pystra.mc.ImportanceSampling#

class ImportanceSampling(analysis_options=None, limit_state=None, stochastic_model=None)[source]#

Bases: CrudeMonteCarlo

Importance Sampling

To decrease the number of simulations and the coefficient of variation, other methods can be performed. One commonly applied method is the Importance Sampling simulation method (IS).

Attributes:

analysis_option (AnalysisOption): Option for the structural analysis
limit_state (LimitState): Information about the limit state
stochastic_model (StochasticModel): Information about the model

Methods

`computeBeta`	Compute beta value
`computeBins`	Return an optimal amount of bins for a histogram
`computeCoefficientOfVariation`	Compute Coefficient of Variation
`computeFailureProbability`	Compute probability of failure
`computeLimitState`	Evaluate limit-state function
`computePercentDone`	Compute percent done
`computeRandomNumbers`	Compute random numbers
`computeResults`	Collect result of sampling
`computeSumUpdate`	Update summation
`computeTransformation`	Compute transformation from u to x space
`getBeta`	Returns the beta value
`getBins`	Returns the amount on bins
`getDistributionData`	Returns data for the failure
`getFailure`	Returns the probability of failure
`init_run`	Derived classes call this at top of their run()
`initializeVariables`	Initialization of the simulation variables
`run`
`setPoint`	Set design point
`showResults`	Show results and plots

showResults()[source]#: Show results and plots

computeBeta()#: Compute beta value

computeBins(samples)#

Return an optimal amount of bins for a histogram

Returns:

bins (int): Returns amount on bins

computeCoefficientOfVariation()#: Compute Coefficient of Variation

computeFailureProbability()#: Compute probability of failure

computeLimitState()#: Evaluate limit-state function

computePercentDone()#: Compute percent done

computeRandomNumbers()#: Compute random numbers

computeResults()#: Collect result of sampling

computeSumUpdate()#: Update summation

computeTransformation()#: Compute transformation from u to x space

Note

TODO: this method takes a lot of time, find some better solution.

getBeta()#

Returns the beta value

Returns:

beta (float): Returns the beta value

getBins()#

Returns the amount on bins

Returns:

bins (int): Returns the amount on bins (histogram)

getDistributionData()#

Returns data for the failure

Returns:

all_G (float): Returns data from the distribution

getFailure()#

Returns the probability of failure

Returns:

Pf (float): Returns the probability of failure

init_run()#: Derived classes call this at top of their run()

initializeVariables()#: Initialization of the simulation variables

setPoint(point=None)#: Set design point