def _spectrum_es(H, wlist, c_ops, a_op, b_op):
"""
Internal function for calculating the spectrum of the correlation function
:math:`\left<A(\\tau)B(0)\\right>`.
"""
if debug:
print(inspect.stack()[0][3])
# construct the Liouvillian
L = liouvillian(H, c_ops)
# find the steady state density matrix and a_op and b_op expecation values
rho0 = steadystate(L)
a_op_ss = expect(a_op, rho0)
b_op_ss = expect(b_op, rho0)
# eseries solution for (b * rho0)(t)
es = ode2es(L, b_op * rho0)
# correlation
corr_es = expect(a_op, es)
# covariance
cov_es = corr_es - a_op_ss * b_op_ss
# tidy up covariance (to combine, e.g., zero-frequency components that cancel)
cov_es.tidyup()
# spectrum
spectrum = esspec(cov_es, wlist)
return spectrum
def spectrum_ss(H, wlist, c_op_list, a_op, b_op):
"""
Calculate the spectrum corresponding to a correlation function
:math:`\left<A(\\tau)B(0)\\right>`, i.e., the Fourier transform of the
correlation function:
.. math::
S(\omega) = \int_{-\infty}^{\infty} \left<A(\\tau)B(0)\\right>
e^{-i\omega\\tau} d\\tau.
Parameters
----------
H : :class:`qutip.qobj`
system Hamiltonian.
wlist : *list* / *array*
list of frequencies for :math:`\\omega`.
c_op_list : list of :class:`qutip.qobj`
list of collapse operators.
a_op : :class:`qutip.qobj`
operator A.
b_op : :class:`qutip.qobj`
operator B.
Returns
-------
spectrum: *array*
An *array* with spectrum :math:`S(\omega)` for the frequencies
specified in `wlist`.
"""
# contruct the Liouvillian
L = liouvillian(H, c_op_list)
# find the steady state density matrix and a_op and b_op expecation values
rho0 = steady(L)
a_op_ss = expect(a_op, rho0)
b_op_ss = expect(b_op, rho0)
# eseries solution for (b * rho0)(t)
es = ode2es(L, b_op * rho0)
# correlation
corr_es = expect(a_op, es)
# covarience
cov_es = corr_es - np.real(np.conjugate(a_op_ss) * b_op_ss)
# spectrum
spectrum = esspec(cov_es, wlist)
return spectrum
def _spectrum_es(H, wlist, c_ops, a_op, b_op):
"""
Internal function for calculating the spectrum of the correlation function
:math:`\left<A(\\tau)B(0)\\right>`.
"""
if debug:
print(inspect.stack()[0][3])
# construct the Liouvillian
L = liouvillian(H, c_ops)
# find the steady state density matrix and a_op and b_op expecation values
rho0 = steadystate(L)
a_op_ss = expect(a_op, rho0)
b_op_ss = expect(b_op, rho0)
# eseries solution for (b * rho0)(t)
es = ode2es(L, b_op * rho0)
# correlation
corr_es = expect(a_op, es)
# covariance
cov_es = corr_es - np.real(np.conjugate(a_op_ss) * b_op_ss)
# spectrum
spectrum = esspec(cov_es, wlist)
return spectrum
def _spectrum_es(H, wlist, c_ops, a_op, b_op):
"""
Internal function for calculating the spectrum of the correlation function
:math:`\left<A(\\tau)B(0)\\right>`.
"""
if debug:
print(inspect.stack()[0][3])
# construct the Liouvillian
L = liouvillian(H, c_ops)
# find the steady state density matrix and a_op and b_op expecation values
rho0 = steadystate(L)
a_op_ss = expect(a_op, rho0)
b_op_ss = expect(b_op, rho0)
# eseries solution for (b * rho0)(t)
es = ode2es(L, b_op * rho0)
# correlation
corr_es = expect(a_op, es)
# covariance
cov_es = corr_es - np.real(np.conjugate(a_op_ss) * b_op_ss)
# spectrum
spectrum = esspec(cov_es, wlist)
return spectrum
def spectrum_ss(H, wlist, c_op_list, a_op, b_op):
"""
Calculate the spectrum corresponding to a correlation function
:math:`\left<A(\\tau)B(0)\\right>`, i.e., the Fourier transform of the
correlation function:
.. math::
S(\omega) = \int_{-\infty}^{\infty} \left<A(\\tau)B(0)\\right> e^{-i\omega\\tau} d\\tau.
Parameters
----------
H : :class:`qutip.Qobj`
system Hamiltonian.
wlist : *list* / *array*
list of frequencies for :math:`\\omega`.
c_op_list : list of :class:`qutip.Qobj`
list of collapse operators.
a_op : :class:`qutip.Qobj`
operator A.
b_op : :class:`qutip.Qobj`
operator B.
Returns
-------
spectrum: *array*
An *array* with spectrum :math:`S(\omega)` for the frequencies specified
in `wlist`.
"""
# contruct the Liouvillian
L = liouvillian(H, c_op_list)
# find the steady state density matrix and a_op and b_op expecation values
rho0 = steady(L)
a_op_ss = expect(a_op, rho0)
b_op_ss = expect(b_op, rho0)
# eseries solution for (b * rho0)(t)
es = ode2es(L, b_op * rho0)
# correlation
corr_es = expect(a_op, es)
# covarience
cov_es = corr_es - np.real(np.conjugate(a_op_ss) * b_op_ss)
# spectrum
spectrum = esspec(cov_es, wlist)
return spectrum
