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__all__ = ['floquet_modes', 'floquet_modes_t', 'floquet_modes_table',
           'floquet_modes_t_lookup', 'floquet_states', 'floquet_states_t',
           'floquet_wavefunction', 'floquet_wavefunction_t',
           'floquet_state_decomposition', 'fsesolve',
           'floquet_master_equation_rates', 'floquet_collapse_operators',
           'floquet_master_equation_tensor',
           'floquet_master_equation_steadystate', 'floquet_basis_transform',
           'floquet_markov_mesolve', 'fmmesolve']

import numpy as np
import scipy.linalg as la
import scipy
from numpy import angle, pi, exp, sqrt
from types import FunctionType
from qutip.qobj import Qobj, isket
from qutip.superoperator import vec2mat_index, mat2vec, vec2mat
#from qutip.mesolve import mesolve
from qutip.sesolve import sesolve
from qutip.rhs_generate import rhs_clear
from qutip.steadystate import steadystate
from qutip.states import ket2dm
from qutip.states import projection
from qutip.solver import Options
from qutip.propagator import propagator
from qutip.solver import Result, _solver_safety_check
from qutip.cy.spmatfuncs import cy_ode_rhs
from qutip.expect import expect
from qutip.utilities import n_thermal

def floquet_modes(H, T, args=None, sort=False, U=None):
    """
    Calculate the initial Floquet modes Phi_alpha(0) for a driven system with
    period T.

    Returns a list of :class:`qutip.qobj` instances representing the Floquet
    modes and a list of corresponding quasienergies, sorted by increasing
    quasienergy in the interval [-pi/T, pi/T]. The optional parameter `sort`
    decides if the output is to be sorted in increasing quasienergies or not.

    Parameters
    ----------

    H : :class:`qutip.qobj`
        system Hamiltonian, time-dependent with period `T`

    args : dictionary
        dictionary with variables required to evaluate H

    T : float
        The period of the time-dependence of the hamiltonian. The default value
        'None' indicates that the 'tlist' spans a single period of the driving.

    U : :class:`qutip.qobj`
        The propagator for the time-dependent Hamiltonian with period `T`.
        If U is `None` (default), it will be calculated from the Hamiltonian
        `H` using :func:`qutip.propagator.propagator`.

    Returns
    -------

    output : list of kets, list of quasi energies

        Two lists: the Floquet modes as kets and the quasi energies.

    """

    if U is None:
        # get the unitary propagator
        U = propagator(H, T, [], args)

    # find the eigenstates for the propagator
    evals, evecs = la.eig(U.full())

    eargs = angle(evals)

    # make sure that the phase is in the interval [-pi, pi], so that
    # the quasi energy is in the interval [-pi/T, pi/T] where T is the
    # period of the driving.  eargs += (eargs <= -2*pi) * (2*pi) +
    # (eargs > 0) * (-2*pi)
    eargs += (eargs <= -pi) * (2 * pi) + (eargs > pi) * (-2 * pi)
    e_quasi = -eargs / T

    # sort by the quasi energy
    if sort:
        order = np.argsort(-e_quasi)
    else:
        order = list(range(len(evals)))

    # prepare a list of kets for the floquet states
    new_dims = [U.dims[0], [1] * len(U.dims[0])]
    new_shape = [U.shape[0], 1]
    kets_order = [Qobj(np.array(evecs[:, o]).T,
                       dims=new_dims, shape=new_shape) for o in order]

    return kets_order, e_quasi[order]

def floquet_modes_t(f_modes_0, f_energies, t, H, T, args=None):
    """
    Calculate the Floquet modes at times tlist Phi_alpha(tlist) propagting the
    initial Floquet modes Phi_alpha(0)

    Parameters
    ----------

    f_modes_0 : list of :class:`qutip.qobj` (kets)
        Floquet modes at :math:`t`

    f_energies : list
        Floquet energies.

    t : float
        The time at which to evaluate the floquet modes.

