def testSettings(self):
# set distributions of the input parameters
builder = ParameterBuilder()
up = builder.defineUncertainParameters()
up.new().isCalled('x').withUniformDistribution(0, 1)
up.new().isCalled('y').withUniformDistribution(0, 1)
params = builder.andGetResult()
def postprocessor(x, *args, **kws):
return {'x': np.array([x[0], -x[0]]), 'time': list(range(len(x)))}
f = lambda x, **kws: [x[0], 2 * x[0], 4 * x[0], 8 * x[0]]
toi = [0, 1]
# set up uq setting
builder = ASGCUQManagerBuilder().withParameters(params)\
.withTypesOfKnowledge([KnowledgeTypes.SIMPLE,
KnowledgeTypes.SQUARED])\
.withQoI("x")\
.withTimeStepsOfInterest(toi)\
.useInterpolation()
builder.defineUQSetting().withSimulation(f)\
.withPostprocessor(postprocessor)
builder.defineSampler().withGrid().withLevel(3)\
.hasType(GridType_Poly)\
.withDegree(2)\
.withBoundaryLevel(1)
uqManager = builder.andGetResult()
uqManager.runNextSamples()
# define analysis
uqAnalysis = ASGCAnalysisBuilder().withUQManager(uqManager)\
.withAnalyticEstimationStrategy()\
.andGetResult()
# plot result
ans = {}
for t in uqManager.getTimeStepsOfInterest():
grid, alpha = uqManager.getKnowledge().getSparseGridFunction(
t=t, qoi="x")
fig = plt.figure()
ans[t] = plotSG2d(grid,
alpha,
show_grid_points=True,
show_numbers=True)
fig.show()
assert all(ans[0] == -ans[1])
plt.show()
# define UQ setting
builder = ASGCUQManagerBuilder()
builder.withParameters(params)\
.withTypesOfKnowledge([KnowledgeTypes.SIMPLE,
KnowledgeTypes.SQUARED])\
.useInterpolation()
# define model function
def g(x, **kws):
return np.arctan(
50 *
(x[0] - .35)) + np.pi / 2 + 4 * x[1]**3 + 10 * np.exp(x[0] * x[1] - 1)
builder.defineUQSetting().withSimulation(g)
samplerSpec = builder.defineSampler()
samplerSpec.withGrid().withLevel(4)\
.withPolynomialBase(2)\
.withBorder(BorderTypes.TRAPEZOIDBOUNDARY)
samplerSpec.withRefinement()\
.withAdaptThreshold(1e-10)\
.withAdaptPoints(5)\
.withBalancing()\
.refineMostPromisingNodes().withSquaredSurplusRanking()\
.createAllChildrenOnRefinement()\
.refineInnerNodes()
# ref.withBalancing()\
# .addMostPromisingChildren().withLinearSurplusEstimationRanking()