def cov_galve(t, w0, g, c0, c1, T, ci1, ci2 ,wd, i = 2, unpacked = False):
"""
2 osciladores, alta temperatura, ambos entornos a T
"""
a1, e1 = w0**2-g**2/4+c0-2*g, c1
a2, e2 = w0**2-g**2/4-c0-2*g, -c1
nu1, nu2 = fl.mathieu_nu(wd, a1, e1), fl.mathieu_nu(wd, a2, e2)
print 'Limite: ', np.abs(nu1.imag/g)
print 'Estable: ', nu1.imag - g/2 < 0, nu2.imag - g/2 < 0
Vmas = w0**2 + c0 + c1 * np.cos(wd*t)
Vmenos = w0**2 - c0 - c1 * np.cos(wd*t)
sxxM, sppM, sxpM = sigma(t, g, T, a1, e1, wd, ci1, Vmas, i)
sxxm, sppm, sxpm = sigma(t, g, T, a2, e2, wd, ci2, Vmenos, i)
x1x1 = 1/2 * (sxxM+sxxm)
p1p1 = 1/2 * (sppM+sppm)
x1p1 = 1/2 * (sxpM+sxpm)
x2x2 = x1x1
p2p2 = p1p1
x2p2 = x1p1
x1x2 = 1/2 * (sxxM-sxxm)
p1p2 = 1/2 * (sppM-sppm)
x1p2 = 1/2 * (sxpM-sxpm)
x2p1 = x1p2
if unpacked:
return x1x1, x2x2, x1x2, x1p1, x2p2, x1p2, x2p1, p1p1, p2p2, p1p2, nu1, nu2
else:
cov_matrix = np.array([[x1x1, x1p1, x1x2, x1p2], [x1p1, p1p1, x2p1, p1p2], [x1x2, x2p1, x2x2, x2p2], [x1p2, p1p2, x2p2, p2p2]])
return cov_matrix, nu1, nu2
def sigma(t, g, temp, a, e, wd, ci, V, i = 2):
"""
V(t) = a + e * cos(wd*t)
"""
nu = fl.mathieu_nu(wd, a, e)
C = fl.mathieu_coefs(wd, a, e, nu)
C = C[C.size//2-i:C.size//2+i+1]
phi1, dphi1, phi2, dphi2 = fl.mathieu(wd, a, e, t)
dt = t[1]-t[0]
#d2phi1, d2phi2 = np.gradient(dphi1, dt), np.gradient(dphi2, dt)
sxx0, sxp0, spp0 = ci[0], ci[1], ci[2]
# dsxx0 = -((g*(spp0*phi1**2+2*sxp0*phi1*(-(g*phi1)/2+phi2)+sxx0*(-(g*phi1)/2+phi2)**2))/np.exp(g*t))+(2*spp0*phi1*dphi1+2*sxp0*(-(g*phi1)/2+phi2)*dphi1+2*sxp0*phi1*(-(g*dphi1)/2+dphi2)+2*sxx0*(-(g*phi1)/2+phi2)*(-(g*dphi1)/2+dphi2))/np.exp(g*t)
# d2sxx0 = (g**2*(spp0*phi1**2+2*sxp0*phi1*(-(g*phi1)/2+phi2)+sxx0*(-(g*phi1)/2+phi2)**2))/np.exp(g*t)-(2*g*(2*spp0*phi1*dphi1+2*sxp0*(-(g*phi1)/2+phi2)*dphi1+2*sxp0*phi1*(-(g*dphi1)/2+dphi2)+2*sxx0*(-(g*phi1)/2+phi2)*(-(g*dphi1)/2+dphi2)))/np.exp(g*t)+(spp0*(2*dphi1**2+2*phi1*d2phi1)+2*sxp0*(2*dphi1*(-(g*dphi1)/2+dphi2)+(-(g*phi1)/2+phi2)*d2phi1+phi1*(-(g*d2phi1)/2+d2phi2))+sxx0*(2*(-(g*dphi1)/2+dphi2)**2+2*(-(g*phi1)/2+phi2)*(-(g*d2phi1)/2+d2phi2)))/np.exp(g*t)
sxx = np.exp(-g*t)*( (phi2-g/2*phi1)**2*sxx0 + 2*phi1*(phi2-g/2*phi1)*sxp0 + phi1**2 * spp0 )+S_xx(t, g, temp, nu, C)
sxx = sxx.real
# dsxx = dsxx0 + S_xx(t, g, temp, nu, C, deriv = 1)
# d2sxx = d2sxx0 + S_xx(t, g, temp, nu, C, deriv = 2)
sxp = np.gradient(sxx, dt)/2
spp = np.gradient(sxp, dt)+g*sxp+(V-2*g)*sxx
# sxp = 1/2 * dsxx
# spp = 1/2 * d2sxx +g*sxp+(V-2*g)*sxx
return sxx.real, spp.real, sxp.real
def cov(t, g, ca1, cq1, ca2, cq2, temp1, temp2, wc = 50, i = 5, unpacked = False):
"""
Devuelve todos los valores medios para la matriz de covarianza. Puede devolverlos como
la matriz armada o unpacked.
