pystra.mc.DistributionAnalysis#

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

Bases: MonteCarlo

Distribution Analysis

To analyze the random variables, used in the limit state function, a numerical distribution analysis based on Monte Carlo simulation can be performed.

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
`computeDataUpdate`	Update data
`computeDistributionData`	Compute data for the distributions
`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

initializeVariables()[source]#: Initialization of the simulation variables

computeDataUpdate()[source]#: Update data

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

computeDistributionData()[source]#: Compute data for the distributions

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()

setPoint(point=None)#: Set design point

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