def print_freqs(): log = logarithmic() timeseries = np.linspace(0.,500.,2048) ntheta,nphi = 30,30 for i in np.arange(4.,ntheta+1)/(ntheta+1): for j in .5*np.pi*np.arange(15.,nphi+1)/(nphi+1): # if(i>0.9 and j>np.pi*0.45): # if(j>np.pi/4.): # if(i>0.59 and j>np.pi/2.*0.9): print np.arccos(i),j st,ct = np.sin(np.arccos(i)),i sp,cp = np.sin(j),np.cos(j) combo = cp*cp*st*st+sp*sp*st*st/log.qy2+ct*ct/log.qz2 r = np.sqrt((np.exp(1.)-0.1)/combo) initial = np.array([r*cp*st,r*sp*st,r*ct,0.0001,0.0001,0.0001]) # initial = np.array([0.111937987197,0.0104758765442,1.12993449025,0.0001,0.0001,0.0001]) results = odeint(pot.orbit_derivs2,initial,timeseries,args=(log,),rtol=1e-5,atol=1e-5) # print(log.H(initial),log.H(results[-1])) plots(timeseries,results) # freq = find_freqs(timeseries,results) L = find_actions(results, timeseries, N_matrix = 4, ifloop=True,ifprint = False) # if(L==None): # break (act,ang,n_vec,toy_aa,para),loop = L E = eval_mean_error_functions(act,ang,n_vec,toy_aa,timeseries,withplot=False)/np.std(timeseries) # ctas(ang,n_vec,toy_aa,timeseries) # print freq[0],freq[1],freq[2] print ang[3],ang[4],ang[5],initial[0],initial[1],initial[2],act[0],act[1],act[2] #,E[0],E[1],E[2],E[3],E[4],E[5] #,freq[0],freq[1],freq[2] exit()
def print_freqs():
log = logarithmic()
timeseries = np.linspace(0., 500., 2048)
ntheta, nphi = 30, 30
for i in np.arange(4., ntheta + 1) / (ntheta + 1):
for j in .5 * np.pi * np.arange(15., nphi + 1) / (nphi + 1):
# if(i>0.9 and j>np.pi*0.45):
# if(j>np.pi/4.):
# if(i>0.59 and j>np.pi/2.*0.9):
print np.arccos(i), j
st, ct = np.sin(np.arccos(i)), i
sp, cp = np.sin(j), np.cos(j)
combo = cp * cp * st * st + sp * sp * st * st / log.qy2 + ct * ct / log.qz2
r = np.sqrt((np.exp(1.) - 0.1) / combo)
initial = np.array(
[r * cp * st, r * sp * st, r * ct, 0.0001, 0.0001, 0.0001])
# initial = np.array([0.111937987197,0.0104758765442,1.12993449025,0.0001,0.0001,0.0001])
results = odeint(pot.orbit_derivs2,
initial,
timeseries,
args=(log, ),
rtol=1e-5,
atol=1e-5)
# print(log.H(initial),log.H(results[-1]))
plots(timeseries, results)
# freq = find_freqs(timeseries,results)
L = find_actions(results,
timeseries,
N_matrix=4,
ifloop=True,
ifprint=False)
# if(L==None):
# break
(act, ang, n_vec, toy_aa, para), loop = L
E = eval_mean_error_functions(
act, ang, n_vec, toy_aa, timeseries,
withplot=False) / np.std(timeseries)
# ctas(ang,n_vec,toy_aa,timeseries)
# print freq[0],freq[1],freq[2]
print ang[3], ang[4], ang[5], initial[0], initial[1], initial[
2], act[0], act[1], act[
2] #,E[0],E[1],E[2],E[3],E[4],E[5] #,freq[0],freq[1],freq[2]
exit()
print("Longer integration")
# print(results.T[-1])
L = find_actions(results, np.linspace(0.,len(results)/100.,len(results)), N_matrix = 6, ifloop=True,ifprint = False)
# print(len(results))
(act,ang,n_vec,toy_aa,para),loop = L
print(len(results),loop)
checks,maxgap = ced(n_vec,ua(toy_aa.T[3:].T,np.ones(3)))
counter=counter+1
# if(counter==10):
# continue
# print(ang)
minfreq = np.min(np.abs(ang[3:6]))
if(8.*2.*np.pi/minfreq>10.):
print(i,px,pz,"Fewer than 8 fundamental periods")
T = np.linspace(0.,len(results)/100.,len(results))
errors = eval_mean_error_functions(act,ang,n_vec,toy_aa,T,withplot=False)/np.sqrt(len(T))
print LM.H(initial),i,px,pz,act[0],act[1],act[2],' '.join(map(str, loop)),ang[3],ang[4],ang[5], errors[0],errors[1], LM.H(unrotate_coords(results[-1]))
# check_angle_solution(ang,n_vec,toy_aa,timeseries)
# plt.plot(toy_aa.T[3],toy_aa.T[4],'.')
# plt.show()
# plt.plot(toy_aa.T[3],toy_aa.T[5],'.')
# plt.show()
# plt.plot(results.T[0],results.T[1],'.');plt.show()
# F = pot.orbit_integrate(initial,80.,LM)
# results = np.array([rotate_coords(p) for p in F[0]])
# print(len(results))
# plt.plot(results.T[0],results.T[1])
N_matrix=6,
ifloop=True,
ifprint=False)
# print(len(results))
(act, ang, n_vec, toy_aa, para), loop = L
print(len(results), loop)
checks, maxgap = ced(n_vec, ua(toy_aa.T[3:].T, np.ones(3)))
counter = counter + 1
# if(counter==10):
# continue
# print(ang)
minfreq = np.min(np.abs(ang[3:6]))
if (8. * 2. * np.pi / minfreq > 10.):
print(i, px, pz, "Fewer than 8 fundamental periods")
T = np.linspace(0., len(results) / 100., len(results))
errors = eval_mean_error_functions(
act, ang, n_vec, toy_aa, T, withplot=False) / np.sqrt(len(T))
print LM.H(initial), i, px, pz, act[0], act[1], act[2], ' '.join(
map(str,
loop)), ang[3], ang[4], ang[5], errors[0], errors[1], LM.H(
unrotate_coords(results[-1]))
# check_angle_solution(ang,n_vec,toy_aa,timeseries)
# plt.plot(toy_aa.T[3],toy_aa.T[4],'.')
# plt.show()
# plt.plot(toy_aa.T[3],toy_aa.T[5],'.')
# plt.show()
# plt.plot(results.T[0],results.T[1],'.');plt.show()
# F = pot.orbit_integrate(initial,80.,LM)
# results = np.array([rotate_coords(p) for p in F[0]])
# print(len(results))
# plt.plot(results.T[0],results.T[1])