"""
nu1, nu2 = fl.mathieu_nu(ca1, cq1), fl.mathieu_nu(ca2, cq2)
c1, c2 = fl.mathieu_coefs(ca1, cq1, nu1), fl.mathieu_coefs(ca2, cq2, nu2)
c1, c2 = c1[c1.size//2-i:c1.size//2+i+1], c2[c2.size//2-i:c2.size//2+i+1]
phi1, dphi1, phi2, dphi2 = fl.mathieu(ca1, cq1, t)
phim1, dphim1, phim2, dphim2 = fl.mathieu(ca2, cq2, t)
# impedir_peq(phi1, .01)
# impedir_peq(phim1, .01)
Ma1 = a1(t, g, nu1, c1, temp1, nu2, c2, temp2, wc, phi1, phim1)
Ma2 = a2(t, g, nu1, c1, temp1, nu2, c2, temp2, wc, phi1, phim1)
Ma3 = a3(t, g, nu1, c1, temp1, nu2, c2, temp2, wc, phi1, phim1)
b1d, b1c, b2d, b2c, b3d, b3c = b(t, g, phi1, dphi1, phi2, dphi2, phim1, dphim1, phim2, dphim2)
x1x1=(Ma3[1][1]*b3c**2-(Ma3[0][1]+Ma3[1][0])*b3c*b3d+Ma3[0][0]*b3d**2)/(b3c**2-b3d**2)**2
x2x2=(Ma3[0][0]*b3c**2-(Ma3[0][1]+Ma3[1][0])*b3c*b3d+Ma3[1][1]*b3d**2)/(b3c**2-b3d**2)**2
x1x2=((Ma3[0][1]+Ma3[1][0])*b3c**2-2*(Ma3[0][0]+Ma3[1][1])*b3c*b3d+(Ma3[0][1]+Ma3[1][0])*b3d**2)/(2*(b3c**2-b3d**2)**2)
x1p1=(b3c**2*((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[1][1]*b1d-Ma2[0][1]*b3c)+b3c*(-2*(Ma3[0][0]+Ma3[1][1])*b1c-2*(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[0][0]*b3c)*b3d+((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[0][0]*b1d+Ma2[0][1]*b3c)*b3d**2-Ma2[0][0]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x2p2=(b3c**2*((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[0][0]*b1d-Ma2[1][0]*b3c)+b3c*(-2*(Ma3[0][0]+Ma3[1][1])*b1c-2*(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[1][1]*b3c)*b3d+((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[1][1]*b1d+Ma2[1][0]*b3c)*b3d**2-Ma2[1][1]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x1p2=(b3c**2*(2*Ma3[1][1]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d-Ma2[1][1]*b3c)+b3c*(-2*(Ma3[0][1]+Ma3[1][0])*b1c-2*(Ma3[0][0]+Ma3[1][1])*b1d+Ma2[1][0]*b3c)*b3d+(2*Ma3[0][0]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[1][1]*b3c)*b3d**2-Ma2[1][0]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x2p1=(b3c**2*(2*Ma3[0][0]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d-Ma2[0][0]*b3c)+b3c*(-2*(Ma3[0][1]+Ma3[1][0])*b1c-2*(Ma3[0][0]+Ma3[1][1])*b1d+Ma2[0][1]*b3c)*b3d+(2*Ma3[1][1]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[0][0]*b3c)*b3d**2-Ma2[0][1]*b3d**3)/(2*(b3c**2-b3d**2)**2)
