# 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
[docs]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
```