    H : :class:`qutip.qobj`
        system Hamiltonian, time-dependent with period `T`

    args : dictionary
        dictionary with variables required to evaluate H

    T : float
        The period of the time-dependence of the hamiltonian.

    Returns
    -------

    output : list of kets

        The Floquet modes as kets at time :math:`t`

    """
    # find t in [0,T] such that t_orig = t + n * T for integer n
    t = t - int(t / T) * T
    f_modes_t = []

    # get the unitary propagator from 0 to t
    if t > 0.0:
        U = propagator(H, t, [], args)

        for n in np.arange(len(f_modes_0)):
            f_modes_t.append(U * f_modes_0[n] * exp(1j * f_energies[n] * t))
    else:
        f_modes_t = f_modes_0

    return f_modes_t

def floquet_modes_table(f_modes_0, f_energies, tlist, H, T, args=None):
    """
    Pre-calculate the Floquet modes for a range of times spanning the floquet
    period. Can later be used as a table to look up the floquet modes for
    any time.

    Parameters
    ----------

    f_modes_0 : list of :class:`qutip.qobj` (kets)
        Floquet modes at :math:`t`

    f_energies : list
        Floquet energies.

    tlist : array
        The list of times at which to evaluate the floquet modes.

    H : :class:`qutip.qobj`
        system Hamiltonian, time-dependent with period `T`

    T : float
        The period of the time-dependence of the hamiltonian.

    args : dictionary
        dictionary with variables required to evaluate H

    Returns
    -------

    output : nested list

        A nested list of Floquet modes as kets for each time in `tlist`

    """
    # truncate tlist to the driving period
    tlist_period = tlist[np.where(tlist <= T)]

    f_modes_table_t = [[] for t in tlist_period]

    opt = Options()
    opt.rhs_reuse = True
    rhs_clear()

    for n, f_mode in enumerate(f_modes_0):
        output = sesolve(H, f_mode, tlist_period, [], args, opt)
        for t_idx, f_state_t in enumerate(output.states):
            f_modes_table_t[t_idx].append(
                f_state_t * exp(1j * f_energies[n] * tlist_period[t_idx]))

    return f_modes_table_t

def floquet_modes_t_lookup(f_modes_table_t, t, T):
    """
    Lookup the floquet mode at time t in the pre-calculated table of floquet
    modes in the first period of the time-dependence.

    Parameters
    ----------

    f_modes_table_t : nested list of :class:`qutip.qobj` (kets)
        A lookup-table of Floquet modes at times precalculated by
        :func:`qutip.floquet.floquet_modes_table`.

    t : float
        The time for which to evaluate the Floquet modes.

    T : float
        The period of the time-dependence of the hamiltonian.

    Returns
    -------

    output : nested list

        A list of Floquet modes as kets for the time that most closely matching
        the time `t` in the supplied table of Floquet modes.
    """

    # find t_wrap in [0,T] such that t = t_wrap + n * T for integer n
    t_wrap = t - int(t / T) * T

    # find the index in the table that corresponds to t_wrap (= tlist[t_idx])
    t_idx = int(t_wrap / T * len(f_modes_table_t))

    # XXX: might want to give a warning if the cast of t_idx to int discard
    # a significant fraction in t_idx, which would happen if the list of time
    # values isn't perfect matching the driving period
    # if debug: print "t = %f -> t_wrap = %f @ %d of %d" % (t, t_wrap, t_idx,
    # N)

    return f_modes_table_t[t_idx]

def floquet_states(f_modes_t, f_energies, t):
    """
    Evaluate the floquet states at time t given the Floquet modes at that time.

    Parameters
    ----------

    f_modes_t : list of :class:`qutip.qobj` (kets)
        A list of Floquet modes for time :math:`t`.

    f_energies : array
        The Floquet energies.

    t : float
        The time for which to evaluate the Floquet states.

    Returns
    -------

    output : list

        A list of Floquet states for the time :math:`t`.

