print 'initial momentum: %.3f,%.3f %.3f' % ( pi[0], pi[1], Li[0][1] )
print 'initial J_R^1 momenta:'
Ki = np.zeros([N,DIM,DIM])
for i in range(0,N):
Ki[i] = jpf.Jr1_momentum(q,p,mu,q1,particles=[i])
print Ki[i]
state = jpf.weinstein_darboux_to_state( q , p , mu , q1 )
step_max = 406
t_span = np.linspace( 0. , T , step_max )
y_span = odeint( jpf.ode_function , state , t_span , rtol=0.0000001 )
np.save('state_data',y_span)
np.save('time_data',t_span)
import numpy as np
import sys
N = jpf.N
DIM = jpf.DIM
SIGMA = jpf.SIGMA
y_data = np.load('./state_data.npy')
times = np.load('./time_data.npy')
N_timestep = y_data.shape[0]
momenta = np.zeros([N,3,N_timestep])
for i in range(N_timestep):
q,p,mu,q1 = jpf.state_to_weinstein_darboux( y_data[i] )
for j in range(N):
tmp = jpf.lin_momentum(q,p,mu,[j])
momenta[j][0][i] = tmp[0]
momenta[j][1][i] = tmp[1]
momenta[j][2][i] = jpf.ang_momentum(q,p,mu,[j])[0][1]
plt.figure()
plt.xlabel('t')
plt.plot(times,momenta[0][2],'b-',
times,momenta[1][2],'r-',
times,momenta[2][2],'g-')
#plt.axis([0,times[N_timestep-1],0,1])
plt.show()
