FieldModelEvaluation

class persalys.FieldModelEvaluation(*args)

Generate the evaluation of a model with mesh.

Parameters:

namestr

Name

modelPhysicalModel

Model containing a mesh

valuessequence of float

Input values (optional)

Methods

getBlockSize()	Block size accessor.
getClassName()	Accessor to the object's name.
getErrorDescription()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getFailedInputSample()	Failed input sample accessor.
getInterestVariables()	Get the variables to analyse.
getName()	Accessor to the object's name.
getNotEvaluatedInputSample()	Not evaluated input sample accessor.
getOriginalInputSample()	Input sample accessor.
getPhysicalModel()	Physical model accessor.
getPythonScript()	Physical model accessor.
getSeed()	Seed accessor.
getValues()	Values accessor.
getWarningMessage()	Warning message accessor.
hasName()	Test if the object is named.
hasValidResult()	Whether the analysis has been run.
isReliabilityAnalysis()	Whether the analysis involves reliability.
isRunning()	Whether the analysis is running.
run()	Launch the analysis.
setBlockSize(size)	Block size accessor.
setEvaluations(outputSample)	Add evaluations for the design of experiments
setInterestVariables(variablesNames)	Set the variables to analyse.
setName(name)	Accessor to the object's name.
setSeed(seed)	Seed accessor.
setValues(values)	Values accessor.

GetDefaultBounds
getProcessSample
getResult

Examples

>>> import openturns as ot
>>> import persalys

Create the model:

>>> meshModel = persalys.GridMeshModel(ot.Interval(0., 12.), [5])

>>> z0 = persalys.Input('z0', 100, '')
>>> v0 = persalys.Input('v0', 55, '')
>>> m = persalys.Input('m', 80, '')
>>> c = persalys.Input('c', 15, '')
>>> z = persalys.Output('z', '')
>>> formula = ['max(0, z0 + (m * 9.81 / c) * t + (m / c) * (v0 - (m * 9.81 / c)) * (1 - exp(-t * c / m)))']

>>> model = persalys.SymbolicFieldModel('aModel', meshModel, [z0, v0, m, c], [z], formula)

Create the analysis:

>>> analysis = persalys.FieldModelEvaluation('analysis', model)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()

__init__(*args)

getBlockSize()

Block size accessor.

Returns:

blockSizepositive int

Number of terms analysed together. It is set by default to 1.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getErrorDescription()

Design of experiments accessor.

Returns:

errorDescDescription

Description containing messages from failed points.

getErrorMessage()

Error message accessor.

Returns:

messagestr

Error message if the analysis failed

getFailedInputSample()

Failed input sample accessor.

Returns:

sampleopenturns.Sample

Sample with the failed input values

getInterestVariables()

Get the variables to analyse.

Returns:

variablesNamessequence of str

Names of the variables to analyse

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getNotEvaluatedInputSample()

Not evaluated input sample accessor.

Returns:

sampleopenturns.Sample

Points of the design of experiments which were not evaluated

getOriginalInputSample()

Input sample accessor.

Returns:

sampleopenturns.Sample

Input sample.

getPhysicalModel()

Physical model accessor.

Returns:

modelPhysicalModel

Physical model

getPythonScript()

Physical model accessor.

Returns:

scriptstr

Python script to replay the analysis

getSeed()

Seed accessor.

Returns:

seedint

Seed value

getValues()

Values accessor.

Returns:

valuesopenturns.Point

Inputs values used in the case where there is at least a constant variable.

getWarningMessage()

Warning message accessor.

Returns:

messagestr

Warning message which can appear during the analysis computation

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

hasValidResult()

Whether the analysis has been run.

Returns:

hasValidResultbool

Whether the analysis has already been run

isReliabilityAnalysis()

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysisbool

Whether the analysis involves a reliability analysis

isRunning()

Whether the analysis is running.

Returns:

isRunningbool

Whether the analysis is running

run()

Launch the analysis.

setBlockSize(size)

Block size accessor.

Parameters:

blockSizepositive int

Number of terms analysed together. It is set by default to 1.

setEvaluations(outputSample)

Add evaluations for the design of experiments

Parameters:

outputSample:py:openturns.Sample

sample containing values for the output variables

setInterestVariables(variablesNames)

Set the variables to analyse.

Parameters:

variablesNamessequence of str

Names of the variables to analyse

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setSeed(seed)

Seed accessor.

Parameters:

seedint

Seed value

setValues(values)

Values accessor.

Parameters:

valuesopenturns.Point

Inputs values used in the case where there is at least a constant variable.