docstr: Returns the topographical vector stemming from the specified value
  ``x_min`` for the current feasible set ``X_min`` with True boolean
  values indicating positive entries and False values indicating
  negative entries.
docstr_tok: ["Returns","the","topographical","vector","stemming","from","the","specified","value","``","x_min","``","for","the","current","feasible","set","``","X_min","``","with","True","boolean","values","indicating","positive","entries","and","False","values","indicating","negative","entries","."]
code: def g_topograph(self, x_min, X_min):
      """
      Returns the topographical vector stemming from the specified value
      ``x_min`` for the current feasible set ``X_min`` with True boolean
      values indicating positive entries and False values indicating
      negative entries.
  
      """
      x_min = np.array([x_min])
      self.Y = spatial.distance.cdist(x_min, X_min, "euclidean")
      # Find sorted indexes of spatial distances:
      self.Z = np.argsort(self.Y, axis=-1)
  
      self.Ss = X_min[self.Z][0]
      self.minimizer_pool = self.minimizer_pool[self.Z]
      self.minimizer_pool = self.minimizer_pool[0]
      return self.Ss
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import time
  import logging
  import warnings
  from scipy import spatial
  from scipy.optimize import OptimizeResult, minimize
  from scipy.optimize._shgo_lib import sobol_seq
  from scipy.optimize._shgo_lib.triangulation import Complex
label_desc: Use image node_id 20 for calling the SHGO obj's underlying member method code with example usage: obj.g_topograph(x_min, X_min) and returns: self