original_docstring: Returns the kernel density estimate for point x based on x-values
          xs
          
docstring: Returns the kernel density estimate for point x based on x-values
  xs
docstring_tokens: Returns the kernel density estimate for point x based on x - values xs
code: def density(self, xs, x):
          """Returns the kernel density estimate for point x based on x-values
          xs
          """
          xs = np.asarray(xs)
          n = len(xs) # before inDomain?
          xs = self.inDomain( xs, xs, x )[0]
          if xs.ndim == 1:
              xs = xs[:,None]
          if len(xs)>0:
              h = self.h
              w = 1/h * np.mean(self((xs-x)/h), axis=0)
              return w
          else:
              return np.nan
code_tokens: def density ( self , xs , x ) : xs = np . asarray ( xs ) n = len ( xs ) xs = self . inDomain ( xs , xs , x ) [ 0 ] if xs . ndim == 1 : xs = xs [ : , None ] if len ( xs ) > 0 : h = self . h w = 1 / h * np . mean ( self ( ( xs - x ) / h ) , axis = 0 ) return w else : return np . nan
short_docstring: Returns the kernel density estimate for point x based on x-values
  xs
short_docstring_tokens: Returns the kernel density estimate for point x based on x - values xs
comment: """Returns the kernel density estimate for point x based on x-values
          xs
          """$SEP$# before inDomain?