pystra.reliability.monte_carlo.DistributionAnalysis#

Use it: User guide · Worked example · Theory

class DistributionAnalysis(model, limit_state, *, options=None, rng=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.

Parameters:

  • model (StochasticModel)

  • limit_state (LimitState)

  • options (SimulationOptions | None)

  • rng (int | integer | Sequence[int] | ndarray | SeedSequence | BitGenerator | Generator | None) – Random source; NumPy’s global generator is not used. A seed recreates the same stream on every run, a generator advances its own state, and None draws fresh entropy.

Methods

init_run

Initialize the model's isoprobabilistic transformation.

run

Sample the model and return a DistributionAnalysisResult.
run()[source]#

Sample the model and return a DistributionAnalysisResult.

Return type:

DistributionAnalysisResult

init_run()#

Initialize the model’s isoprobabilistic transformation.

Uses the explicit copula or calibrates the legacy Gaussian Nataf correlation. Must be called at the start of every run() method in subclasses.

Return type:

None