p1p1 = Ma1[0][0]+(b3c**2*(Ma3[0][0]*b1c**2+b1d*((Ma3[0][1]+Ma3[1][0])*b1c+Ma3[1][1]*b1d)-(Ma2[0][0]*b1c+Ma2[0][1]*b1d)*b3c)-b3c*(2*(Ma3[0][0]+Ma3[1][1])*b1c*b1d+Ma3[0][1]*(b1c**2+b1d**2)+Ma3[1][0]*(b1c**2+b1d**2)-(Ma2[0][1]*b1c+Ma2[0][0]*b1d)*b3c)*b3d+(Ma3[1][1]*b1c**2+Ma3[0][1]*b1c*b1d+Ma3[1][0]*b1c*b1d+Ma3[0][0]*b1d**2+Ma2[0][0]*b1c*b3c+Ma2[0][1]*b1d*b3c)*b3d**2-(Ma2[0][1]*b1c+Ma2[0][0]*b1d)*b3d**3)/(b3c**2-b3d**2)**2
# p1p1 = Ma1[0][0]+b1d**2*x1x1+b1c**2*x2x2+4*b1d*b1d*x1x2+2*b1d*x1p1+2*b1c*x2p1
p2p2 = Ma1[1][1]+(b3c**2*(Ma3[1][1]*b1c**2+b1d*((Ma3[0][1]+Ma3[1][0])*b1c+Ma3[0][0]*b1d)-(Ma2[1][1]*b1c+Ma2[1][0]*b1d)*b3c)-b3c*(2*(Ma3[0][0]+Ma3[1][1])*b1c*b1d+Ma3[0][1]*(b1c**2+b1d**2)+Ma3[1][0]*(b1c**2+b1d**2)-(Ma2[1][0]*b1c+Ma2[1][1]*b1d)*b3c)*b3d+(Ma3[0][0]*b1c**2+Ma3[0][1]*b1c*b1d+Ma3[1][0]*b1c*b1d+Ma3[1][1]*b1d**2+Ma2[1][1]*b1c*b3c+Ma2[1][0]*b1d*b3c)*b3d**2-(Ma2[1][0]*b1c+Ma2[1][1]*b1d)*b3d**3)/(b3c**2-b3d**2)**2
p1p2 = (Ma1[0][1]+Ma1[1][0]+(((Ma2[0][1]+Ma2[1][0])*b1c+(Ma2[0][0]+Ma2[1][1])*b1d)*b3c-((Ma2[0][0]+Ma2[1][1])*b1c+(Ma2[0][1]+Ma2[1][0])*b1d)*b3d)/(-b3c**2+b3d**2)+((b1c**2+b1d**2)*((Ma3[0][1]+Ma3[1][0])*b3c**2-2*(Ma3[0][0]+Ma3[1][1])*b3c*b3d+(Ma3[0][1]+Ma3[1][0])*b3d**2))/(b3c**2-b3d**2)**2+(2*b1c*b1d*((Ma3[0][0]+Ma3[1][1])*b3c**2-2*(Ma3[0][1]+Ma3[1][0])*b3c*b3d+(Ma3[0][0]+Ma3[1][1])*b3d**2))/(b3c**2-b3d**2)**2)/2
for i in [x1x1, x2x2, x1x2, x1p1, x2p2, x1p2, x2p1, p1p1, p2p2, p1p2]:
i[0] = i[1]
if unpacked:
return x1x1, x2x2, x1x2, x1p1, x2p2, x1p2, x2p1, p1p1, p2p2, p1p2
else:
cov_matrix = np.array([[x1x1, x1p1, x1x2, x1p2], [x1p1, p1p1, x2p1, p1p2], [x1x2, x2p1, x2x2, x2p2], [x1p2, p1p2, x2p2, p2p2]])
return cov_matrix
def cov(t, g, ca1, cq1, ca2, cq2, temp1, temp2, wc = 50, i = 5):
"""
Devuelve todos los valores medios para la matriz de covarianza.
