class_docstr: A general minimizer for curve fitting and optimization.
class_docstr_tok: ["A","general","minimizer","for","curve","fitting","and","optimization","."]
docstr: Transform covariance matrix to external parameter space.
  
  It makes use of the gradient scaling according to the MINUIT
  recipe:
  
      cov_ext = np.dot(grad.T, grad) * cov_int
  
  Parameters
  ----------
  cov_int : numpy.ndarray
      Covariance matrix in the internal parameter space.
  fvars : numpy.ndarray
      Array of the optimal internal, freely variable, parameter
      values.
  
  Returns
  -------
  cov_ext : numpy.ndarray
      Covariance matrix, transformed to external parameter space.
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code: def _int2ext_cov_x(self, cov_int, fvars):
      """Transform covariance matrix to external parameter space.
  
      It makes use of the gradient scaling according to the MINUIT
      recipe:
  
          cov_ext = np.dot(grad.T, grad) * cov_int
  
      Parameters
      ----------
      cov_int : numpy.ndarray
          Covariance matrix in the internal parameter space.
      fvars : numpy.ndarray
          Array of the optimal internal, freely variable, parameter
          values.
  
      Returns
      -------
      cov_ext : numpy.ndarray
          Covariance matrix, transformed to external parameter space.
  
      """
      g = [
          self.result.params[name].scale_gradient(fvars[i])
          for i, name in enumerate(self.result.var_names)
      ]
      grad2d = np.atleast_2d(g)
      grad = np.dot(grad2d.T, grad2d)
  
      cov_ext = cov_int * grad
      return cov_ext
  
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 10 for calling the Minimizer obj's underlying member method code with example usage: obj._int2ext_cov_x(cov_int, fvars) and returns: cov_ext