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# @author: Alexander Pitchford
# @email1: agp1@aber.ac.uk
# @email2: alex.pitchford@gmail.com
# @organization: Aberystwyth University
# @supervisor: Daniel Burgarth

"""
Wrapper functions that will manage the creation of the objects,
build the configuration, and execute the algorithm required to optimise
a set of ctrl pulses for a given (quantum) system.
The fidelity error is some measure of distance of the system evolution
from the given target evolution in the time allowed for the evolution.
The functions minimise this fidelity error wrt the piecewise control
amplitudes in the timeslots

There are currently two quantum control pulse optmisations algorithms
implemented in this library. There are accessible through the methods
in this module. Both the algorithms use the scipy.optimize methods
to minimise the fidelity error with respect to to variables that define
the pulse.

GRAPE
-----
The default algorithm (as it was implemented here first) is GRAPE
GRadient Ascent Pulse Engineering [1][2]. It uses a gradient based method such
as BFGS to minimise the fidelity error. This makes convergence very quick
when an exact gradient can be calculated, but this limits the factors that can
taken into account in the fidelity.

CRAB
----
The CRAB [3][4] algorithm was developed at the University of Ulm.
In full it is the Chopped RAndom Basis algorithm.
The main difference is that it reduces the number of optimisation variables
by defining the control pulses by expansions of basis functions,
where the variables are the coefficients. Typically a Fourier series is chosen,
i.e. the variables are the Fourier coefficients.
Therefore it does not need to compute an explicit gradient.
By default it uses the Nelder-Mead method for fidelity error minimisation.

References
----------
1.  N Khaneja et. al.
    Optimal control of coupled spin dynamics: Design of NMR pulse sequences
    by gradient ascent algorithms. J. Magn. Reson. 172, 296–305 (2005).
2.  Shai Machnes et.al
    DYNAMO - Dynamic Framework for Quantum Optimal Control
    arXiv.1011.4874
3.  Doria, P., Calarco, T. & Montangero, S.
    Optimal Control Technique for Many-Body Quantum Dynamics.
    Phys. Rev. Lett. 106, 1–4 (2011).
4.  Caneva, T., Calarco, T. & Montangero, S.
    Chopped random-basis quantum optimization.
    Phys. Rev. A - At. Mol. Opt. Phys. 84, (2011).

"""
import numpy as np
import warnings

# QuTiP
from qutip.qobj import Qobj
import qutip.logging_utils as logging
logger = logging.get_logger()
# QuTiP control modules
import qutip.control.optimconfig as optimconfig
import qutip.control.dynamics as dynamics
import qutip.control.termcond as termcond
import qutip.control.optimizer as optimizer
import qutip.control.stats as stats
import qutip.control.errors as errors
import qutip.control.fidcomp as fidcomp
import qutip.control.propcomp as propcomp
import qutip.control.pulsegen as pulsegen
#import qutip.control.pulsegencrab as pulsegencrab

warnings.simplefilter('always', DeprecationWarning) #turn off filter
def _param_deprecation(message, stacklevel=3):
    """
    Issue deprecation warning
    Using stacklevel=3 will ensure message refers the function
    calling with the deprecated parameter,
    """
    warnings.warn(message, DeprecationWarning, stacklevel=stacklevel)

def _upper_safe(s):
    try:
        s = s.upper()
    except:
        pass
    return s

def optimize_pulse(
        drift, ctrls, initial, target,
        num_tslots=None, evo_time=None, tau=None,
        amp_lbound=None, amp_ubound=None,
        fid_err_targ=1e-10, min_grad=1e-10,
        max_iter=500, max_wall_time=180,
        alg='GRAPE', alg_params=None,
        optim_params=None, optim_method='DEF', method_params=None,
        optim_alg=None, max_metric_corr=None, accuracy_factor=None,
        dyn_type='GEN_MAT', dyn_params=None,
        prop_type='DEF', prop_params=None,
        fid_type='DEF', fid_params=None,
        phase_option=None, fid_err_scale_factor=None,
        tslot_type='DEF', tslot_params=None,
        amp_update_mode=None,
        init_pulse_type='DEF', init_pulse_params=None,
        pulse_scaling=1.0, pulse_offset=0.0,
        ramping_pulse_type=None, ramping_pulse_params=None,
        log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
    """
    Optimise a control pulse to minimise the fidelity error.  The dynamics of
    the system in any given timeslot are governed by the combined dynamics
    generator, i.e. the sum of the ``drift + ctrl_amp[j]*ctrls[j]``.

    The control pulse is an ``[n_ts, n_ctrls]`` array of piecewise amplitudes
    Starting from an initial (typically random) pulse, a multivariable
    optimisation algorithm attempts to determines the optimal values for the
    control pulse to minimise the fidelity error.  The fidelity error is some
    measure of distance of the system evolution from the given target evolution
    in the time allowed for the evolution.

    Parameters
    ----------
    drift : Qobj or list of Qobj
        The underlying dynamics generator of the system can provide list (of
        length ``num_tslots``) for time dependent drift.

    ctrls : List of Qobj or array like [num_tslots, evo_time]
        A list of control dynamics generators. These are scaled by the
        amplitudes to alter the overall dynamics.  Array-like input can be
        provided for time dependent control generators.

    initial : Qobj
        Starting point for the evolution.  Typically the identity matrix.

    target : Qobj
        Target transformation, e.g. gate or state, for the time evolution.

    num_tslots : integer or None
        Number of timeslots.  ``None`` implies that timeslots will be given in
        the tau array.

    evo_time : float or None
        Total time for the evolution.  ``None`` implies that timeslots will be
        given in the tau array.

    tau : array[num_tslots] of floats or None
        Durations for the timeslots.  If this is given then ``num_tslots`` and
        ``evo_time`` are derived from it.  ``None`` implies that timeslot
        durations will be equal and calculated as ``evo_time/num_tslots``.

    amp_lbound : float or list of floats
        Lower boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    amp_ubound : float or list of floats
        Upper boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    fid_err_targ : float
        Fidelity error target. Pulse optimisation will terminate when the
        fidelity error falls below this value.

    mim_grad : float
        Minimum gradient. When the sum of the squares of the gradients wrt to
        the control amplitudes falls below this value, the optimisation
        terminates, assuming local minima.

    max_iter : integer
        Maximum number of iterations of the optimisation algorithm.

    max_wall_time : float
        Maximum allowed elapsed time for the  optimisation algorithm.

    alg : string
        Algorithm to use in pulse optimisation.  Options are:

        - 'GRAPE' (default) - GRadient Ascent Pulse Engineering
        - 'CRAB' - Chopped RAndom Basis

    alg_params : Dictionary
        Options that are specific to the algorithm see above.

    optim_params : Dictionary
        The key value pairs are the attribute name and value used to set
        attribute values.  Note: attributes are created if they do not exist
        already, and are overwritten if they do.  Note: ``method_params`` are
        applied afterwards and so may override these.

    optim_method : string
        A ``scipy.optimize.minimize`` method that will be used to optimise the
        pulse for minimum fidelity error.  Note that ``FMIN``, ``FMIN_BFGS`` &
        ``FMIN_L_BFGS_B`` will all result in calling these specific
        ``scipy.optimize methods``.  Note the ``LBFGSB`` is equivalent to
        ``FMIN_L_BFGS_B`` for backwards compatibility reasons.  Supplying DEF
        will given alg dependent result:

        - GRAPE - Default ``optim_method`` is ``FMIN_L_BFGS_B``
        - CRAB - Default ``optim_method`` is ``FMIN``

    method_params : dict
        Parameters for the ``optim_method``.  Note that where there is an
        attribute of the :obj:`~Optimizer` object or the termination_conditions
        matching the key that attribute. Otherwise, and in some case also, they
        are assumed to be method_options for the ``scipy.optimize.minimize``
        method.

    optim_alg : string
        Deprecated. Use ``optim_method``.

    max_metric_corr : integer
        Deprecated. Use ``method_params`` instead.

    accuracy_factor : float
        Deprecated. Use ``method_params`` instead.

    dyn_type : string
        Dynamics type, i.e. the type of matrix used to describe the dynamics.
        Options are ``UNIT``, ``GEN_MAT``, ``SYMPL`` (see :obj:`~Dynamics`
        classes for details).

    dyn_params : dict
        Parameters for the :obj:`~Dynamics` object.  The key value pairs are
        assumed to be attribute name value pairs.  They applied after the
        object is created.