"""
nu1, nu2 = fl.mathieu_nu(ca1, cq1), fl.mathieu_nu(ca2, cq2)
c1, c2 = fl.mathieu_coefs(ca1, cq1, nu1), fl.mathieu_coefs(ca2, cq2, nu2)
c1, c2 = c1[c1.size//2-i:c1.size//2+i+1], c2[c2.size//2-i:c2.size//2+i+1]
phi1, dphi1, phi2, dphi2 = fl.mathieu(ca1, cq1, t)
phim1, dphim1, phim2, dphim2 = fl.mathieu(ca2, cq2, t)
Ma1 = a1t1(t, g, nu1, c1, temp1, nu2, c2, wc, phi1, phim1)+a1t2(t, g, nu1, c1, temp2, nu2, c2, wc, phi1, phim1)
Ma2 = a2t1(t, g, nu1, c1, temp1, nu2, c2, wc, phi1, phim1)+a2t2(t, g, nu1, c1, temp2, nu2, c2, wc, phi1, phim1)
Ma3 = a3t1(t, g, nu1, c1, temp1, nu2, c2, wc, phi1, phim1)+a3t2(t, g, nu1, c1, temp2, nu2, c2, wc, phi1, phim1)
Ma1, Ma2, Ma3 = 2*Ma1, 2*Ma2, 2*Ma3
b1d, b1c, b2d, b2c, b3d, b3c = b(t, g, phi1, dphi1, phi2, dphi2, phim1, dphim1, phim2, dphim2)
x1x1=(Ma3[1][1]*b3c**2-(Ma3[0][1]+Ma3[1][0])*b3c*b3d+Ma3[0][0]*b3d**2)/(b3c**2-b3d**2)**2
x2x2=(Ma3[0][0]*b3c**2-(Ma3[0][1]+Ma3[1][0])*b3c*b3d+Ma3[1][1]*b3d**2)/(b3c**2-b3d**2)**2
x1x2=((Ma3[0][1]+Ma3[1][0])*b3c**2-2*(Ma3[0][0]+Ma3[1][1])*b3c*b3d+(Ma3[0][1]+Ma3[1][0])*b3d**2)/(2*(b3c**2-b3d**2)**2)
x1p1=(b3c**2*((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[1][1]*b1d-Ma2[0][1]*b3c)+b3c*(-2*(Ma3[0][0]+Ma3[1][1])*b1c-2*(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[0][0]*b3c)*b3d+((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[0][0]*b1d+Ma2[0][1]*b3c)*b3d**2-Ma2[0][0]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x2p2=(b3c**2*((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[0][0]*b1d-Ma2[1][0]*b3c)+b3c*(-2*(Ma3[0][0]+Ma3[1][1])*b1c-2*(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[1][1]*b3c)*b3d+((Ma3[0][1]+Ma3[1][0])*b1c+2*Ma3[1][1]*b1d+Ma2[1][0]*b3c)*b3d**2-Ma2[1][1]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x1p2=(b3c**2*(2*Ma3[1][1]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d-Ma2[1][1]*b3c)+b3c*(-2*(Ma3[0][1]+Ma3[1][0])*b1c-2*(Ma3[0][0]+Ma3[1][1])*b1d+Ma2[1][0]*b3c)*b3d+(2*Ma3[0][0]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[1][1]*b3c)*b3d**2-Ma2[1][0]*b3d**3)/(2*(b3c**2-b3d**2)**2)
x2p1=(b3c**2*(2*Ma3[0][0]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d-Ma2[0][0]*b3c)+b3c*(-2*(Ma3[0][1]+Ma3[1][0])*b1c-2*(Ma3[0][0]+Ma3[1][1])*b1d+Ma2[0][1]*b3c)*b3d+(2*Ma3[1][1]*b1c+(Ma3[0][1]+Ma3[1][0])*b1d+Ma2[0][0]*b3c)*b3d**2-Ma2[0][1]*b3d**3)/(2*(b3c**2-b3d**2)**2)