    """

    return [(f_modes_t[i] * exp(-1j * f_energies[i] * t))
            for i in np.arange(len(f_energies))]

def floquet_states_t(f_modes_0, f_energies, t, H, T, args=None):
    """
    Evaluate the floquet states at time t given the initial Floquet modes.

    Parameters
    ----------

    f_modes_t : list of :class:`qutip.qobj` (kets)
        A list of initial Floquet modes (for time :math:`t=0`).

    f_energies : array
        The Floquet energies.

    t : float
        The time for which to evaluate the Floquet states.

    H : :class:`qutip.qobj`
        System Hamiltonian, time-dependent with period `T`.

    T : float
        The period of the time-dependence of the hamiltonian.

    args : dictionary
        Dictionary with variables required to evaluate H.

    Returns
    -------

    output : list

        A list of Floquet states for the time :math:`t`.

    """

    f_modes_t = floquet_modes_t(f_modes_0, f_energies, t, H, T, args)
    return [(f_modes_t[i] * exp(-1j * f_energies[i] * t))
            for i in np.arange(len(f_energies))]

def floquet_wavefunction(f_modes_t, f_energies, f_coeff, t):
    """
    Evaluate the wavefunction for a time t using the Floquet state
    decompositon, given the Floquet modes at time `t`.

    Parameters
    ----------

    f_modes_t : list of :class:`qutip.qobj` (kets)
        A list of initial Floquet modes (for time :math:`t=0`).

    f_energies : array
        The Floquet energies.

    f_coeff : array
        The coefficients for Floquet decomposition of the initial wavefunction.

    t : float
        The time for which to evaluate the Floquet states.

    Returns
    -------

    output : :class:`qutip.qobj`

        The wavefunction for the time :math:`t`.

    """
    return sum([f_modes_t[i] * exp(-1j * f_energies[i] * t) * f_coeff[i]
                for i in np.arange(len(f_energies))])

def floquet_wavefunction_t(f_modes_0, f_energies, f_coeff, t, H, T, args=None):
    """
    Evaluate the wavefunction for a time t using the Floquet state
    decompositon, given the initial Floquet modes.

    Parameters
    ----------

    f_modes_t : list of :class:`qutip.qobj` (kets)
        A list of initial Floquet modes (for time :math:`t=0`).

    f_energies : array
        The Floquet energies.

    f_coeff : array
        The coefficients for Floquet decomposition of the initial wavefunction.

    t : float
        The time for which to evaluate the Floquet states.

    H : :class:`qutip.qobj`
        System Hamiltonian, time-dependent with period `T`.

    T : float
        The period of the time-dependence of the hamiltonian.

    args : dictionary
        Dictionary with variables required to evaluate H.

    Returns
    -------

    output : :class:`qutip.qobj`

        The wavefunction for the time :math:`t`.

    """

    f_states_t = floquet_states_t(f_modes_0, f_energies, t, H, T, args)
    return sum([f_states_t[i] * f_coeff[i]
                for i in np.arange(len(f_energies))])

def floquet_state_decomposition(f_states, f_energies, psi):
    r"""
    Decompose the wavefunction `psi` (typically an initial state) in terms of
    the Floquet states, :math:`\psi = \sum_\alpha c_\alpha \psi_\alpha(0)`.

    Parameters
    ----------

    f_states : list of :class:`qutip.qobj` (kets)
        A list of Floquet modes.

    f_energies : array
        The Floquet energies.

    psi : :class:`qutip.qobj`
        The wavefunction to decompose in the Floquet state basis.

    Returns
    -------

    output : array

        The coefficients :math:`c_\alpha` in the Floquet state decomposition.

    """
    # [:1,:1][0, 0] patch around scipy 1.3.0 bug
    return [(f_states[i].dag() * psi).data[:1, :1][0, 0]
            for i in np.arange(len(f_energies))]

def fsesolve(H, psi0, tlist, e_ops=[], T=None, args={}, Tsteps=100):
    """
    Solve the Schrodinger equation using the Floquet formalism.

