SG++-Doxygen-Documentation
python.uq.operations.discretizeProduct Namespace Reference

## 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

 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

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

 def python.uq.operations.discretizeProduct.interpolateProduct ( grid1, alpha1, grid2, alpha2, grid_result )
 def python.uq.operations.discretizeProduct.refine ( jgrid, jalpha )

## Variable Documentation

 list python.uq.operations.discretizeProduct.refinable = []