class_docstr: The results of a minimization.
  
  Minimization results include data such as status and error messages,
  fit statistics, and the updated (i.e., best-fit) parameters themselves
  in the :attr:`params` attribute.
  
  The list of (possible) `MinimizerResult` attributes is given below:
  
  Attributes
  ----------
  residual : numpy.ndarray
      Residual array :math:`{\rm Resid_i}`. Return value of the objective
      function when using the best-fit values of the parameters.
  params : Parameters
      The best-fit Parameters resulting from the fit.
  uvars : dict
      Dictionary of uncertainties ufloats from Parameters
  var_names : list
      list of variable Parameter names used in optimization in the
      same order as the values in :attr:`init_vals` and :attr:`covar`.
  covar : numpy.ndarray or None
      Covariance matrix from minimization, with rows and columns
      corresponding to :attr:`var_names`.  If uncertainties cannot
      be determined, this value will be ``None``.
  init_vals : list
      List of initial values for variable parameters using
      :attr:`var_names`.
  init_values : dict
      Dictionary of initial values for variable parameters.
  aborted : bool
      Whether the fit was aborted.
  status : int
      Termination status of the optimizer. Its value depends on the
      underlying solver. Refer to `message` for details.
  success : bool
      True if the fit succeeded, otherwise False. This is an optimistic
      view of success, meaning that the method finished without error.
  errorbars : bool
      whether uncertainties were estimated for variable Parameters.
  message : str
      Message about fit success.
  ier : int
      Integer error value from :scipydoc:`optimize.leastsq` (`'leastsq'`
      method only).
  lmdif_message : str
      Message from :scipydoc:`optimize.leastsq` (`'leastsq'` method only).
  call_kws : dict
      Keyword arguments sent to underlying solver.
  flatchain : pandas.DataFrame
      A flatchain view of the sampling chain from the `emcee` method.
  nfev : int
      Number of function evaluations.
  nvarys : int
      Number of variables in fit: :math:`N_{\rm varys}`.
  ndata : int
      Number of data points: :math:`N`.
  nfree : int
      Degrees of freedom in fit: :math:`N - N_{\rm varys}`.
  chisqr : float
      Chi-square: :math:`\chi^2 = \sum_i^N [{\rm Resid}_i]^2`.
  redchi : float
      Reduced chi-square:
      :math:`\chi^2_{\nu}= {\chi^2} / {(N - N_{\rm varys})}`.
  aic : float
      Akaike Information Criterion statistic:
      :math:`N \ln(\chi^2/N) + 2 N_{\rm varys}`.
  bic : float
      Bayesian Information Criterion statistic:
      :math:`N \ln(\chi^2/N) + \ln(N) N_{\rm varys}`.
  
  Methods
  -------
  show_candidates
      :meth:`pretty_print` representation of candidates from the `brute`
      fitting method.
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code: def __init__(self, **kws):
      for key, val in kws.items():
          setattr(self, key, val)
  
imports: from collections import namedtuple
  from copy import deepcopy
  import inspect
  import multiprocessing
  import numbers
  import warnings
  import numpy
  from scipy.linalg import LinAlgError, inv
  from scipy.optimize import basinhopping
  from scipy.optimize import brute
  from scipy.optimize import differential_evolution
  from scipy.optimize import dual_annealing
  from scipy.optimize import least_squares
  from scipy.optimize import leastsq
  from scipy.optimize import minimize
  from scipy.optimize import shgo
  from scipy.sparse import issparse
  from scipy.sparse.linalg import LinearOperator
  from scipy.stats import cauchy
  from scipy.stats import norm
  from ._ampgo import ampgo
  from .parameter import Parameter, Parameters
  from .printfuncs import fitreport_html_table
label_desc: Use image node_id 1 to create a new MinimizerResult object  with example: obj = MinimizerResult()