    Parameters
    ----------

    H : :class:`qutip.qobj.Qobj`
        System Hamiltonian, time-dependent with period `T`.

    psi0 : :class:`qutip.qobj`
        Initial state vector (ket).

    tlist : *list* / *array*
        list of times for :math:`t`.

    e_ops : list of :class:`qutip.qobj` / callback function
        list of operators for which to evaluate expectation values. If this
        list is empty, the state vectors for each time in `tlist` will be
        returned instead of expectation values.

    T : float
        The period of the time-dependence of the hamiltonian.

    args : dictionary
        Dictionary with variables required to evaluate H.

    Tsteps : integer
        The number of time steps in one driving period for which to
        precalculate the Floquet modes. `Tsteps` should be an even number.

    Returns
    -------

    output : :class:`qutip.solver.Result`

        An instance of the class :class:`qutip.solver.Result`, which
        contains either an *array* of expectation values or an array of
        state vectors, for the times specified by `tlist`.
    """

    if not T:
        # assume that tlist span exactly one period of the driving
        T = tlist[-1]

    # find the floquet modes for the time-dependent hamiltonian
    f_modes_0, f_energies = floquet_modes(H, T, args)

    # calculate the wavefunctions using the from the floquet modes
    f_modes_table_t = floquet_modes_table(f_modes_0, f_energies,
                                          np.linspace(0, T, Tsteps + 1),
                                          H, T, args)

    # setup Result for storing the results
    output = Result()
    output.times = tlist
    output.solver = "fsesolve"

    if isinstance(e_ops, FunctionType):
        output.num_expect = 0
        expt_callback = True

    elif isinstance(e_ops, list):

        output.num_expect = len(e_ops)
        expt_callback = False

        if output.num_expect == 0:
            output.states = []
        else:
            output.expect = []
            for op in e_ops:
                if op.isherm:
                    output.expect.append(np.zeros(len(tlist)))
                else:
                    output.expect.append(np.zeros(len(tlist), dtype=complex))

    else:
        raise TypeError("e_ops must be a list Qobj or a callback function")

    psi0_fb = psi0.transform(f_modes_0)
    for t_idx, t in enumerate(tlist):
        f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
        f_states_t = floquet_states(f_modes_t, f_energies, t)
        psi_t = psi0_fb.transform(f_states_t, True)

        if expt_callback:
            # use callback method
            e_ops(t, psi_t)
        else:
            # calculate all the expectation values, or output psi if
            # no expectation value operators where defined
            if output.num_expect == 0:
                output.states.append(Qobj(psi_t))
            else:
                for e_idx, e in enumerate(e_ops):
                    output.expect[e_idx][t_idx] = expect(e, psi_t)

    return output

def floquet_master_equation_rates(f_modes_0, f_energies, c_op, H, T,
                                  args, J_cb, w_th, kmax=5,
                                  f_modes_table_t=None):
    """
    Calculate the rates and matrix elements for the Floquet-Markov master
    equation.

    Parameters
    ----------

    f_modes_0 : list of :class:`qutip.qobj` (kets)
        A list of initial Floquet modes.

    f_energies : array
        The Floquet energies.

    c_op : :class:`qutip.qobj`
        The collapse operators describing the dissipation.

    H : :class:`qutip.qobj`
        System Hamiltonian, time-dependent with period `T`.

    T : float
        The period of the time-dependence of the hamiltonian.

    args : dictionary
        Dictionary with variables required to evaluate H.

    J_cb : callback functions
        A callback function that computes the noise power spectrum, as
        a function of frequency, associated with the collapse operator `c_op`.

    w_th : float
        The temperature in units of frequency.

    k_max : int
        The truncation of the number of sidebands (default 5).

    f_modes_table_t : nested list of :class:`qutip.qobj` (kets)
        A lookup-table of Floquet modes at times precalculated by
        :func:`qutip.floquet.floquet_modes_table` (optional).

    Returns
    -------

    output : list

        A list (Delta, X, Gamma, A) containing the matrices Delta, X, Gamma
        and A used in the construction of the Floquet-Markov master equation.

