# Source code for qutip.control.optimresult

    # -*- coding: utf-8 -*-
    # @author: Alexander Pitchford
    # @email1: agp1@aber.ac.uk
    # @email2: alex.pitchford@gmail.com
    # @organization: Aberystwyth University
    # @supervisor: Daniel Burgarth

    """
    Class containing the results of the pulse optimisation
    """

    import numpy as np

    class OptimResult(object):
        """
        Attributes give the result of the pulse optimisation attempt

        Attributes
        ----------
        termination_reason : string
            Description of the reason for terminating the optimisation

        fidelity : float
            final (normalised) fidelity that was achieved

        initial_fid_err : float
            fidelity error before optimisation starting

        fid_err : float
            final fidelity error that was achieved

        goal_achieved : boolean
            True is the fidely error achieved was below the target

        grad_norm_final : float
            Final value of the sum of the squares of the (normalised) fidelity
            error gradients

        grad_norm_min_reached : float
            True if the optimisation terminated due to the minimum value
            of the gradient being reached

        num_iter : integer
            Number of iterations of the optimisation algorithm completed

        max_iter_exceeded : boolean
            True if the iteration limit was reached

        max_fid_func_exceeded : boolean
            True if the fidelity function call limit was reached

        wall_time : float
            time elapsed during the optimisation

        wall_time_limit_exceeded  : boolean
            True if the wall time limit was reached

        time : array[num_tslots+1] of float
            Time are the start of each timeslot
            with the final value being the total evolution time

        initial_amps : array[num_tslots, n_ctrls]
            The amplitudes at the start of the optimisation

        final_amps : array[num_tslots, n_ctrls]
            The amplitudes at the end of the optimisation

        evo_full_final : Qobj
            The evolution operator from t=0 to t=T based on the final amps

        evo_full_initial : Qobj
            The evolution operator from t=0 to t=T based on the initial amps

        stats : Stats
            Object contaning the stats for the run (if any collected)

        optimizer : Optimizer
            Instance of the Optimizer used to generate the result
        """
        def __init__(self):
            self.reset()

        def reset(self):
            self.fidelity = 0.0
            self.initial_fid_err = np.Inf
            self.fid_err = np.Inf
            self.goal_achieved = False
            self.grad_norm_final = 0.0
            self.grad_norm_min_reached = False
            self.num_iter = 0
            self.max_iter_exceeded = False
            self.num_fid_func_calls = 0
            self.max_fid_func_exceeded = False
            self.wall_time = 0.0
            self.wall_time_limit_exceeded = False
            self.termination_reason = "not started yet"
            self.time = None
            self.initial_amps = None
            self.final_amps = None
            self.evo_full_final = None
            self.evo_full_initial = None
            self.stats = None
            self.optimizer = None