    prop_type : string
        Propagator type i.e. the method used to calculate the propagators and
        propagator gradient for each timeslot options are DEF, APPROX, DIAG,
        FRECHET, AUG_MAT.  DEF will use the default for the specific
        ``dyn_type`` (see :obj:`~PropagatorComputer` classes for details).

    prop_params : dict
        Parameters for the :obj:`~PropagatorComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    fid_type : string
        Fidelity error (and fidelity error gradient) computation method.
        Options are DEF, UNIT, TRACEDIFF, TD_APPROX.  DEF will use the default
        for the specific ``dyn_type`` (See :obj:`~FidelityComputer` classes for
        details).

    fid_params : dict
        Parameters for the :obj:`~FidelityComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    phase_option : string
        Deprecated. Pass in ``fid_params`` instead.

    fid_err_scale_factor : float
        Deprecated. Use ``scale_factor`` key in ``fid_params`` instead.

    tslot_type : string
        Method for computing the dynamics generators, propagators and evolution
        in the timeslots.  Options: DEF, UPDATE_ALL, DYNAMIC.  UPDATE_ALL is
        the only one that currently works.  (See :obj:`~TimeslotComputer`
        classes for details.)

    tslot_params : dict
        Parameters for the :obj:`~TimeslotComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    amp_update_mode : string
        Deprecated. Use ``tslot_type`` instead.

    init_pulse_type : string
        Type / shape of pulse(s) used to initialise the control amplitudes.
        Options (GRAPE) include: RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW.
        Default is RND.  (see :obj:`~PulseGen` classes for details.) For the
        CRAB the this the ``guess_pulse_type``.

    init_pulse_params : dict
        Parameters for the initial / guess pulse generator object.
        The key value pairs are assumed to be attribute name value pairs.
        They applied after the object is created.

    pulse_scaling : float
        Linear scale factor for generated initial / guess pulses.  By default
        initial pulses are generated with amplitudes in the range (-1.0, 1.0).
        These will be scaled by this parameter.

    pulse_offset : float
        Linear offset for the pulse. That is this value will be added to any
        initial / guess pulses generated.

    ramping_pulse_type : string
        Type of pulse used to modulate the control pulse.  It's intended use
        for a ramping modulation, which is often required in experimental
        setups.  This is only currently implemented in CRAB.  GAUSSIAN_EDGE was
        added for this purpose.

    ramping_pulse_params : dict
        Parameters for the ramping pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    log_level : integer
        Level of messaging output from the logger.  Options are attributes of
        :obj:`qutip.logging_utils`, in decreasing levels of messaging, are:
        DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL.
        Anything WARN or above is effectively 'quiet' execution, assuming
        everything runs as expected.  The default NOTSET implies that the level
        will be taken from the QuTiP settings file, which by default is WARN.

    out_file_ext : string or None
        Files containing the initial and final control pulse amplitudes are
        saved to the current directory.  The default name will be postfixed
        with this extension.  Setting this to None will suppress the output of
        files.

    gen_stats : boolean
        If set to True then statistics for the optimisation run will be
        generated - accessible through attributes of the stats object.

    Returns
    -------
    opt : OptimResult
        Returns instance of :obj:`~OptimResult`, which has attributes giving
        the reason for termination, final fidelity error, final evolution final
        amplitudes, statistics etc.
    """
    if log_level == logging.NOTSET:
        log_level = logger.getEffectiveLevel()
    else:
        logger.setLevel(log_level)

    # The parameters types are checked in create_pulse_optimizer
    # so no need to do so here
    # However, the deprecation management is repeated here
    # so that the stack level is correct
    if not optim_alg is None:
        optim_method = optim_alg
        _param_deprecation(
            "The 'optim_alg' parameter is deprecated. "
            "Use 'optim_method' instead")

    if not max_metric_corr is None:
        if isinstance(method_params, dict):
            if not 'max_metric_corr' in method_params:
                 method_params['max_metric_corr'] = max_metric_corr
        else:
            method_params = {'max_metric_corr':max_metric_corr}
        _param_deprecation(
            "The 'max_metric_corr' parameter is deprecated. "
            "Use 'max_metric_corr' in method_params instead")

    if not accuracy_factor is None:
        if isinstance(method_params, dict):
            if not 'accuracy_factor' in method_params:
                 method_params['accuracy_factor'] = accuracy_factor
        else:
            method_params = {'accuracy_factor':accuracy_factor}
        _param_deprecation(
            "The 'accuracy_factor' parameter is deprecated. "
            "Use 'accuracy_factor' in method_params instead")

    # phase_option
    if not phase_option is None:
        if isinstance(fid_params, dict):
            if not 'phase_option' in fid_params:
                 fid_params['phase_option'] = phase_option
        else:
            fid_params = {'phase_option':phase_option}
        _param_deprecation(
            "The 'phase_option' parameter is deprecated. "
            "Use 'phase_option' in fid_params instead")

    # fid_err_scale_factor
    if not fid_err_scale_factor is None:
        if isinstance(fid_params, dict):
            if not 'fid_err_scale_factor' in fid_params:
                 fid_params['scale_factor'] = fid_err_scale_factor
        else:
            fid_params = {'scale_factor':fid_err_scale_factor}
        _param_deprecation(
            "The 'fid_err_scale_factor' parameter is deprecated. "
            "Use 'scale_factor' in fid_params instead")

    # amp_update_mode
    if not amp_update_mode is None:
        amp_update_mode_up = _upper_safe(amp_update_mode)
        if amp_update_mode_up == 'ALL':
            tslot_type = 'UPDATE_ALL'
        else:
            tslot_type = amp_update_mode
        _param_deprecation(
            "The 'amp_update_mode' parameter is deprecated. "
            "Use 'tslot_type' instead")

    optim = create_pulse_optimizer(
        drift, ctrls, initial, target,
        num_tslots=num_tslots, evo_time=evo_time, tau=tau,
        amp_lbound=amp_lbound, amp_ubound=amp_ubound,
        fid_err_targ=fid_err_targ, min_grad=min_grad,
        max_iter=max_iter, max_wall_time=max_wall_time,
        alg=alg, alg_params=alg_params, optim_params=optim_params,
        optim_method=optim_method, method_params=method_params,
        dyn_type=dyn_type, dyn_params=dyn_params,
        prop_type=prop_type, prop_params=prop_params,
        fid_type=fid_type, fid_params=fid_params,
        init_pulse_type=init_pulse_type, init_pulse_params=init_pulse_params,
        pulse_scaling=pulse_scaling, pulse_offset=pulse_offset,
        ramping_pulse_type=ramping_pulse_type,
        ramping_pulse_params=ramping_pulse_params,
        log_level=log_level, gen_stats=gen_stats)

    dyn = optim.dynamics

    dyn.init_timeslots()
    # Generate initial pulses for each control
    init_amps = np.zeros([dyn.num_tslots, dyn.num_ctrls])

    if alg == 'CRAB':
        for j in range(dyn.num_ctrls):
            pgen = optim.pulse_generator[j]
            pgen.init_pulse()
            init_amps[:, j] = pgen.gen_pulse()
    else:
        pgen = optim.pulse_generator
        for j in range(dyn.num_ctrls):
            init_amps[:, j] = pgen.gen_pulse()

    # Initialise the starting amplitudes
    dyn.initialize_controls(init_amps)

    if log_level <= logging.INFO:
        msg = "System configuration:\n"
        dg_name = "dynamics generator"
        if dyn_type == 'UNIT':
            dg_name = "Hamiltonian"
        if dyn.time_depend_drift:
            msg += "Initial drift {}:\n".format(dg_name)
            msg += str(dyn.drift_dyn_gen[0])
        else:
            msg += "Drift {}:\n".format(dg_name)
            msg += str(dyn.drift_dyn_gen)
        for j in range(dyn.num_ctrls):
            msg += "\nControl {} {}:\n".format(j+1, dg_name)
            msg += str(dyn.ctrl_dyn_gen[j])
        msg += "\nInitial state / operator:\n"
        msg += str(dyn.initial)
        msg += "\nTarget state / operator:\n"
        msg += str(dyn.target)
        logger.info(msg)

    if out_file_ext is not None:
        # Save initial amplitudes to a text file
        pulsefile = "ctrl_amps_initial_" + out_file_ext
        dyn.save_amps(pulsefile)
        if log_level <= logging.INFO:
            logger.info("Initial amplitudes output to file: " + pulsefile)