    """

    N = len(f_energies)
    M = 2 * kmax + 1

    omega = (2 * pi) / T

    Delta = np.zeros((N, N, M))
    X = np.zeros((N, N, M), dtype=complex)
    Gamma = np.zeros((N, N, M))
    A = np.zeros((N, N))

    nT = 100
    dT = T / nT
    tlist = np.arange(dT, T + dT / 2, dT)

    if f_modes_table_t is None:
        f_modes_table_t = floquet_modes_table(f_modes_0, f_energies,
                                              np.linspace(0, T, nT + 1), H, T,
                                              args)

    for t in tlist:
        # TODO: repeated invocations of floquet_modes_t is
        # inefficient...  make a and b outer loops and use the mesolve
        # instead of the propagator.

        # f_modes_t = floquet_modes_t(f_modes_0, f_energies, t, H, T, args)
        f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
        for a in range(N):
            for b in range(N):
                k_idx = 0
                for k in range(-kmax, kmax + 1, 1):
                    # [:1,:1][0, 0] patch around scipy 1.3.0 bug
                    X[a, b, k_idx] += (dT / T) * exp(-1j * k * omega * t) * \
                        (f_modes_t[a].dag() * c_op * f_modes_t[b])[:1, :1][0, 0]
                    k_idx += 1

    Heaviside = lambda x: ((np.sign(x) + 1) / 2.0)
    for a in range(N):
        for b in range(N):
            k_idx = 0
            for k in range(-kmax, kmax + 1, 1):
                Delta[a, b, k_idx] = f_energies[a] - f_energies[b] + k * omega
                Gamma[a, b, k_idx] = 2 * pi * Heaviside(Delta[a, b, k_idx]) * \
                    J_cb(Delta[a, b, k_idx]) * abs(X[a, b, k_idx]) ** 2
                k_idx += 1

    for a in range(N):
        for b in range(N):
            for k in range(-kmax, kmax + 1, 1):
                k1_idx = k + kmax
                k2_idx = -k + kmax
                A[a, b] += Gamma[a, b, k1_idx] + \
                    n_thermal(abs(Delta[a, b, k1_idx]), w_th) * \
                    (Gamma[a, b, k1_idx] + Gamma[b, a, k2_idx])

    return Delta, X, Gamma, A

def floquet_collapse_operators(A):
    """
    Construct collapse operators corresponding to the Floquet-Markov
    master-equation rate matrix `A`.

    .. note::

        Experimental.

    """
    c_ops = []

    N, M = np.shape(A)

    #
    # Here we really need a master equation on Bloch-Redfield form, or perhaps
    # we can use the Lindblad form master equation with some rotating frame
    # approximations? ...
    #
    for a in range(N):
        for b in range(N):
            if a != b and abs(A[a, b]) > 0.0:
                # only relaxation terms included...
                c_ops.append(sqrt(A[a, b]) * projection(N, a, b))

    return c_ops

def floquet_master_equation_tensor(Alist, f_energies):
    """
    Construct a tensor that represents the master equation in the floquet
    basis (with constant Hamiltonian and collapse operators).

    Simplest RWA approximation [Grifoni et al, Phys.Rep. 304 229 (1998)]

    Parameters
    ----------

    Alist : list
        A list of Floquet-Markov master equation rate matrices.

    f_energies : array
        The Floquet energies.

    Returns
    -------

    output : array

        The Floquet-Markov master equation tensor `R`.

