(self, points)
return sparse.coo_matrix((weights, (rows, cols)),
                                 shape=(npoints, self.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 : ndarray
            A 2d array where 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 : ndarray
            A 2d array where each row represents one point.

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

        """
        npoints = len(points)
        weights = np.ones(npoints, dtype=np.int)
        rows = np.arange(npoints)
        cols = self.discretization.state_to_index(points)
        return sparse.coo_matrix((weights, (rows, cols)),
                                 shape=(npoints, self.nindex))
def parameter_derivative ( self , points ) : npoints = len ( points ) weights = np . ones ( npoints , dtype = np . int ) rows = np . arange ( npoints ) cols = self . discretization . state_to_index ( points ) return sparse . coo_matrix ( ( weights , ( rows , cols ) ) , shape = ( npoints , self . nindex ) )