    # Start the optimisation
    result = optim.run_optimization()

    if out_file_ext is not None:
        # Save final amplitudes to a text file
        pulsefile = "ctrl_amps_final_" + out_file_ext
        dyn.save_amps(pulsefile)
        if log_level <= logging.INFO:
            logger.info("Final amplitudes output to file: " + pulsefile)

    return result

def optimize_pulse_unitary(
        H_d, H_c, U_0, U_targ,
        num_tslots=None, evo_time=None, tau=None,
        amp_lbound=None, amp_ubound=None,
        fid_err_targ=1e-10, min_grad=1e-10,
        max_iter=500, max_wall_time=180,
        alg='GRAPE', alg_params=None,
        optim_params=None, optim_method='DEF', method_params=None,
        optim_alg=None, max_metric_corr=None, accuracy_factor=None,
        phase_option='PSU',
        dyn_params=None, prop_params=None, fid_params=None,
        tslot_type='DEF', tslot_params=None,
        amp_update_mode=None,
        init_pulse_type='DEF', init_pulse_params=None,
        pulse_scaling=1.0, pulse_offset=0.0,
        ramping_pulse_type=None, ramping_pulse_params=None,
        log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):

    """
    Optimise a control pulse to minimise the fidelity error, assuming that the
    dynamics of the system are generated by unitary operators.  This function
    is simply a wrapper for optimize_pulse, where the appropriate options for
    unitary dynamics are chosen and the parameter names are in the format
    familiar to unitary dynamics The dynamics of the system  in any given
    timeslot are governed by the combined Hamiltonian, i.e. the sum of the
    ``H_d + ctrl_amp[j]*H_c[j]`` The control pulse is an ``[n_ts, n_ctrls]``
    array of piecewise amplitudes Starting from an initial (typically random)
    pulse, a multivariable optimisation algorithm attempts to determines the
    optimal values for the control pulse to minimise the fidelity error The
    maximum fidelity for a unitary system is 1, i.e. when the time evolution
    resulting from the pulse is equivalent to the target.  And therefore the
    fidelity error is ``1 - fidelity``.

    Parameters
    ----------
    H_d : Qobj or list of Qobj
        Drift (aka system) the underlying Hamiltonian of the system can provide
        list (of length ``num_tslots``) for time dependent drift.

    H_c : List of Qobj or array like [num_tslots, evo_time]
        A list of control Hamiltonians. These are scaled by the amplitudes to
        alter the overall dynamics.  Array-like input can be provided for time
        dependent control generators.

    U_0 : Qobj
        Starting point for the evolution.  Typically the identity matrix.

    U_targ : Qobj
        Target transformation, e.g. gate or state, for the time evolution.

    num_tslots : integer or None
        Number of timeslots.  ``None`` implies that timeslots will be given in
        the tau array.

    evo_time : float or None
        Total time for the evolution.  ``None`` implies that timeslots will be
        given in the tau array.

    tau : array[num_tslots] of floats or None
        Durations for the timeslots.  If this is given then ``num_tslots`` and
        ``evo_time`` are derived from it.  ``None`` implies that timeslot
        durations will be equal and calculated as ``evo_time/num_tslots``.

    amp_lbound : float or list of floats
        Lower boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    amp_ubound : float or list of floats
        Upper boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    fid_err_targ : float
        Fidelity error target. Pulse optimisation will terminate when the
        fidelity error falls below this value.

    mim_grad : float
        Minimum gradient. When the sum of the squares of the gradients wrt to
        the control amplitudes falls below this value, the optimisation
        terminates, assuming local minima.

    max_iter : integer
        Maximum number of iterations of the optimisation algorithm.

    max_wall_time : float
        Maximum allowed elapsed time for the optimisation algorithm.

    alg : string
        Algorithm to use in pulse optimisation.  Options are:

        - 'GRAPE' (default) - GRadient Ascent Pulse Engineering
        - 'CRAB' - Chopped RAndom Basis

    alg_params : Dictionary
        options that are specific to the algorithm see above

    optim_params : Dictionary
        The key value pairs are the attribute name and value used to set
        attribute values.  Note: attributes are created if they do not exist
        already, and are overwritten if they do.  Note: ``method_params`` are
        applied afterwards and so may override these.

    optim_method : string
        A ``scipy.optimize.minimize`` method that will be used to optimise the
        pulse for minimum fidelity error Note that ``FMIN``, ``FMIN_BFGS`` &
        ``FMIN_L_BFGS_B`` will all result in calling these specific
        scipy.optimize methods Note the ``LBFGSB`` is equivalent to
        ``FMIN_L_BFGS_B`` for backwards compatibility reasons.  Supplying
        ``DEF`` will given algorithm-dependent result:

        - GRAPE - Default ``optim_method`` is FMIN_L_BFGS_B
        - CRAB - Default ``optim_method`` is FMIN

    method_params : dict
        Parameters for the ``optim_method``.  Note that where there is an
        attribute of the :obj:`~Optimizer` object or the
        ``termination_conditions`` matching the key that attribute. Otherwise,
        and in some case also, they are assumed to be method_options for the
        ``scipy.optimize.minimize`` method.

    optim_alg : string
        Deprecated. Use ``optim_method``.

    max_metric_corr : integer
        Deprecated. Use ``method_params`` instead.

    accuracy_factor : float
        Deprecated. Use ``method_params`` instead.

    phase_option : string
        Determines how global phase is treated in fidelity calculations
        (``fid_type='UNIT'`` only). Options:

        - PSU - global phase ignored
        - SU - global phase included

    dyn_params : dict
        Parameters for the :obj:`~Dynamics` object.  The key value pairs are
        assumed to be attribute name value pairs.  They applied after the
        object is created.

    prop_params : dict
        Parameters for the :obj:`~PropagatorComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    fid_params : dict
        Parameters for the :obj:`~FidelityComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    tslot_type : string
        Method for computing the dynamics generators, propagators and evolution
        in the timeslots.  Options: ``DEF``, ``UPDATE_ALL``, ``DYNAMIC``.
        ``UPDATE_ALL`` is the only one that currently works.  (See
        :obj:`~TimeslotComputer` classes for details.)

    tslot_params : dict
        Parameters for the :obj:`~TimeslotComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    amp_update_mode : string
        Deprecated. Use ``tslot_type`` instead.

    init_pulse_type : string
        Type / shape of pulse(s) used to initialise the control amplitudes.
        Options (GRAPE) include: RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW.
        DEF is RND.  (see :obj:`~PulseGen` classes for details.) For the CRAB
        the this the guess_pulse_type.

    init_pulse_params : dict
        Parameters for the initial / guess pulse generator object.  The key
        value pairs are assumed to be attribute name value pairs.  They applied
        after the object is created.

    pulse_scaling : float
        Linear scale factor for generated initial / guess pulses.  By default
        initial pulses are generated with amplitudes in the range (-1.0, 1.0).
        These will be scaled by this parameter.

    pulse_offset : float
        Linear offset for the pulse. That is this value will be added to any
        initial / guess pulses generated.

    ramping_pulse_type : string
        Type of pulse used to modulate the control pulse.  It's intended use
        for a ramping modulation, which is often required in experimental
        setups.  This is only currently implemented in CRAB.  GAUSSIAN_EDGE was
        added for this purpose.

    ramping_pulse_params : dict
        Parameters for the ramping pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    log_level : integer
        Level of messaging output from the logger.  Options are attributes of
        :obj:`qutip.logging_utils` in decreasing levels of messaging, are:
        DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL
        Anything WARN or above is effectively 'quiet' execution, assuming
        everything runs as expected.  The default NOTSET implies that the level
        will be taken from the QuTiP settings file, which by default is WARN.

    out_file_ext : string or None
        Files containing the initial and final control pulse amplitudes are
        saved to the current directory.  The default name will be postfixed
        with this extension.  Setting this to ``None`` will suppress the output
        of files.

    gen_stats : boolean
        If set to ``True`` then statistics for the optimisation run will be
        generated - accessible through attributes of the stats object.