p1p1 = Ma1[0][0]+(b3c**2*(Ma3[0][0]*b1c**2+b1d*((Ma3[0][1]+Ma3[1][0])*b1c+Ma3[1][1]*b1d)-(Ma2[0][0]*b1c+Ma2[0][1]*b1d)*b3c)-b3c*(2*(Ma3[0][0]+Ma3[1][1])*b1c*b1d+Ma3[0][1]*(b1c**2+b1d**2)+Ma3[1][0]*(b1c**2+b1d**2)-(Ma2[0][1]*b1c+Ma2[0][0]*b1d)*b3c)*b3d+(Ma3[1][1]*b1c**2+Ma3[0][1]*b1c*b1d+Ma3[1][0]*b1c*b1d+Ma3[0][0]*b1d**2+Ma2[0][0]*b1c*b3c+Ma2[0][1]*b1d*b3c)*b3d**2-(Ma2[0][1]*b1c+Ma2[0][0]*b1d)*b3d**3)/(b3c**2-b3d**2)**2
p2p2 = Ma1[1][1]+(b3c**2*(Ma3[1][1]*b1c**2+b1d*((Ma3[0][1]+Ma3[1][0])*b1c+Ma3[0][0]*b1d)-(Ma2[1][1]*b1c+Ma2[1][0]*b1d)*b3c)-b3c*(2*(Ma3[0][0]+Ma3[1][1])*b1c*b1d+Ma3[0][1]*(b1c**2+b1d**2)+Ma3[1][0]*(b1c**2+b1d**2)-(Ma2[1][0]*b1c+Ma2[1][1]*b1d)*b3c)*b3d+(Ma3[0][0]*b1c**2+Ma3[0][1]*b1c*b1d+Ma3[1][0]*b1c*b1d+Ma3[1][1]*b1d**2+Ma2[1][1]*b1c*b3c+Ma2[1][0]*b1d*b3c)*b3d**2-(Ma2[1][0]*b1c+Ma2[1][1]*b1d)*b3d**3)/(b3c**2-b3d**2)**2
p1p2 = (Ma1[0][1]+Ma1[1][0]+(((Ma2[0][1]+Ma2[1][0])*b1c+(Ma2[0][0]+Ma2[1][1])*b1d)*b3c-((Ma2[0][0]+Ma2[1][1])*b1c+(Ma2[0][1]+Ma2[1][0])*b1d)*b3d)/(-b3c**2+b3d**2)+((b1c**2+b1d**2)*((Ma3[0][1]+Ma3[1][0])*b3c**2-2*(Ma3[0][0]+Ma3[1][1])*b3c*b3d+(Ma3[0][1]+Ma3[1][0])*b3d**2))/(b3c**2-b3d**2)**2+(2*b1c*b1d*((Ma3[0][0]+Ma3[1][1])*b3c**2-2*(Ma3[0][1]+Ma3[1][0])*b3c*b3d+(Ma3[0][0]+Ma3[1][1])*b3d**2))/(b3c**2-b3d**2)**2)/2
for i in [x1x1, x2x2, x1x2, x1p1, x2p2, x1p2, x2p1, p1p1, p2p2, p1p2]:
i[0] = i[1]
return x1x1, x2x2, x1x2, x1p1, x2p2, x1p2, x2p1, p1p1, p2p2, p1p2
x2p1 = x1p2
Mcov= np.array([[x1x1, x1p1, x1x2, x1p2], [x1p1, p1p1, x2p1, p1p2], [x1x2, x2p1, x2x2, x2p2], [x1p2, p1p2, x2p2, p2p2]])
# print Mcov[:, :, 500]
neg = En(t, Mcov)
detes = det_cov(t, Mcov)
return neg, sxxM, sxxm, sppM, sppm, detes
t = np.linspace(0, 100, 10000)
w = 1
wd = 2.05
c0 = 0.1
c1 = 0.11
nuM = fl.mathieu_nu(wd, w**2+c0, c1)
num = fl.mathieu_nu(wd, w**2-c0, -c1)
print 'nu mas: ', nuM.imag
print 'nu menos: ', num.imag
neg, sxxM, sxxm, sppM, sppm, detes = entrelazamiento(t, w, wd, c0, c1)
plt.clf()
plt.subplot(1, 2, 1)
plt.plot(t, neg)
plt.grid()
plt.subplot(1, 2, 2)
plt.plot(t, sxxm, 'b', t, sppm, 'r')
#plt.plot(t, detes)
#plt.axis([190, 210, 0, 100000000])
plt.grid()
#
#print 'new time: ', end1-beg1
#print 'old time: ', end2-beg2
#
t = np.linspace(0, 10, 100)
plt.clf()
c_sci = np.loadtxt('0.75_-0.5.txt', np.complex)
nu_sci = c_sci[0]
c = c[1:]
i = 2
c = c[c.size//2-i:c.size//2+i+1]
a, q = .75, 1
g = 1
wc = 50
nu = fl.mathieu_nu(a, q, 15)
c = fl.mathieu_coefs(a, q, nu, 11)
c = c[c.size//2-i:c.size//2+i+1]
#old, cha, cha = si.sigma_baja(t, c, nu, g, wc, 0)
