parameters: (self, infty_cons_sampl=False)
docstring: Sample the function surface.
          
          There are 2 modes, if ``infty_cons_sampl`` is True then the sampled
          points that are generated outside the feasible domain will be
          assigned an ``inf`` value in accordance with SHGO rules.
          This guarantees convergence and usually requires less objective function
          evaluations at the computational costs of more Delaunay triangulation
          points.
  
          If ``infty_cons_sampl`` is False then the infeasible points are discarded
          and only a subspace of the sampled points are used. This comes at the
          cost of the loss of guaranteed convergence and usually requires more
          objective function evaluations.
docstring_summary: Sample the function surface.
          
          There are 2 modes, if ``infty_cons_sampl`` is True then the sampled
          points that are generated outside the feasible domain will be
          assigned an ``inf`` value in accordance with SHGO rules.
          This guarantees convergence and usually requires less objective function
          evaluations at the computational costs of more Delaunay triangulation
          points.
docstring_tokens: Sample the function surface . There are 2 modes if infty_cons_sampl is True then the sampled points that are generated outside the feasible domain will be assigned an inf value in accordance with SHGO rules . This guarantees convergence and usually requires less objective function evaluations at the computational costs of more Delaunay triangulation points .
function: def sampled_surface(self, infty_cons_sampl=False):
          """
          Sample the function surface.
          
          There are 2 modes, if ``infty_cons_sampl`` is True then the sampled
          points that are generated outside the feasible domain will be
          assigned an ``inf`` value in accordance with SHGO rules.
          This guarantees convergence and usually requires less objective function
          evaluations at the computational costs of more Delaunay triangulation
          points.
  
          If ``infty_cons_sampl`` is False then the infeasible points are discarded
          and only a subspace of the sampled points are used. This comes at the
          cost of the loss of guaranteed convergence and usually requires more
          objective function evaluations.
          """
          # Generate sampling points
          if self.disp:
              print('Generating sampling points')
          self.sampling(self.nc, self.dim)
  
          if not infty_cons_sampl:
              # Find subspace of feasible points
              if self.g_cons is not None:
                  self.sampling_subspace()
  
          # Sort remaining samples
          self.sorted_samples()
  
          # Find objective function references
          self.fun_ref()
  
          self.n_sampled = self.nc
function_tokens: def sampled_surface ( self , infty_cons_sampl = False ) : # Generate sampling points if self . disp : print ( 'Generating sampling points' ) self . sampling ( self . nc , self . dim ) if not infty_cons_sampl : # Find subspace of feasible points if self . g_cons is not None : self . sampling_subspace ( ) # Sort remaining samples self . sorted_samples ( ) # Find objective function references self . fun_ref ( ) self . n_sampled = self . nc