    Returns
    -------
    opt : OptimResult
        Returns instance of :obj:`~OptimResult`, which has attributes giving
        the reason for termination, final fidelity error, final evolution final
        amplitudes, statistics etc.
    """

    # parameters are checked in create pulse optimiser

    # The deprecation management is repeated here
    # so that the stack level is correct
    if not optim_alg is None:
        optim_method = optim_alg
        _param_deprecation(
            "The 'optim_alg' parameter is deprecated. "
            "Use 'optim_method' instead")

    if not max_metric_corr is None:
        if isinstance(method_params, dict):
            if not 'max_metric_corr' in method_params:
                 method_params['max_metric_corr'] = max_metric_corr
        else:
            method_params = {'max_metric_corr':max_metric_corr}
        _param_deprecation(
            "The 'max_metric_corr' parameter is deprecated. "
            "Use 'max_metric_corr' in method_params instead")

    if not accuracy_factor is None:
        if isinstance(method_params, dict):
            if not 'accuracy_factor' in method_params:
                 method_params['accuracy_factor'] = accuracy_factor
        else:
            method_params = {'accuracy_factor':accuracy_factor}
        _param_deprecation(
            "The 'accuracy_factor' parameter is deprecated. "
            "Use 'accuracy_factor' in method_params instead")

    # amp_update_mode
    if not amp_update_mode is None:
        amp_update_mode_up = _upper_safe(amp_update_mode)
        if amp_update_mode_up == 'ALL':
            tslot_type = 'UPDATE_ALL'
        else:
            tslot_type = amp_update_mode
        _param_deprecation(
            "The 'amp_update_mode' parameter is deprecated. "
            "Use 'tslot_type' instead")

    # phase_option is still valid for this method
    # pass it via the fid_params
    if not phase_option is None:
        if fid_params is None:
            fid_params = {'phase_option':phase_option}
        else:
            if not 'phase_option' in fid_params:
                fid_params['phase_option'] = phase_option


    return optimize_pulse(
            drift=H_d, ctrls=H_c, initial=U_0, target=U_targ,
            num_tslots=num_tslots, evo_time=evo_time, tau=tau,
            amp_lbound=amp_lbound, amp_ubound=amp_ubound,
            fid_err_targ=fid_err_targ, min_grad=min_grad,
            max_iter=max_iter, max_wall_time=max_wall_time,
            alg=alg, alg_params=alg_params, optim_params=optim_params,
            optim_method=optim_method, method_params=method_params,
            dyn_type='UNIT', dyn_params=dyn_params,
            prop_params=prop_params, fid_params=fid_params,
            init_pulse_type=init_pulse_type, init_pulse_params=init_pulse_params,
            pulse_scaling=pulse_scaling, pulse_offset=pulse_offset,
            ramping_pulse_type=ramping_pulse_type,
            ramping_pulse_params=ramping_pulse_params,
            log_level=log_level, out_file_ext=out_file_ext,
            gen_stats=gen_stats)

def opt_pulse_crab(
        drift, ctrls, initial, target,
        num_tslots=None, evo_time=None, tau=None,
        amp_lbound=None, amp_ubound=None,
        fid_err_targ=1e-5,
        max_iter=500, max_wall_time=180,
        alg_params=None,
        num_coeffs=None, init_coeff_scaling=1.0,
        optim_params=None, optim_method='fmin', method_params=None,
        dyn_type='GEN_MAT', dyn_params=None,
        prop_type='DEF', prop_params=None,
        fid_type='DEF', fid_params=None,
        tslot_type='DEF', tslot_params=None,
        guess_pulse_type=None, guess_pulse_params=None,
        guess_pulse_scaling=1.0, guess_pulse_offset=0.0,
        guess_pulse_action='MODULATE',
        ramping_pulse_type=None, ramping_pulse_params=None,
        log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
    """
    Optimise a control pulse to minimise the fidelity error.
    The dynamics of the system in any given timeslot are governed
    by the combined dynamics generator,
    i.e. the sum of the drift+ctrl_amp[j]*ctrls[j]
    The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes.
    The CRAB algorithm uses basis function coefficents as the variables to
    optimise. It does NOT use any gradient function.
    A multivariable optimisation algorithm attempts to determines the
    optimal values for the control pulse to minimise the fidelity error
    The fidelity error is some measure of distance of the system evolution
    from the given target evolution in the time allowed for the evolution.

    Parameters
    ----------
    drift : Qobj or list of Qobj
        the underlying dynamics generator of the system
        can provide list (of length num_tslots) for time dependent drift

    ctrls : List of Qobj or array like [num_tslots, evo_time]
        a list of control dynamics generators. These are scaled by
        the amplitudes to alter the overall dynamics
        Array like imput can be provided for time dependent control generators

    initial : Qobj
        Starting point for the evolution.  Typically the identity matrix.

    target : Qobj
        Target transformation, e.g. gate or state, for the time evolution.

    num_tslots : integer or None
        Number of timeslots.  ``None`` implies that timeslots will be given in
        the tau array.

    evo_time : float or None
        Total time for the evolution.  ``None`` implies that timeslots will be
        given in the tau array.

    tau : array[num_tslots] of floats or None
        Durations for the timeslots.  If this is given then ``num_tslots`` and
        ``evo_time`` are dervived from it.
        ``None`` implies that timeslot durations will be equal and calculated
        as ``evo_time/num_tslots``.

    amp_lbound : float or list of floats
        Lower boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    amp_ubound : float or list of floats
        Upper boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    fid_err_targ : float
        Fidelity error target. Pulse optimisation will terminate when the
        fidelity error falls below this value.

    max_iter : integer
        Maximum number of iterations of the optimisation algorithm.

    max_wall_time : float
        Maximum allowed elapsed time for the  optimisation algorithm.

    alg_params : Dictionary
        Options that are specific to the algorithm see above.

    optim_params : Dictionary
        The key value pairs are the attribute name and value used to set
        attribute values.  Note: attributes are created if they do not exist
        already, and are overwritten if they do.  Note: method_params are
        applied afterwards and so may override these.

    coeff_scaling : float
        Linear scale factor for the random basis coefficients.  By default
        these range from -1.0 to 1.0.  Note this is overridden by alg_params
        (if given there).

    num_coeffs : integer
        Number of coefficients used for each basis function.  Note this is
        calculated automatically based on the dimension of the dynamics if not
        given. It is crucial to the performane of the algorithm that it is set
        as low as possible, while still giving high enough frequencies.  Note
        this is overridden by alg_params (if given there).

    optim_method : string
        Multi-variable optimisation method.  The only tested options are 'fmin'
        and 'Nelder-mead'.  In theory any non-gradient method implemented in
        scipy.optimize.mininize could be used.

    method_params : dict
        Parameters for the optim_method.  Note that where there is an attribute
        of the :obj:`~Optimizer` object or the termination_conditions matching
        the key that attribute. Otherwise, and in some case also, they are
        assumed to be method_options for the ``scipy.optimize.minimize``
        method.  The commonly used parameter are:

        - xtol - limit on variable change for convergence
        - ftol - limit on fidelity error change for convergence

    dyn_type : string
        Dynamics type, i.e. the type of matrix used to describe the dynamics.
        Options are UNIT, GEN_MAT, SYMPL (see Dynamics classes for details).

    dyn_params : dict
        Parameters for the Dynamics object.  The key value pairs are assumed to
        be attribute name value pairs.  They applied after the object is
        created.

    prop_type : string
        Propagator type i.e. the method used to calculate the propagtors and
        propagtor gradient for each timeslot options are DEF, APPROX, DIAG,
        FRECHET, AUG_MAT DEF will use the default for the specific dyn_type
        (see :obj:`~PropagatorComputer` classes for details).

    prop_params : dict
        Parameters for the :obj:`~PropagatorComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    fid_type : string
        Fidelity error (and fidelity error gradient) computation method.
        Options are DEF, UNIT, TRACEDIFF, TD_APPROX.  DEF will use the default
        for the specific dyn_type.  (See :obj:`~FidelityComputer` classes for
        details).

    fid_params : dict
        Parameters for the :obj:`~FidelityComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    tslot_type : string
        Method for computing the dynamics generators, propagators and evolution
        in the timeslots.  Options: DEF, UPDATE_ALL, DYNAMIC UPDATE_ALL is the
        only one that currently works.  (See :obj:`~TimeslotComputer` classes
        for details).

    tslot_params : dict
        Parameters for the :obj:`~TimeslotComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    guess_pulse_type : string, default None
        Type / shape of pulse(s) used modulate the control amplitudes.
        Options include: RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN.

    guess_pulse_params : dict
        Parameters for the guess pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    guess_pulse_action : string, default 'MODULATE'
        Determines how the guess pulse is applied to the pulse generated by the
        basis expansion.  Options are: MODULATE, ADD.

    pulse_scaling : float
        Linear scale factor for generated guess pulses.  By default initial
        pulses are generated with amplitudes in the range (-1.0, 1.0). These
        will be scaled by this parameter.

    pulse_offset : float
        Linear offset for the pulse. That is this value will be added to any
        guess pulses generated.

    ramping_pulse_type : string
        Type of pulse used to modulate the control pulse.  It's intended use
        for a ramping modulation, which is often required in experimental
        setups.  This is only currently implemented in CRAB.  GAUSSIAN_EDGE was
        added for this purpose.

    ramping_pulse_params : dict
        Parameters for the ramping pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    log_level : integer
        level of messaging output from the logger.  Options are attributes of
        :obj:`qutip.logging_utils`, in decreasing levels of messaging, are:
        DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL
        Anything WARN or above is effectively 'quiet' execution, assuming
        everything runs as expected.  The default NOTSET implies that the level
        will be taken from the QuTiP settings file, which by default is WARN.

    out_file_ext : string or None
        Files containing the initial and final control pulse.  Amplitudes are
        saved to the current directory.  The default name will be postfixed
        with this extension.  Setting this to ``None`` will suppress the output
        of files.

    gen_stats : boolean
        If set to ``True`` then statistics for the optimisation run will be
        generated - accessible through attributes of the stats object.