    """

    if isinstance(Alist, list):
        # Alist can be a list of rate matrices corresponding
        # to different operators that couple to the environment
        N, M = np.shape(Alist[0])
    else:
        # or a simple rate matrix, in which case we put it in a list
        Alist = [Alist]
        N, M = np.shape(Alist[0])

    Rdata_lil = scipy.sparse.lil_matrix((N * N, N * N), dtype=complex)
    for I in range(N * N):
        a, b = vec2mat_index(N, I)
        for J in range(N * N):
            c, d = vec2mat_index(N, J)

            R = -1.0j * (f_energies[a] - f_energies[b])*(a == c)*(b == d)
            Rdata_lil[I, J] = R

            for A in Alist:
                s1 = s2 = 0
                for n in range(N):
                    s1 += A[a, n] * (n == c) * (n == d) - A[n, a] * \
                        (a == c) * (a == d)
                    s2 += (A[n, a] + A[n, b]) * (a == c) * (b == d)

                dR = (a == b) * s1 - 0.5 * (1 - (a == b)) * s2

                if dR != 0.0:
                    Rdata_lil[I, J] += dR

    return Qobj(Rdata_lil, [[N, N], [N, N]], [N*N, N*N])

def floquet_master_equation_steadystate(H, A):
    """
    Returns the steadystate density matrix (in the floquet basis!) for the
    Floquet-Markov master equation.
    """
    c_ops = floquet_collapse_operators(A)
    rho_ss = steadystate(H, c_ops)
    return rho_ss

def floquet_basis_transform(f_modes, f_energies, rho0):
    """
    Make a basis transform that takes rho0 from the floquet basis to the
    computational basis.
    """
    return rho0.transform(f_modes, True)

# -----------------------------------------------------------------------------
# Floquet-Markov master equation
#
#
def floquet_markov_mesolve(R, ekets, rho0, tlist, e_ops, f_modes_table=None,
                           options=None, floquet_basis=True):
    """
    Solve the dynamics for the system using the Floquet-Markov master equation.
    """

    if options is None:
        opt = Options()
    else:
        opt = options

    if opt.tidy:
        R.tidyup()

    #
    # check initial state
    #
    if isket(rho0):
        # Got a wave function as initial state: convert to density matrix.
        rho0 = ket2dm(rho0)

    #
    # prepare output array
    #
    n_tsteps = len(tlist)
    dt = tlist[1] - tlist[0]

    output = Result()
    output.solver = "fmmesolve"
    output.times = tlist

    if isinstance(e_ops, FunctionType):
        n_expt_op = 0
        expt_callback = True

    elif isinstance(e_ops, list):

        n_expt_op = len(e_ops)
        expt_callback = False

        if n_expt_op == 0:
            output.states = []
        else:
            if not f_modes_table:
                raise TypeError("The Floquet mode table has to be provided " +
                                "when requesting expectation values.")

            output.expect = []
            output.num_expect = n_expt_op
            for op in e_ops:
                if op.isherm:
                    output.expect.append(np.zeros(n_tsteps))
                else:
                    output.expect.append(np.zeros(n_tsteps, dtype=complex))

    else:
        raise TypeError("Expectation parameter must be a list or a function")

    #
    # transform the initial density matrix to the eigenbasis: from
    # computational basis to the floquet basis
    #
    if ekets is not None:
        rho0 = rho0.transform(ekets)

    #
    # setup integrator
    #
    initial_vector = mat2vec(rho0.full())
    r = scipy.integrate.ode(cy_ode_rhs)
    r.set_f_params(R.data.data, R.data.indices, R.data.indptr)
    r.set_integrator('zvode', method=opt.method, order=opt.order,
                     atol=opt.atol, rtol=opt.rtol, max_step=opt.max_step)
    r.set_initial_value(initial_vector, tlist[0])

    #
    # start evolution
    #
    rho = Qobj(rho0)

    t_idx = 0
    for t in tlist:
        if not r.successful():
            break

        rho = Qobj(vec2mat(r.y), rho0.dims, rho0.shape)

        if expt_callback:
            # use callback method
            if floquet_basis:
                e_ops(t, Qobj(rho))
            else:
                f_modes_table_t, T = f_modes_table
                f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
                e_ops(t, Qobj(rho).transform(f_modes_t, True))
        else:
            # calculate all the expectation values, or output rho if
            # no operators
            if n_expt_op == 0:
                if floquet_basis:
                    output.states.append(Qobj(rho))
                else:
                    f_modes_table_t, T = f_modes_table
                    f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
                    output.states.append(Qobj(rho).transform(f_modes_t, True))
            else:
                f_modes_table_t, T = f_modes_table
                f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
                for m in range(0, n_expt_op):
                    output.expect[m][t_idx] = \
                        expect(e_ops[m], rho.transform(f_modes_t, False))

