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python.uq.operations.discretizeProduct Namespace Reference

Functions

def computeErrors
 
def dehierarchizeOnNewGrid
 
def discretizeProduct
 
def interpolateProduct
 
def refine
 

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

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

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

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

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 = []