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SG++-Doxygen-Documentation
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python.uq.operations.sparse_grid.add | ( | alpha, | |
alphas | |||
) |
python.uq.operations.sparse_grid.addConst | ( | grid, | |
alpha, | |||
c, | |||
y | |||
) |
python.uq.operations.sparse_grid.balance | ( | grid | ) |
python.uq.operations.sparse_grid.checkInterpolation | ( | grid, | |
alpha, | |||
nodalValues, | |||
epsilon = 1e-13 |
|||
) |
python.uq.operations.sparse_grid.checkPositivity | ( | grid, | |
alpha | |||
) |
python.uq.operations.sparse_grid.copyGrid | ( | grid, | |
level = 0 , |
|||
deg = 1 |
|||
) |
python.uq.operations.sparse_grid.createGrid | ( | grid, | |
dim, | |||
deg = 1 , |
|||
addTruncatedBorder = False |
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) |
python.uq.operations.sparse_grid.dehierarchize | ( | grid, | |
alpha | |||
) |
python.uq.operations.sparse_grid.dehierarchizeList | ( | grid, | |
alpha, | |||
gps | |||
) |
evaluate sparse grid function at grid points in gps @param grid: Grid @param alpha: DataVector @param gps: list of HashGridPoint
python.uq.operations.sparse_grid.dehierarchizeOnNewGrid | ( | gridResult, | |
grid, | |||
alpha | |||
) |
python.uq.operations.sparse_grid.estimateConvergence | ( | grid, | |
gp, | |||
v | |||
) |
python.uq.operations.sparse_grid.estimateSurplus | ( | grid, | |
gp, | |||
v | |||
) |
def estimateSurplus(grid, gp, v): gs = grid.getStorage() ix = gs.getSequenceNumber(gp)
if ix < len(v): print( "warning: not estimated", ) return v[ix]
vgp = []
myParents = [(d, pgp) for (d, pgp) in parents(grid, gp) if gs.isContaining(pgp)] vparents = np.ndarray(len(myParents), dtype='float') for i, (dp, p) in enumerate(myParents): ipar = gs.getSequenceNumber(p) vparents[i] = v[ipar]
for dgrp, grp in parents(grid, p):
igrandpar = gs.getSequenceNumber(grp) xpar = p.getStandardCoordinate(dgrp) xgrandpar = grp.getStandardCoordinate(dgrp) xgp = p.getStandardCoordinate(dgrp) + 2 ** -gp.getLevel(dp)
a = (v[ipar] - v[igrandpar]) / (xpar - xgrandpar)
a /= 2.
if (v[ipar] > 0 and v[igrandpar] > 0) or \ (v[ipar] < 0 and v[igrandpar] < 0): sign = 1.
else: sign = -1.
a *= sign
b = v[ipar] - a * xpar
xgp = xpar + (xpar - xgrandpar) / 2.
y = a * xgp + b
vgp.append(y) if len(vgp) == 0: vgp = vparents return np.max(vgp)
Linear extrapolation of the surplus @param grid: Grid @param gp: HashGridPoint @param v: DataVector, surplus vector @return: float, estimated surplus for gp
python.uq.operations.sparse_grid.evalHierToTop | ( | basis, | |
grid, | |||
coeffs, | |||
gp, | |||
d | |||
) |
Referenced by python.uq.operations.sparse_grid.hierarchizeBruteForce().
python.uq.operations.sparse_grid.evalSGFunction | ( | grid, | |
alpha, | |||
p, | |||
isConsistent = True |
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) |
python.uq.operations.sparse_grid.evalSGFunctionBasedOnParents | ( | grid, | |
alpha, | |||
gpi | |||
) |
References f.