new = sigma_baja_nuevo(t, c, nu, g, wc)
#plt.plot(t, old)
plt.plot(t, new, 'og')
#for wc in [50]:
# s = sigma_alta_nuevo(t, ci, nu, g, wc)
# plt.plot(t, s, label = str(wc))
# plt.legend()
#for j in [0, .1, .3, .5, .6]:
# i = 2
# -*- coding: utf-8 -*- """ Created on Thu Feb 26 13:04:22 2015 @author: martin """ from __future__ import division import numpy as np import pylab as plt import sigma as si import floquet as fl import erres as R t = np.linspace(0, 30, 1000) a1, q1 = 1, .5 a2, q2 = 1.1, .5 nu1, nu2 = fl.mathieu_nu(a1, q1), fl.mathieu_nu(a2, q2) c1, c2 = fl.mathieu_coefs(a1, q1, nu1, 3), fl.mathieu_coefs(a1, q1, nu1, 3) phi1, dphi1, phi2, dphi2 = fl.mathieu(a1, q1, t) phim1, dphim1, phim2, dphim2 = fl.mathieu(a2, q2, t) g = 1 plt.clf() plt.plot(t, phi1, 'g-', t, phi2, 'b-')
#
#print 'new time: ', end1-beg1
#print 'old time: ', end2-beg2
#
t = np.linspace(0, 10, 100)
plt.clf()
c_sci = np.loadtxt('0.75_-0.5.txt', np.complex)
nu_sci = c_sci[0]
c = c[1:]
i = 2
c = c[c.size // 2 - i:c.size // 2 + i + 1]
a, q = .75, 1
g = 1
wc = 50
nu = fl.mathieu_nu(a, q, 15)
c = fl.mathieu_coefs(a, q, nu, 11)
c = c[c.size // 2 - i:c.size // 2 + i + 1]
#old, cha, cha = si.sigma_baja(t, c, nu, g, wc, 0)
new = sigma_baja_nuevo(t, c, nu, g, wc)
#plt.plot(t, old)
plt.plot(t, new, 'og')
#for wc in [50]:
# s = sigma_alta_nuevo(t, ci, nu, g, wc)
# plt.plot(t, s, label = str(wc))
# plt.legend()
#for j in [0, .1, .3, .5, .6]:
# i = 2
ret = np.zeros(len(t)) + 0 * 1j
for i, ti in enumerate(t):
print ti
def integr(w):
return w * np.real(z(ti, w, g, c1, nu1) * z(ti, w, g, c2, nu2))
int_ti = si.complex_int(integr, 0, wc)
ret[i] = int_ti
return ret
import floquet as fl
import w_w as wes
ca1, cq1 = .7, .5
nu1 = fl.mathieu_nu(ca1, cq1)
g = 1
ca2, cq2 = .8, .5
nu2 = fl.mathieu_nu(ca2, cq2)
A1, A2 = fl.mathieu_coefs(ca1, cq1, nu1,
11), fl.mathieu_coefs(ca2, cq2, nu2, 11)
i = 2
A1, A2 = A1[A1.size // 2 - i:A1.size // 2 + i + 1], A2[A2.size // 2 -
i:A2.size // 2 + i + 1]
wc = 50
t = np.linspace(0, 10, 30)
phi1, dphi1, phi2, dphi2 = fl.mathieu(ca1, cq1, t)
phim1, dphim1, phim2, dphim2 = fl.mathieu(ca2, cq2, t)
int_mia = wes.w1_w1(t, g, 0, nu1, A1, nu2, A2, wc, phi1, phim1)
int_num = w1w1(t, wc, g, A1, nu1, A2, nu2)
Mcov = np.array([[x1x1, x1p1, x1x2, x1p2], [x1p1, p1p1, x2p1, p1p2],
[x1x2, x2p1, x2x2, x2p2], [x1p2, p1p2, x2p2, p2p2]])
# print Mcov[:, :, 500]
neg = En(t, Mcov)
detes = det_cov(t, Mcov)
