Functions
def	computeErrors (jgrid, jalpha, grid1, alpha1, grid2, alpha2, n=200)

def	dehierarchizeOnNewGrid (gridResult, grid, alpha)

def	discretizeProduct (grid1, alpha1, grid2, alpha2)

def	interpolateProduct (grid1, alpha1, grid2, alpha2, grid_result)

def	refine (jgrid, jalpha)

Variables
list	refinable = []

Function Documentation

◆ computeErrors()

def python.uq.operations.discretizeProduct.computeErrors	(	jgrid,
		jalpha,
		grid1,
		alpha1,
		grid2,
		alpha2,
		n = `200`
	)

Compute some errors to estimate the quality of the
interpolation.
@param jgrid: Grid, new discretization
@param jalpha: DataVector, new surpluses
@param grid1: Grid, old discretization
@param alpha1: DataVector, old surpluses
@param grid2: Grid, old discretization
@param alpha2: DataVector, old surpluses
@return: tuple(<float>, <float>), maxdrift, l2norm

References python.uq.operations.sparse_grid.evalSGFunctionMulti().

◆ dehierarchizeOnNewGrid()

def python.uq.operations.discretizeProduct.dehierarchizeOnNewGrid	(	gridResult,
		grid,
		alpha
	)

References python.uq.operations.sparse_grid.evalSGFunctionMulti().

Referenced by python.uq.operations.discretizeProduct.interpolateProduct().

◆ discretizeProduct()

def python.uq.operations.discretizeProduct.discretizeProduct	(	grid1,
		alpha1,
		grid2,
		alpha2
	)

Discretizes the product of two sparse grid functions:

    h(x) := f(x) * g(x)

on a full grid with piecewise polynomial basis. Therefore
a maximum number of grid points 10^6 is allowed.

@param grid1: Grid, grid of f
@param alpha1: DataVector, hierarchical coefficients of f
@param grid2: Grid, grid of g
@param alpha2: DataVector, hierarchical coefficients of g

References python.uq.operations.sparse_grid.getDegree(), and python.uq.operations.discretizeProduct.interpolateProduct().