        r.integrate(r.t + dt)
        t_idx += 1

    return output

# -----------------------------------------------------------------------------
# Solve the Floquet-Markov master equation
#
#
def fmmesolve(H, rho0, tlist, c_ops=[], e_ops=[], spectra_cb=[], T=None,
              args={}, options=Options(), floquet_basis=True, kmax=5,
              _safe_mode=True):
    """
    Solve the dynamics for the system using the Floquet-Markov master equation.

    .. note::

        This solver currently does not support multiple collapse operators.

    Parameters
    ----------

    H : :class:`qutip.qobj`
        system Hamiltonian.

    rho0 / psi0 : :class:`qutip.qobj`
        initial density matrix or state vector (ket).

    tlist : *list* / *array*
        list of times for :math:`t`.

    c_ops : list of :class:`qutip.qobj`
        list of collapse operators.

    e_ops : list of :class:`qutip.qobj` / callback function
        list of operators for which to evaluate expectation values.

    spectra_cb : list callback functions
        List of callback functions that compute the noise power spectrum as
        a function of frequency for the collapse operators in `c_ops`.

    T : float
        The period of the time-dependence of the hamiltonian. The default value
        'None' indicates that the 'tlist' spans a single period of the driving.

    args : *dictionary*
        dictionary of parameters for time-dependent Hamiltonians and
        collapse operators.

        This dictionary should also contain an entry 'w_th', which is
        the temperature of the environment (if finite) in the
        energy/frequency units of the Hamiltonian.  For example, if
        the Hamiltonian written in units of 2pi GHz, and the
        temperature is given in K, use the following conversion

        >>> temperature = 25e-3 # unit K # doctest: +SKIP
        >>> h = 6.626e-34 # doctest: +SKIP
        >>> kB = 1.38e-23 # doctest: +SKIP
        >>> args['w_th'] = temperature * (kB / h) * 2 * pi * 1e-9 \
            #doctest: +SKIP

    options : :class:`qutip.solver`
        options for the ODE solver.

    k_max : int
        The truncation of the number of sidebands (default 5).

    Returns
    -------

    output : :class:`qutip.solver`

        An instance of the class :class:`qutip.solver`, which contains either
        an *array* of expectation values for the times specified by `tlist`.
    """

    if _safe_mode:
        _solver_safety_check(H, rho0, c_ops, e_ops, args)

    if T is None:
        T = max(tlist)

    if len(spectra_cb) == 0:
        # add white noise callbacks if absent
        spectra_cb = [lambda w: 1.0] * len(c_ops)

    f_modes_0, f_energies = floquet_modes(H, T, args)

    f_modes_table_t = floquet_modes_table(f_modes_0, f_energies,
                                          np.linspace(0, T, 500 + 1),
                                          H, T, args)

    # get w_th from args if it exists
    if 'w_th' in args:
        w_th = args['w_th']
    else:
        w_th = 0

    # TODO: loop over input c_ops and spectra_cb, calculate one R for each set

    # calculate the rate-matrices for the floquet-markov master equation
    Delta, X, Gamma, Amat = floquet_master_equation_rates(
        f_modes_0, f_energies, c_ops[0], H, T, args, spectra_cb[0],
        w_th, kmax, f_modes_table_t)

    # the floquet-markov master equation tensor
    R = floquet_master_equation_tensor(Amat, f_energies)

    return floquet_markov_mesolve(R, f_modes_0, rho0, tlist, e_ops,
                                  f_modes_table=(f_modes_table_t, T),
                                  options=options,
                                  floquet_basis=floquet_basis)

numpy.array numpy.angle numpy.zeros numpy.exp numpy.shape numpy.where numpy.sign numpy.arange numpy.argsort numpy.sqrt numpy.linspace