    Returns
    -------
    opt : OptimResult
        Returns instance of OptimResult, which has attributes giving the
        reason for termination, final fidelity error, final evolution
        final amplitudes, statistics etc
    """

    # The parameters are checked in create_pulse_optimizer
    # so no need to do so here

    if log_level == logging.NOTSET:
        log_level = logger.getEffectiveLevel()
    else:
        logger.setLevel(log_level)

    # build the algorithm options
    if not isinstance(alg_params, dict):
        alg_params = {'num_coeffs': num_coeffs,
                      'init_coeff_scaling': init_coeff_scaling}
    else:
        if (num_coeffs is not None and
            not 'num_coeffs' in alg_params):
            alg_params['num_coeffs'] = num_coeffs
        if (init_coeff_scaling is not None and
            not 'init_coeff_scaling' in alg_params):
            alg_params['init_coeff_scaling'] = init_coeff_scaling

    # Build the guess pulse options
    # Any options passed in the guess_pulse_params take precedence
    # over the parameter values.
    if guess_pulse_type:
        if not isinstance(guess_pulse_params, dict):
            guess_pulse_params = {}
        if (guess_pulse_scaling is not None and
            not 'scaling' in guess_pulse_params):
            guess_pulse_params['scaling'] = guess_pulse_scaling
        if (guess_pulse_offset is not None and
            not 'offset' in guess_pulse_params):
            guess_pulse_params['offset'] = guess_pulse_offset
        if (guess_pulse_action is not None and
            not 'pulse_action' in guess_pulse_params):
            guess_pulse_params['pulse_action'] = guess_pulse_action

    return optimize_pulse(
        drift, ctrls, initial, target,
        num_tslots=num_tslots, evo_time=evo_time, tau=tau,
        amp_lbound=amp_lbound, amp_ubound=amp_ubound,
        fid_err_targ=fid_err_targ, min_grad=0.0,
        max_iter=max_iter, max_wall_time=max_wall_time,
        alg='CRAB', alg_params=alg_params, optim_params=optim_params,
        optim_method=optim_method, method_params=method_params,
        dyn_type=dyn_type, dyn_params=dyn_params,
        prop_type=prop_type, prop_params=prop_params,
        fid_type=fid_type, fid_params=fid_params,
        tslot_type=tslot_type, tslot_params=tslot_params,
        init_pulse_type=guess_pulse_type,
        init_pulse_params=guess_pulse_params,
        ramping_pulse_type=ramping_pulse_type,
        ramping_pulse_params=ramping_pulse_params,
        log_level=log_level, out_file_ext=out_file_ext, gen_stats=gen_stats)

def opt_pulse_crab_unitary(
        H_d, H_c, U_0, U_targ,
        num_tslots=None, evo_time=None, tau=None,
        amp_lbound=None, amp_ubound=None,
        fid_err_targ=1e-5,
        max_iter=500, max_wall_time=180,
        alg_params=None,
        num_coeffs=None, init_coeff_scaling=1.0,
        optim_params=None, optim_method='fmin', method_params=None,
        phase_option='PSU',
        dyn_params=None, prop_params=None, fid_params=None,
        tslot_type='DEF', tslot_params=None,
        guess_pulse_type=None, guess_pulse_params=None,
        guess_pulse_scaling=1.0, guess_pulse_offset=0.0,
        guess_pulse_action='MODULATE',
        ramping_pulse_type=None, ramping_pulse_params=None,
        log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
    """
    Optimise a control pulse to minimise the fidelity error, assuming that the
    dynamics of the system are generated by unitary operators.  This function
    is simply a wrapper for optimize_pulse, where the appropriate options for
    unitary dynamics are chosen and the parameter names are in the format
    familiar to unitary dynamics.  The dynamics of the system  in any given
    timeslot are governed by the combined Hamiltonian, i.e. the sum of the
    ``H_d + ctrl_amp[j]*H_c[j]`` The control pulse is an ``[n_ts, n_ctrls]``
    array of piecewise amplitudes.

    The CRAB algorithm uses basis function coefficents as the variables to
    optimise. It does NOT use any gradient function.  A multivariable
    optimisation algorithm attempts to determines the optimal values for the
    control pulse to minimise the fidelity error.  The fidelity error is some
    measure of distance of the system evolution from the given target evolution
    in the time allowed for the evolution.

    Parameters
    ----------

    H_d : Qobj or list of Qobj
        Drift (aka system) the underlying Hamiltonian of the system can provide
        list (of length num_tslots) for time dependent drift.

    H_c : List of Qobj or array like [num_tslots, evo_time]
        A list of control Hamiltonians. These are scaled by the amplitudes to
        alter the overall dynamics.  Array like imput can be provided for time
        dependent control generators.

    U_0 : Qobj
        Starting point for the evolution.  Typically the identity matrix.

    U_targ : Qobj
        Target transformation, e.g. gate or state, for the time evolution.

    num_tslots : integer or None
        Number of timeslots.  ``None`` implies that timeslots will be given in
        the tau array.

    evo_time : float or None
        Total time for the evolution.  ``None`` implies that timeslots will be
        given in the tau array.

    tau : array[num_tslots] of floats or None
        Durations for the timeslots.  If this is given then ``num_tslots`` and
        ``evo_time`` are derived from it.  ``None`` implies that timeslot
        durations will be equal and calculated as ``evo_time/num_tslots``.

    amp_lbound : float or list of floats
        Lower boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    amp_ubound : float or list of floats
        Upper boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    fid_err_targ : float
        Fidelity error target. Pulse optimisation will terminate when the
        fidelity error falls below this value.

    max_iter : integer
        Maximum number of iterations of the optimisation algorithm.

    max_wall_time : float
        Maximum allowed elapsed time for the optimisation algorithm.

    alg_params : Dictionary
        Options that are specific to the algorithm see above.

    optim_params : Dictionary
        The key value pairs are the attribute name and value used to set
        attribute values.  Note: attributes are created if they do not exist
        already, and are overwritten if they do.  Note: ``method_params`` are
        applied afterwards and so may override these.

    coeff_scaling : float
        Linear scale factor for the random basis coefficients.  By default
        these range from -1.0 to 1.0.  Note this is overridden by
        ``alg_params`` (if given there).

    num_coeffs : integer
        Number of coefficients used for each basis function.  Note this is
        calculated automatically based on the dimension of the dynamics if not
        given. It is crucial to the performance of the algorithm that it is set
        as low as possible, while still giving high enough frequencies.  Note
        this is overridden by ``alg_params`` (if given there).

    optim_method : string
        Multi-variable optimisation method.  The only tested options are 'fmin'
        and 'Nelder-mead'.  In theory any non-gradient method implemented in
        ``scipy.optimize.minimize`` could be used.

    method_params : dict
        Parameters for the ``optim_method``.  Note that where there is an
        attribute of the :obj:`~Optimizer` object or the termination_conditions
        matching the key that attribute. Otherwise, and in some case also, they
        are assumed to be method_options for the ``scipy.optimize.minimize``
        method.  The commonly used parameter are:

        - xtol - limit on variable change for convergence
        - ftol - limit on fidelity error change for convergence

    phase_option : string
        Determines how global phase is treated in fidelity calculations
        (``fid_type='UNIT'`` only). Options:

        - PSU - global phase ignored
        - SU - global phase included

    dyn_params : dict
        Parameters for the :obj:`~Dynamics` object.  The key value pairs are
        assumed to be attribute name value pairs.  They applied after the
        object is created.

