PLIMean¶

class
PLIMean
(*args)¶ PLI based on a mean perturbation.
 Parameters
 POD
KrigingPOD
,AdaptiveSignalPOD
orPolynomialChaosPOD
The POD object where the run method has been performed.
 delta1d or 2d sequence of float
The new values of the mean or sigma coefficient. Either 1d if delta values are the same for all marginals, or 2d if delta values are defined independently for each marginal.
 sigmaScaledbool
Change the type of the applied mean shiftingfor all the variables. If False (default case), the given delta values are the new marginal means. If True, newMean = mean + sigma x delta, where sigma is the standard deviation of each marginals.
 POD
Methods
drawContourIndices
(self, marginal[, label, name])Draw a contour plot of the indices for a specific marginal
drawIndices
(self, idefect[, …])Draw the indices of all margins for a specific defect
getDefectSizes
(self)Accessor to the defect size where the indices are computed.
getDistribution
(self)Accessor to the parameters distribution.
getGaussKronrod
(self)Accessor to the Gauss Kronrod algorithm used to compute integrals
getIndices
(self[, idelta, marginal, idefect])Accessor to the indices
getPLIObject
(self, idefect)Accessor to the PLI object for a specific defect.
getSamplingSize
(self)Accessor to the Monte Carlo sampling size.
run
(self)Compute the indices
setDefectSizes
(self, size)Accessor to the defect size where the indices are computed.
setDistribution
(self, distribution)Accessor to the parameters distribution.
setGaussKronrod
(self, algo)Accessor to the Gauss Kronrod algorithm used to compute integrals
setSamplingSize
(self, size)Accessor to the Monte Carlo sampling size.

drawContourIndices
(self, marginal, label=None, name=None)¶ Draw a contour plot of the indices for a specific marginal
 Parameters
 marginalint
The indice of the perturbed marginal.
 labellist of string
The labels of each parameters.
 Returns
 figmatplotlib.figure
Matplotlib figure object.
 axmatplotlib.axes
Matplotlib axes object.

drawIndices
(self, idefect, confidenceLevel=0.95, label=None, hellinger=True, name=None)¶ Draw the indices of all margins for a specific defect
 Parameters
 idefectint
The indice of the defect in the given delta list.
 confidenceLevel0 < float < 1 or None
The wanted confidence level to compute the interval. If set to ‘None’ only the indices are plotted.
 labellist of string
The labels of each parameters.
 hellingerbool
If True, the indices are plotted with respect to the hellinger distance between the original PDF and the perturbed PDF. Default is True.
 Returns
 figmatplotlib.figure
Matplotlib figure object.
 axmatplotlib.axes
Matplotlib axes object.

getDefectSizes
(self)¶ Accessor to the defect size where the indices are computed.
 Returns
 defectSizesequence of float
The defect sizes where the Monte Carlo simulation is performed to compute the POD.

getDistribution
(self)¶ Accessor to the parameters distribution.
 Returns
 distribution
openturns.ComposedDistribution
The input parameters distribution used for the Monte Carlo simulation. Default is a Uniform distribution for all parameters.
 distribution

getGaussKronrod
(self)¶ Accessor to the Gauss Kronrod algorithm used to compute integrals

getIndices
(self, idelta=None, marginal=None, idefect=None)¶ Accessor to the indices
 Parameters
 ideltaint
The indice of the delta in the given delta list. Default is None = all.
 marginalint
The indice of the perturbed marginal. Default is None = all.
 idefectint
The indice of the defect in the given delta list. Default is None = all.
 Returns
 indicesfloat, 1d, 2d or 3d array.
The parameter order of the full matrix is delta, marginal and defect. The returned array depends on the given parameter values.

getPLIObject
(self, idefect)¶ Accessor to the PLI object for a specific defect.
 Parameters
 idefectint
The indice of the defect in the given delta list.
 Returns
 pli
PLI
The PLI base object from which more results can be obtained.
 pli

getSamplingSize
(self)¶ Accessor to the Monte Carlo sampling size.
 Returns
 sizeint
The size of the Monte Carlo simulation used to compute the POD for each defect size.

run
(self)¶ Compute the indices
Notes
 Run the analysis:
run a Monte Carlo simulation
compute the indices for each defect size
If, for a defect size, the probability estimate is less than 1e3 or greater than 0.999, then the indices are not computed.

setDefectSizes
(self, size)¶ Accessor to the defect size where the indices are computed.
 Parameters
 defectSizesequence of float
The defect sizes where the Monte Carlo simulation is performed to compute the POD.

setDistribution
(self, distribution)¶ Accessor to the parameters distribution.
 Parameters
 distribution
openturns.ComposedDistribution
The input parameters distribution used for the Monte Carlo simulation.
 distribution

setGaussKronrod
(self, algo)¶ Accessor to the Gauss Kronrod algorithm used to compute integrals
 Parameters
 algo
openturns.GaussKronrod
The algorithm
 algo

setSamplingSize
(self, size)¶ Accessor to the Monte Carlo sampling size.
 Parameters
 sizeint
The size of the Monte Carlo simulation used to compute the POD for each defect size.