class_docstr: Class used to record the energy state. At any time, it knows what is the
  currently used coordinates and the most recent best location.
  
  Parameters
  ----------
  lower : array_like
      A 1-D numpy ndarray containing lower bounds for generating an initial
      random components in the `reset` method.
  upper : array_like
      A 1-D numpy ndarray containing upper bounds for generating an initial
      random components in the `reset` method
      components. Neither NaN or inf are allowed.
  callback : callable, ``callback(x, f, context)``, optional
      A callback function which will be called for all minima found.
      ``x`` and ``f`` are the coordinates and function value of the
      latest minimum found, and `context` has value in [0, 1, 2]
class_docstr_tok: ["Class","used","to","record","the","energy","state",".","At","any","time",",","it","knows","what","is","the","currently","used","coordinates","and","the","most","recent","best","location",".","Parameters","--","--","--","--","--","lower",":","array_like","A","1-D","numpy","ndarray","containing","lower","bounds","for","generating","an","initial","random","components","in","the","`","reset","`","method",".","upper",":","array_like","A","1-D","numpy","ndarray","containing","upper","bounds","for","generating","an","initial","random","components","in","the","`","reset","`","method","components",".","Neither","NaN","or","inf","are","allowed",".","callback",":","callable",",","``","callback","(","x",",","f",",","context",")","``",",","optional","A","callback","function","which","will","be","called","for","all","minima","found",".","``","x","``","and","``","f","``","are","the","coordinates","and","function","value","of","the","latest","minimum","found",",","and","`","context","`","has","value","in","[","0",",","1",",","2","]"]
code: def __init__(self, lower, upper, callback=None):
      self.ebest = None
      self.current_energy = None
      self.current_location = None
      self.xbest = None
      self.lower = lower
      self.upper = upper
      self.callback = callback
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from scipy.optimize import OptimizeResult
  from scipy.optimize import minimize
  from scipy.special import gammaln
  from scipy._lib._util import check_random_state
label_desc: Use image node_id 1 to create a new EnergyState object from inherited base classes: object with example: obj = EnergyState(lower, upper, callback)