    prop_params : dict
        Parameters for the :obj:`~PropagatorComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    fid_params : dict
        Parameters for the :obj:`~FidelityComputer` object.  The key value
        pairs are assumed to be attribute name value pairs.  They applied after
        the object is created.

    tslot_type : string
        Method for computing the dynamics generators, propagators and evolution
        in the timeslots.  Options: DEF, UPDATE_ALL, DYNAMIC.  UPDATE_ALL is
        the only one that currently works.  (See :obj:`~TimeslotComputer`
        classes for details).

    tslot_params : dict
        Parameters for the :obj:`~TimeslotComputer` object The key value pairs
        are assumed to be attribute name value pairs. They applied after the
        object is created.

    guess_pulse_type : string, optional
        Type / shape of pulse(s) used modulate the control amplitudes.
        Options include: RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN.

    guess_pulse_params : dict
        Parameters for the guess pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    guess_pulse_action : string, 'MODULATE'
        Determines how the guess pulse is applied to the pulse generated by the
        basis expansion.  Options are: MODULATE, ADD.

    pulse_scaling : float
        Linear scale factor for generated guess pulses.  By default initial
        pulses are generated with amplitudes in the range (-1.0, 1.0). These
        will be scaled by this parameter.

    pulse_offset : float
        Linear offset for the pulse. That is this value will be added to any
        guess pulses generated.

    ramping_pulse_type : string
        Type of pulse used to modulate the control pulse.  It's intended use
        for a ramping modulation, which is often required in experimental
        setups.  This is only currently implemented in CRAB.  GAUSSIAN_EDGE was
        added for this purpose.

    ramping_pulse_params : dict
        Parameters for the ramping pulse generator object.  The key value pairs
        are assumed to be attribute name value pairs.  They applied after the
        object is created.

    log_level : integer
        Level of messaging output from the logger.  Options are attributes of
        :obj:`qutip.logging_utils`, in decreasing levels of messaging, are:
        DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL.
        Anything WARN or above is effectively 'quiet' execution, assuming
        everything runs as expected.  The default NOTSET implies that the level
        will be taken from the QuTiP settings file, which by default is WARN.

    out_file_ext : string or None
        Files containing the initial and final control pulse amplitudes are
        saved to the current directory.  The default name will be postfixed
        with this extension.  Setting this to None will suppress the output of
        files.

    gen_stats : boolean
        If set to ``True`` then statistics for the optimisation run will be
        generated - accessible through attributes of the stats object.

    Returns
    -------
    opt : OptimResult
        Returns instance of :obj:`~OptimResult`, which has attributes giving
        the reason for termination, final fidelity error, final evolution final
        amplitudes, statistics etc.
    """

    # The parameters are checked in create_pulse_optimizer
    # so no need to do so here

    if log_level == logging.NOTSET:
        log_level = logger.getEffectiveLevel()
    else:
        logger.setLevel(log_level)

    # build the algorithm options
    if not isinstance(alg_params, dict):
        alg_params = {'num_coeffs':num_coeffs,
                       'init_coeff_scaling':init_coeff_scaling}
    else:
        if (num_coeffs is not None and
            not 'num_coeffs' in alg_params):
            alg_params['num_coeffs'] = num_coeffs
        if (init_coeff_scaling is not None and
            not 'init_coeff_scaling' in alg_params):
            alg_params['init_coeff_scaling'] = init_coeff_scaling

    # Build the guess pulse options
    # Any options passed in the guess_pulse_params take precedence
    # over the parameter values.
    if guess_pulse_type:
        if not isinstance(guess_pulse_params, dict):
            guess_pulse_params = {}
        if (guess_pulse_scaling is not None and
            not 'scaling' in guess_pulse_params):
            guess_pulse_params['scaling'] = guess_pulse_scaling
        if (guess_pulse_offset is not None and
            not 'offset' in guess_pulse_params):
            guess_pulse_params['offset'] = guess_pulse_offset
        if (guess_pulse_action is not None and
            not 'pulse_action' in guess_pulse_params):
            guess_pulse_params['pulse_action'] = guess_pulse_action

    return optimize_pulse_unitary(
        H_d, H_c, U_0, U_targ,
        num_tslots=num_tslots, evo_time=evo_time, tau=tau,
        amp_lbound=amp_lbound, amp_ubound=amp_ubound,
        fid_err_targ=fid_err_targ, min_grad=0.0,
        max_iter=max_iter, max_wall_time=max_wall_time,
        alg='CRAB', alg_params=alg_params, optim_params=optim_params,
        optim_method=optim_method, method_params=method_params,
        phase_option=phase_option,
        dyn_params=dyn_params, prop_params=prop_params, fid_params=fid_params,
        tslot_type=tslot_type, tslot_params=tslot_params,
        init_pulse_type=guess_pulse_type,
        init_pulse_params=guess_pulse_params,
        ramping_pulse_type=ramping_pulse_type,
        ramping_pulse_params=ramping_pulse_params,
        log_level=log_level, out_file_ext=out_file_ext, gen_stats=gen_stats)

def create_pulse_optimizer(
        drift, ctrls, initial, target,
        num_tslots=None, evo_time=None, tau=None,
        amp_lbound=None, amp_ubound=None,
        fid_err_targ=1e-10, min_grad=1e-10,
        max_iter=500, max_wall_time=180,
        alg='GRAPE', alg_params=None,
        optim_params=None, optim_method='DEF', method_params=None,
        optim_alg=None, max_metric_corr=None, accuracy_factor=None,
        dyn_type='GEN_MAT', dyn_params=None,
        prop_type='DEF', prop_params=None,
        fid_type='DEF', fid_params=None,
        phase_option=None, fid_err_scale_factor=None,
        tslot_type='DEF', tslot_params=None,
        amp_update_mode=None,
        init_pulse_type='DEF', init_pulse_params=None,
        pulse_scaling=1.0, pulse_offset=0.0,
        ramping_pulse_type=None, ramping_pulse_params=None,
        log_level=logging.NOTSET, gen_stats=False):

    """
    Generate the objects of the appropriate subclasses required for the pulse
    optmisation based on the parameters given Note this method may be
    preferable to calling optimize_pulse if more detailed configuration is
    required before running the optmisation algorthim, or the algorithm will be
    run many times, for instances when trying to finding global the optimum or
    minimum time optimisation

    Parameters
    ----------
    drift : Qobj or list of Qobj
        The underlying dynamics generator of the system can provide list (of
        length num_tslots) for time dependent drift.

    ctrls : List of Qobj or array like [num_tslots, evo_time]
        A list of control dynamics generators. These are scaled by the
        amplitudes to alter the overall dynamics.  Array-like input can be
        provided for time dependent control generators.

    initial : Qobj
        Starting point for the evolution.  Typically the identity matrix.

    target : Qobj
        Target transformation, e.g. gate or state, for the time evolution.

    num_tslots : integer or None
        Number of timeslots.  ``None`` implies that timeslots will be given in
        the tau array.

    evo_time : float or None
        Total time for the evolution.  ``None`` implies that timeslots will be
        given in the tau array.

    tau : array[num_tslots] of floats or None
        Durations for the timeslots.  If this is given then ``num_tslots`` and
        ``evo_time`` are dervived from it.  ``None`` implies that timeslot
        durations will be equal and calculated as ``evo_time/num_tslots``.

    amp_lbound : float or list of floats
        Lower boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    amp_ubound : float or list of floats
        Upper boundaries for the control amplitudes.  Can be a scalar value
        applied to all controls or a list of bounds for each control.

    fid_err_targ : float
        Fidelity error target. Pulse optimisation will terminate when the
        fidelity error falls below this value.

    mim_grad : float
        Minimum gradient. When the sum of the squares of the gradients wrt to
        the control amplitudes falls below this value, the optimisation
        terminates, assuming local minima.

    max_iter : integer
        Maximum number of iterations of the optimisation algorithm.

    max_wall_time : float
        Maximum allowed elapsed time for the optimisation algorithm.

    alg : string
        Algorithm to use in pulse optimisation.
        Options are:

        - 'GRAPE' (default) - GRadient Ascent Pulse Engineering
        - 'CRAB' - Chopped RAndom Basis

    alg_params : Dictionary
        options that are specific to the algorithm see above

    optim_params : Dictionary
        The key value pairs are the attribute name and value used to set
        attribute values.  Note: attributes are created if they do not exist
        already, and are overwritten if they do.  Note: method_params are
        applied afterwards and so may override these.

