(self, points)
return sparse.coo_matrix((weights.ravel(), (rows, cols)),
                                 shape=(npoints, self.discretization.nindex))
Obtain function values at points from triangulation.

        This function returns a sparse matrix that, when multiplied
        with the vector with all the function values on the vertices,
        returns the function values at points.

        Parameters
        ----------
        points : 2d array
            Each row represents one point.

        Returns
        -------
        values
            A sparse matrix B so that evaluate(points) = B.dot(parameters).
Obtain function values at points from triangulation.
Obtain function values at points from triangulation .
def parameter_derivative(self, points):
        """
        Obtain function values at points from triangulation.

        This function returns a sparse matrix that, when multiplied
        with the vector with all the function values on the vertices,
        returns the function values at points.

        Parameters
        ----------
        points : 2d array
            Each row represents one point.

        Returns
        -------
        values
            A sparse matrix B so that evaluate(points) = B.dot(parameters).

        """
        points = np.atleast_2d(points)
        weights, simplices = self._get_weights(points)
        # Construct sparse matrix for optimization

        nsimp = self.input_dim + 1
        npoints = len(simplices)
        # Indices of constraints (nsimp points per simplex, so we have nsimp
        # values in each row; one for each simplex)
        rows = np.repeat(np.arange(len(points)), nsimp)
        cols = simplices.ravel()

        return sparse.coo_matrix((weights.ravel(), (rows, cols)),
                                 shape=(npoints, self.discretization.nindex))
def parameter_derivative ( self , points ) : points = np . atleast_2d ( points ) weights , simplices = self . _get_weights ( points ) # Construct sparse matrix for optimization nsimp = self . input_dim + 1 npoints = len ( simplices ) # Indices of constraints (nsimp points per simplex, so we have nsimp # values in each row; one for each simplex) rows = np . repeat ( np . arange ( len ( points ) ) , nsimp ) cols = simplices . ravel ( ) return sparse . coo_matrix ( ( weights . ravel ( ) , ( rows , cols ) ) , shape = ( npoints , self . discretization . nindex ) )