python.uq.operations.sparse_grid.evalSGFunctionMulti | ( | grid, | |
alpha, | |||
samples, | |||
isConsistent = True |
|||
) |
python.uq.operations.sparse_grid.getBasis | ( | grid | ) |
python.uq.operations.sparse_grid.getBoundsOfSupport | ( | gs, | |
level, | |||
index, | |||
gridType = GridType_Linear |
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) |
python.uq.operations.sparse_grid.getDegree | ( | grid | ) |
python.uq.operations.sparse_grid.getGridPointsOnBoundary | ( | level, | |
index | |||
) |
python.uq.operations.sparse_grid.getGridPointsOnBoundaryEfficiently | ( | level, | |
index | |||
) |
python.uq.operations.sparse_grid.getHierarchicalAncestors | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.getIndex | ( | gp | ) |
python.uq.operations.sparse_grid.getLevel | ( | gp | ) |
python.uq.operations.sparse_grid.getLevelIndex | ( | gp | ) |
python.uq.operations.sparse_grid.getNonExistingHierarchicalAncestors | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.hasAllChildren | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.hasBorder | ( | gridType | ) |
python.uq.operations.sparse_grid.hasChildren | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.haveHierarchicalRelationshipByLevelIndex | ( | location_i, | |
location_j | |||
) |
python.uq.operations.sparse_grid.haveOverlappingSupport | ( | gpi, | |
gpj | |||
) |
python.uq.operations.sparse_grid.haveOverlappingSupportByLevelIndex | ( | location_i, | |
location_j | |||
) |
python.uq.operations.sparse_grid.haveOverlappingSupportDimx | ( | lid, | |
iid, | |||
ljd, | |||
ijd | |||
) |
python.uq.operations.sparse_grid.hierarchize | ( | grid, | |
nodalValues, | |||
isConsistent = True , |
|||
ignore = None |
|||
) |
python.uq.operations.sparse_grid.hierarchizeBruteForce | ( | grid, | |
nodalValues, | |||
ignore = None |
|||
) |
References python.uq.operations.sparse_grid.evalHierToTop().
python.uq.operations.sparse_grid.hierarchizeEvalHierToTop | ( | grid, | |
nodalValues | |||
) |
python.uq.operations.sparse_grid.insertHierarchicalAncestors | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.insertPoint | ( | grid, | |
gp | |||
) |
insert a grid point to the storage if it is valid. Returns the sequence number of the new grid point in the storage
python.uq.operations.sparse_grid.insertTruncatedBorder | ( | grid, | |
gp | |||
) |
insert points on the border recursively for grids with border @param grid: Grid @param gp: HashGridPoint @return: list of HashGridPoint, contains all the newly added grid points
python.uq.operations.sparse_grid.isHierarchicalAncestor | ( | gpi, | |
gpj | |||
) |
python.uq.operations.sparse_grid.isHierarchicalAncestorByLevelIndex | ( | location_i, | |
location_j | |||
) |
python.uq.operations.sparse_grid.isHierarchicalAncestorDimx | ( | li, | |
ii, | |||
lj, | |||
ij | |||
) |
python.uq.operations.sparse_grid.isRefineable | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.isValid | ( | grid, | |
gp | |||
) |
References python.uq.operations.sparse_grid.isValid1d().
python.uq.operations.sparse_grid.isValid1d | ( | grid, | |
level, | |||
index | |||
) |
Referenced by python.uq.operations.sparse_grid.isValid(), and python.uq.operations.sparse_grid.parent().
python.uq.operations.sparse_grid.loadOperationMultiEval | ( | grid, | |
samples, | |||
isConsistent = True |
|||
) |
python.uq.operations.sparse_grid.parent | ( | grid, | |
gp, | |||
d | |||
) |
References python.uq.operations.sparse_grid.isValid1d().
python.uq.operations.sparse_grid.parents | ( | grid, | |
gp | |||
) |
python.uq.operations.sparse_grid.sub | ( | alpha, | |
alphas | |||
) |
list python.uq.operations.sparse_grid.bsplineBoundaryGridTypes |
list python.uq.operations.sparse_grid.bsplineGridTypes = bsplineNoBoundaryGridTypes + bsplineBoundaryGridTypes |
list python.uq.operations.sparse_grid.bsplineNoBoundaryGridTypes |
list python.uq.operations.sparse_grid.linearBoundaryGridTypes |
list python.uq.operations.sparse_grid.linearGridTypes = linearNoBoundaryGridTypes + linearBoundaryGridTypes |
list python.uq.operations.sparse_grid.linearNoBoundaryGridTypes |
list python.uq.operations.sparse_grid.multipleEvalNaiveGridTypes |
list python.uq.operations.sparse_grid.polyBoundaryGridTypes |
list python.uq.operations.sparse_grid.polyGridTypes = polyNoBoundaryGridTypes + polyBoundaryGridTypes |
list python.uq.operations.sparse_grid.polyNoBoundaryGridTypes |