    optim_method : string
        a scipy.optimize.minimize method that will be used to optimise
        the pulse for minimum fidelity error
        Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result
        in calling these specific scipy.optimize methods
        Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards
        capatibility reasons.
        Supplying DEF will given alg dependent result:

        - GRAPE - Default optim_method is FMIN_L_BFGS_B
        - CRAB - Default optim_method is Nelder-Mead

    method_params : dict
        Parameters for the optim_method.
        Note that where there is an attribute of the
        Optimizer object or the termination_conditions matching the key
        that attribute. Otherwise, and in some case also,
        they are assumed to be method_options
        for the scipy.optimize.minimize method.

    optim_alg : string
        Deprecated. Use optim_method.

    max_metric_corr : integer
        Deprecated. Use method_params instead

    accuracy_factor : float
        Deprecated. Use method_params instead

    dyn_type : string
        Dynamics type, i.e. the type of matrix used to describe
        the dynamics. Options are UNIT, GEN_MAT, SYMPL
        (see Dynamics classes for details)

    dyn_params : dict
        Parameters for the Dynamics object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    prop_type : string
        Propagator type i.e. the method used to calculate the
        propagtors and propagtor gradient for each timeslot
        options are DEF, APPROX, DIAG, FRECHET, AUG_MAT
        DEF will use the default for the specific dyn_type
        (see PropagatorComputer classes for details)

    prop_params : dict
        Parameters for the PropagatorComputer object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    fid_type : string
        Fidelity error (and fidelity error gradient) computation method
        Options are DEF, UNIT, TRACEDIFF, TD_APPROX
        DEF will use the default for the specific dyn_type
        (See FidelityComputer classes for details)

    fid_params : dict
        Parameters for the FidelityComputer object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    phase_option : string
        Deprecated. Pass in fid_params instead.

    fid_err_scale_factor : float
        Deprecated. Use scale_factor key in fid_params instead.

    tslot_type : string
        Method for computing the dynamics generators, propagators and
        evolution in the timeslots.
        Options: DEF, UPDATE_ALL, DYNAMIC
        UPDATE_ALL is the only one that currently works
        (See TimeslotComputer classes for details)

    tslot_params : dict
        Parameters for the TimeslotComputer object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    amp_update_mode : string
        Deprecated. Use tslot_type instead.

    init_pulse_type : string
        type / shape of pulse(s) used to initialise the
        the control amplitudes.
        Options (GRAPE) include:

            RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
            DEF is RND

        (see PulseGen classes for details)
        For the CRAB the this the guess_pulse_type.

    init_pulse_params : dict
        Parameters for the initial / guess pulse generator object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    pulse_scaling : float
        Linear scale factor for generated initial / guess pulses
        By default initial pulses are generated with amplitudes in the
        range (-1.0, 1.0). These will be scaled by this parameter

    pulse_offset : float
        Linear offset for the pulse. That is this value will be added
        to any initial / guess pulses generated.

    ramping_pulse_type : string
        Type of pulse used to modulate the control pulse.
        It's intended use for a ramping modulation, which is often required in
        experimental setups.
        This is only currently implemented in CRAB.
        GAUSSIAN_EDGE was added for this purpose.

    ramping_pulse_params : dict
        Parameters for the ramping pulse generator object
        The key value pairs are assumed to be attribute name value pairs
        They applied after the object is created

    log_level : integer
        level of messaging output from the logger.
        Options are attributes of qutip.logging_utils,
        in decreasing levels of messaging, are:
        DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL
        Anything WARN or above is effectively 'quiet' execution,
        assuming everything runs as expected.
        The default NOTSET implies that the level will be taken from
        the QuTiP settings file, which by default is WARN

    gen_stats : boolean
        if set to True then statistics for the optimisation
        run will be generated - accessible through attributes
        of the stats object

    Returns
    -------
    opt : Optimizer
        Instance of an Optimizer, through which the
        Config, Dynamics, PulseGen, and TerminationConditions objects
        can be accessed as attributes.
        The PropagatorComputer, FidelityComputer and TimeslotComputer objects
        can be accessed as attributes of the Dynamics object, e.g. optimizer.dynamics.fid_computer
        The optimisation can be run through the optimizer.run_optimization

    """

    # check parameters
    ctrls = dynamics._check_ctrls_container(ctrls)
    dynamics._check_drift_dyn_gen(drift)

    if not isinstance(initial, Qobj):
        raise TypeError("initial must be a Qobj")

    if not isinstance(target, Qobj):
        raise TypeError("target must be a Qobj")

    # Deprecated parameter management
    if not optim_alg is None:
        optim_method = optim_alg
        _param_deprecation(
            "The 'optim_alg' parameter is deprecated. "
            "Use 'optim_method' instead")

    if not max_metric_corr is None:
        if isinstance(method_params, dict):
            if not 'max_metric_corr' in method_params:
                 method_params['max_metric_corr'] = max_metric_corr
        else:
            method_params = {'max_metric_corr':max_metric_corr}
        _param_deprecation(
            "The 'max_metric_corr' parameter is deprecated. "
            "Use 'max_metric_corr' in method_params instead")

    if not accuracy_factor is None:
        if isinstance(method_params, dict):
            if not 'accuracy_factor' in method_params:
                 method_params['accuracy_factor'] = accuracy_factor
        else:
            method_params = {'accuracy_factor':accuracy_factor}
        _param_deprecation(
            "The 'accuracy_factor' parameter is deprecated. "
            "Use 'accuracy_factor' in method_params instead")

    # phase_option
    if not phase_option is None:
        if isinstance(fid_params, dict):
            if not 'phase_option' in fid_params:
                 fid_params['phase_option'] = phase_option
        else:
            fid_params = {'phase_option':phase_option}
        _param_deprecation(
            "The 'phase_option' parameter is deprecated. "
            "Use 'phase_option' in fid_params instead")

    # fid_err_scale_factor
    if not fid_err_scale_factor is None:
        if isinstance(fid_params, dict):
            if not 'fid_err_scale_factor' in fid_params:
                 fid_params['scale_factor'] = fid_err_scale_factor
        else:
            fid_params = {'scale_factor':fid_err_scale_factor}
        _param_deprecation(
            "The 'fid_err_scale_factor' parameter is deprecated. "
            "Use 'scale_factor' in fid_params instead")

    # amp_update_mode
    if not amp_update_mode is None:
        amp_update_mode_up = _upper_safe(amp_update_mode)
        if amp_update_mode_up == 'ALL':
            tslot_type = 'UPDATE_ALL'
        else:
            tslot_type = amp_update_mode
        _param_deprecation(
            "The 'amp_update_mode' parameter is deprecated. "
            "Use 'tslot_type' instead")

    # set algorithm defaults
    alg_up = _upper_safe(alg)
    if alg is None:
        raise errors.UsageError(
            "Optimisation algorithm must be specified through 'alg' parameter")
    elif alg_up == 'GRAPE':
        if optim_method is None or optim_method.upper() == 'DEF':
            optim_method = 'FMIN_L_BFGS_B'
        if init_pulse_type is None or init_pulse_type.upper() == 'DEF':
            init_pulse_type = 'RND'
    elif alg_up == 'CRAB':
        if optim_method is None or optim_method.upper() == 'DEF':
            optim_method = 'FMIN'
        if prop_type is None or prop_type.upper() == 'DEF':
            prop_type = 'APPROX'
        if init_pulse_type is None or init_pulse_type.upper() == 'DEF':
            init_pulse_type = None
    else:
        raise errors.UsageError(
            "No option for pulse optimisation algorithm alg={}".format(alg))

    cfg = optimconfig.OptimConfig()
    cfg.optim_method = optim_method
    cfg.dyn_type = dyn_type
    cfg.prop_type = prop_type
    cfg.fid_type = fid_type
    cfg.init_pulse_type = init_pulse_type

    if log_level == logging.NOTSET:
        log_level = logger.getEffectiveLevel()
    else:
        logger.setLevel(log_level)

    cfg.log_level = log_level

    # Create the Dynamics instance
    if dyn_type == 'GEN_MAT' or dyn_type is None or dyn_type == '':
        dyn = dynamics.DynamicsGenMat(cfg)
    elif dyn_type == 'UNIT':
        dyn = dynamics.DynamicsUnitary(cfg)
    elif dyn_type == 'SYMPL':
        dyn = dynamics.DynamicsSymplectic(cfg)
    else:
        raise errors.UsageError("No option for dyn_type: " + dyn_type)
    dyn.apply_params(dyn_params)
    dyn._drift_dyn_gen_checked = True
    dyn._ctrl_dyn_gen_checked = True