return neg, sxxM, sxxm, sppM, sppm, detes
t = np.linspace(0, 100, 10000)
w = 1
wd = 2.05
c0 = 0.1
c1 = 0.11
nuM = fl.mathieu_nu(wd, w**2 + c0, c1)
num = fl.mathieu_nu(wd, w**2 - c0, -c1)
print 'nu mas: ', nuM.imag
print 'nu menos: ', num.imag
neg, sxxM, sxxm, sppM, sppm, detes = entrelazamiento(t, w, wd, c0, c1)
plt.clf()
plt.subplot(1, 2, 1)
plt.plot(t, neg)
plt.grid()
plt.subplot(1, 2, 2)
plt.plot(t, sxxm, 'b', t, sppm, 'r')
#plt.plot(t, detes)
#plt.axis([190, 210, 0, 100000000])
plt.grid()
import numpy as np
import floquet as fl
from matplotlib import pylab as plt
import sigma as si
import aes as aes
from numpy.linalg import det
w = 1
c_1 = .5
c_0 = 0
g = .005
T = 30
ca1, cq1 = w**2 - g**2 / 4 + c_0 - 2 * g, -.5 * c_1
ca2, cq2 = w**2 - g**2 / 4 - c_0 - 2 * g, .5 * c_1
nu1, nu2 = fl.mathieu_nu(ca1, cq1), fl.mathieu_nu(ca2, cq2)
#print np.abs(nu1.imag/g), np.abs(nu2.imag/g)
i = 3
t = np.linspace(0, 5, 30)
wc = 50
T1, T2 = T, T
def En(t, Mcov):
var = np.array([])
for i, ti in enumerate(t):
A = Mcov[:2, :2, i]
B = Mcov[2:, 2:, i]
C = Mcov[2:, :2, i]
import numpy as np
import floquet as fl
from matplotlib import pylab as plt
import sigma as si
import aes as aes
from numpy.linalg import det
w = 1
c_1 = .5
c_0 = 0
g = .005
T = 30
ca1, cq1 = w**2-g**2/4+c_0-2*g, -.5*c_1
ca2, cq2 = w**2-g**2/4-c_0-2*g, .5*c_1
nu1, nu2 = fl.mathieu_nu(ca1, cq1), fl.mathieu_nu(ca2, cq2)
#print np.abs(nu1.imag/g), np.abs(nu2.imag/g)
i = 3
t = np.linspace(0,5, 30)
wc = 50
T1, T2 = T, T
def En(t, Mcov):
var = np.array([])
for i, ti in enumerate(t):
A = Mcov[:2, :2, i]
B = Mcov[2:, 2:, i]
C = Mcov[2:, :2, i]
return ret
def w1w1(t, wc, g, c1, nu1, c2, nu2):
ret = np.zeros(len(t)) + 0 * 1j
for i, ti in enumerate(t):
print ti
def integr(w):
return w*np.real(z(ti, w, g, c1, nu1) * z(ti, w, g, c2, nu2))
int_ti = si.complex_int(integr, 0, wc)
ret[i] = int_ti
return ret
import floquet as fl
import w_w as wes
ca1, cq1 = .7, .5
nu1 = fl.mathieu_nu(ca1, cq1)
g = 1
ca2, cq2 = .8, .5
nu2 = fl.mathieu_nu(ca2, cq2)
A1, A2 = fl.mathieu_coefs(ca1, cq1, nu1, 11), fl.mathieu_coefs(ca2, cq2, nu2, 11)
i = 2
A1, A2 = A1[A1.size//2-i:A1.size//2+i+1], A2[A2.size//2-i:A2.size//2+i+1]
wc = 50
t = np.linspace(0,10, 30)
phi1, dphi1, phi2, dphi2 = fl.mathieu(ca1, cq1, t)
phim1, dphim1, phim2, dphim2 = fl.mathieu(ca2, cq2, t)
int_mia = wes.w1_w1(t, g, 0, nu1, A1, nu2, A2, wc, phi1, phim1)
int_num = w1w1(t, wc, g, A1, nu1, A2, nu2)