    # Create the PropagatorComputer instance
    # The default will be typically be the best option
    if prop_type == 'DEF' or prop_type is None or prop_type == '':
        # Do nothing use the default for the Dynamics
        pass
    elif prop_type == 'APPROX':
        if not isinstance(dyn.prop_computer, propcomp.PropCompApproxGrad):
            dyn.prop_computer = propcomp.PropCompApproxGrad(dyn)
    elif prop_type == 'DIAG':
        if not isinstance(dyn.prop_computer, propcomp.PropCompDiag):
            dyn.prop_computer = propcomp.PropCompDiag(dyn)
    elif prop_type == 'AUG_MAT':
        if not isinstance(dyn.prop_computer, propcomp.PropCompAugMat):
            dyn.prop_computer = propcomp.PropCompAugMat(dyn)
    elif prop_type == 'FRECHET':
        if not isinstance(dyn.prop_computer, propcomp.PropCompFrechet):
            dyn.prop_computer = propcomp.PropCompFrechet(dyn)
    else:
        raise errors.UsageError("No option for prop_type: " + prop_type)
    dyn.prop_computer.apply_params(prop_params)

    # Create the FidelityComputer instance
    # The default will be typically be the best option
    # Note: the FidCompTraceDiffApprox is a subclass of FidCompTraceDiff
    # so need to check this type first
    fid_type_up = _upper_safe(fid_type)
    if fid_type_up == 'DEF' or fid_type_up is None or fid_type_up == '':
        # None given, use the default for the Dynamics
        pass
    elif fid_type_up == 'TDAPPROX':
        if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiffApprox):
            dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
    elif fid_type_up == 'TRACEDIFF':
        if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
            dyn.fid_computer = fidcomp.FidCompTraceDiff(dyn)
    elif fid_type_up == 'UNIT':
        if not isinstance(dyn.fid_computer, fidcomp.FidCompUnitary):
            dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
    else:
        raise errors.UsageError("No option for fid_type: " + fid_type)
    dyn.fid_computer.apply_params(fid_params)

    # Currently the only working option for tslot computer is
    # TSlotCompUpdateAll.
    # so just apply the parameters
    dyn.tslot_computer.apply_params(tslot_params)

    # Create the Optimiser instance
    optim_method_up = _upper_safe(optim_method)
    if optim_method is None or optim_method_up == '':
        raise errors.UsageError("Optimisation method must be specified "
                                "via 'optim_method' parameter")
    elif optim_method_up == 'FMIN_BFGS':
        optim = optimizer.OptimizerBFGS(cfg, dyn)
    elif optim_method_up == 'LBFGSB' or optim_method_up == 'FMIN_L_BFGS_B':
        optim = optimizer.OptimizerLBFGSB(cfg, dyn)
    elif optim_method_up == 'FMIN':
        if alg_up == 'CRAB':
            optim = optimizer.OptimizerCrabFmin(cfg, dyn)
        else:
            raise errors.UsageError(
                "Invalid optim_method '{}' for '{}' algorthim".format(
                                    optim_method, alg))
    else:
        # Assume that the optim_method is a valid
        #scipy.optimize.minimize method
        # Choose an optimiser based on the algorithm
        if alg_up == 'CRAB':
            optim = optimizer.OptimizerCrab(cfg, dyn)
        else:
            optim = optimizer.Optimizer(cfg, dyn)

    optim.alg = alg
    optim.method = optim_method
    optim.amp_lbound = amp_lbound
    optim.amp_ubound = amp_ubound
    optim.apply_params(optim_params)

    # Create the TerminationConditions instance
    tc = termcond.TerminationConditions()
    tc.fid_err_targ = fid_err_targ
    tc.min_gradient_norm = min_grad
    tc.max_iterations = max_iter
    tc.max_wall_time = max_wall_time
    optim.termination_conditions = tc

    optim.apply_method_params(method_params)

    if gen_stats:
        # Create a stats object
        # Note that stats object is optional
        # if the Dynamics and Optimizer stats attribute is not set
        # then no stats will be collected, which could improve performance
        if amp_update_mode == 'DYNAMIC':
            sts = stats.StatsDynTsUpdate()
        else:
            sts = stats.Stats()

        dyn.stats = sts
        optim.stats = sts

    # Configure the dynamics
    dyn.drift_dyn_gen = drift
    dyn.ctrl_dyn_gen = ctrls
    dyn.initial = initial
    dyn.target = target
    if tau is None:
        # Check that parameters have been supplied to generate the
        # timeslot durations
        try:
            evo_time / num_tslots
        except:
            raise errors.UsageError(
                "Either the timeslot durations should be supplied as an "
                "array 'tau' or the number of timeslots 'num_tslots' "
                "and the evolution time 'evo_time' must be given.")

        dyn.num_tslots = num_tslots
        dyn.evo_time = evo_time
    else:
        dyn.tau = tau

    # this function is called, so that the num_ctrls attribute will be set
    n_ctrls = dyn.num_ctrls

    ramping_pgen = None
    if ramping_pulse_type:
        ramping_pgen = pulsegen.create_pulse_gen(
                            pulse_type=ramping_pulse_type, dyn=dyn,
                            pulse_params=ramping_pulse_params)
    if alg_up == 'CRAB':
        # Create a pulse generator for each ctrl
        crab_pulse_params = None
        num_coeffs = None
        init_coeff_scaling = None
        if isinstance(alg_params, dict):
            num_coeffs = alg_params.get('num_coeffs')
            init_coeff_scaling = alg_params.get('init_coeff_scaling')
            if 'crab_pulse_params' in alg_params:
                crab_pulse_params = alg_params.get('crab_pulse_params')

        guess_pulse_type = init_pulse_type
        if guess_pulse_type:
            guess_pulse_action = None
            guess_pgen = pulsegen.create_pulse_gen(
                                pulse_type=guess_pulse_type, dyn=dyn)
            guess_pgen.scaling = pulse_scaling
            guess_pgen.offset = pulse_offset
            if init_pulse_params is not None:
                guess_pgen.apply_params(init_pulse_params)
                guess_pulse_action = init_pulse_params.get('pulse_action')

        optim.pulse_generator = []
        for j in range(n_ctrls):
            crab_pgen = pulsegen.PulseGenCrabFourier(
                                dyn=dyn, num_coeffs=num_coeffs)
            if init_coeff_scaling is not None:
                crab_pgen.scaling = init_coeff_scaling
            if isinstance(crab_pulse_params, dict):
                crab_pgen.apply_params(crab_pulse_params)

            lb = None
            if amp_lbound:
                if isinstance(amp_lbound, list):
                    try:
                        lb = amp_lbound[j]
                    except:
                        lb = amp_lbound[-1]
                else:
                    lb = amp_lbound
            ub = None
            if amp_ubound:
                if isinstance(amp_ubound, list):
                    try:
                        ub = amp_ubound[j]
                    except:
                        ub = amp_ubound[-1]
                else:
                    ub = amp_ubound
            crab_pgen.lbound = lb
            crab_pgen.ubound = ub

            if guess_pulse_type:
                guess_pgen.lbound = lb
                guess_pgen.ubound = ub
                crab_pgen.guess_pulse = guess_pgen.gen_pulse()
                if guess_pulse_action:
                    crab_pgen.guess_pulse_action = guess_pulse_action

            if ramping_pgen:
                crab_pgen.ramping_pulse = ramping_pgen.gen_pulse()

            optim.pulse_generator.append(crab_pgen)
        #This is just for the debug message now
        pgen = optim.pulse_generator[0]

    else:
        # Create a pulse generator of the type specified
        pgen = pulsegen.create_pulse_gen(pulse_type=init_pulse_type, dyn=dyn,
                                        pulse_params=init_pulse_params)
        pgen.scaling = pulse_scaling
        pgen.offset = pulse_offset
        pgen.lbound = amp_lbound
        pgen.ubound = amp_ubound

        optim.pulse_generator = pgen

    if log_level <= logging.DEBUG:
        logger.debug(
            "Optimisation config summary...\n"
            "  object classes:\n"
            "    optimizer: " + optim.__class__.__name__ +
            "\n    dynamics: " + dyn.__class__.__name__ +
            "\n    tslotcomp: " + dyn.tslot_computer.__class__.__name__ +
            "\n    fidcomp: " + dyn.fid_computer.__class__.__name__ +
            "\n    propcomp: " + dyn.prop_computer.__class__.__name__ +
            "\n    pulsegen: " + pgen.__class__.__name__)
    return optim
