SymbolicFieldModel¶

class persalys.SymbolicFieldModel(*args)¶

Create a physical model defined by analytical formulas and a mesh.

Parameters:

namestr: Name
meshMeshModel: Mesh
inputssequence of Input: Input variables (optional)
outputssequence of Output: Output variables (optional)
formulassequence of str: List of analytical formulas between the inputs and the outputs (optional). The function is defined by outputs = formulas(inputs).

Methods

`addInput`(input)	Add an input variable.
`addOutput`(output)	Add an output variable.
`clearInputs`()	Remove all the input variables.
`clearOutputs`()	Remove all the output variables.
`exportStandalonePythonScript`(fileName)	Export the model into a Python script.
`getClassName`()	Accessor to the object's name.
`getCopula`()	Copula accessor.
`getDistribution`()	Distribution accessor.
`getEvalTime`()	Evaluation time accessor.
`getFormula`(outputName)	Output formula accessor.
`getFormulas`()	Formulas accessor.
`getFunction`(*args)	Accessor to the underlying function.
`getInputByName`(*args)	Input variable accessor.
`getInputDimension`()	Number of input variables accessor.
`getInputNames`()	Input variable names accessor.
`getInputRandomVector`()	Input random vector accessor.
`getInputs`()	Input variables accessor.
`getMeshModel`()	Mesh model accessor.
`getName`()	Accessor to the object's name.
`getOutputByName`(*args)	Output variable accessor.
`getOutputDimension`()	Number of output variables accessor.
`getOutputNames`()	Output variable names accessor.
`getOutputRandomVector`(outputNames)	Output random vector accessor.
`getOutputs`()	Output variables accessor.
`getPointToFieldFunction`(*args)	Accessor to the underlying function.
`getProcessNumber`()	Number of concurrent processes.
`getPythonScript`()	Python script accessor.
`getRestrictedFunction`(*args)	Accessor to the function restricted to its stochastic inputs.
`getRestrictedPointToFieldFunction`(outputNames)	Accessor to the field function restricted to its stochastic inputs.
`getSelectedOutputsNames`()	Accessor to the selected outputs names.
`getStochasticInputNames`()	Accessor to the names of the stochastic input variables.
`hasInputNamed`(inputName)	Check if an input has the given name.
`hasMesh`()	Whether the model has a mesh.
`hasName`()	Test if the object is named.
`hasOutputNamed`(outputName)	Check if an output has the given name.
`hasStochasticInputs`()	Whether it contains stochastic variables.
`isParallel`()	Whether the evaluations of the model are parallelized.
`isValid`()	Whether it is valid.
`removeInput`(inputName)	Remove an input variable.
`removeOutput`(outputName)	Remove an output variable.
`selectOutput`(outputName, selected)	Select output.
`setCopula`(inputNames, copula)	Copula accessor.
`setDistribution`(inputName, distribution)	Input distribution accessor.
`setDistributionParametersType`(inputName, ...)	Input distribution parameter type accessor.
`setEvalTime`(evalTime)	Evaluation time accessor.
`setFiniteDifferenceStep`(inputName, step)	Finite difference step accessor.
`setFormula`(outputName, formula)	Output formula accessor.
`setFormulas`(formulas)	Formulas accessor.
`setInputDescription`(inputName, description)	Input description accessor.
`setInputName`(inputName, newName)	Input variable name accessor.
`setInputStochastic`(inputName, stoch)	Whether the input variable is stochastic.
`setInputValue`(inputName, value)	Input variable value accessor.
`setInputs`(inputs)	Input variables accessor.
`setMeshModel`(meshModel)	Mesh model accessor.
`setName`(name)	Accessor to the object's name.
`setOutputDescription`(outputName, description)	Output description accessor.
`setOutputName`(outputName, newName)	Output variable name accessor.
`setOutputValue`(outputName, value)	Output variable name accessor.
`setOutputs`(outputs)	Output variables accessor.
`setParallel`(flag)	Whether the evaluations of the model are parallelized.
`setProcessNumber`(processNumber)	Number of concurrent processes.

Examples

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

Create the mesh model:

>>> meshModel = persalys.GridMeshModel([persalys.Variable('x', 'aDescription')], ot.Interval(0., 12.), [10])

Create the model:

>>> 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) * x + (m / c) * (v0 - (m * 9.81 / c)) * (1 - exp(-x * c / m)))']
>>> model = persalys.SymbolicFieldModel('model', meshModel, [z0, v0, m, c], [z], formula)

>>> y = model.getPointToFieldFunction()([100, 55, 80, 15])

__init__(*args)¶

addInput(input)¶

Add an input variable.

Parameters:

inputInput: New variable

addOutput(output)¶

Add an output variable.

Parameters:

outputOutput: New variable

clearInputs()¶: Remove all the input variables.

clearOutputs()¶: Remove all the output variables.

exportStandalonePythonScript(fileName)¶

Export the model into a Python script.

In the case of metamodels the script only depends on OpenTURNS and is meant to be used out of Persalys.

Parameters:

fileNamestr: Path of the exported script

getClassName()¶

Accessor to the object’s name.

Returns:

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

getCopula()¶

Copula accessor.

Parameters:

copulaopenturns.Distribution: The copula

getDistribution()¶

Distribution accessor.

Parameters:

Distributionopenturns.JointDistribution: The composed distribution (marginals and dependence)

getEvalTime()¶

Evaluation time accessor.

Returns:

evalTimefloat: Evaluation time

getFormula(outputName)¶

Output formula accessor.

Parameters:

namestr: Variable name

Returns:

formulastr: Formula text

getFormulas()¶

Formulas accessor.

Returns:

formulasopenturns.Description: Output formulas

getFunction(*args)¶

Accessor to the underlying function.

Parameters:

outputNamessequence of str: Names of the outputs to be evaluated (optional)

Returns:

functionopenturns.Function: Function evaluating the outputs

getInputByName(*args)¶

Input variable accessor.

Parameters:

namestr: Variable name

Returns:

inputInput: Input variable

getInputDimension()¶

Number of input variables accessor.

Returns:

dimensionint: Number of input variables

getInputNames()¶

Input variable names accessor.

Returns:

namesopenturns.Description: Input variable names

getInputRandomVector()¶

Input random vector accessor.

Returns:

namesopenturns.RandomVector: Input random vector

getInputs()¶

Input variables accessor.

Returns:

inputssequence of Input: Input variables

getMeshModel()¶

Mesh model accessor.

Returns:

meshMeshModel: Mesh model

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getOutputByName(*args)¶

Output variable accessor.

Parameters:

namestr: Variable name

Returns:

inputOutput: Output variable

getOutputDimension()¶

Number of output variables accessor.

Returns:

dimensionint: Number of output variables

getOutputNames()¶

Output variable names accessor.

Returns:

namesopenturns.Description: Output variable names

getOutputRandomVector(outputNames)¶

Output random vector accessor.

Returns:

namesopenturns.RandomVector: Output random vector

getOutputs()¶

Output variables accessor.

Returns:

outputssequence of Output: Output variables

getPointToFieldFunction(*args)¶

Accessor to the underlying function.

Parameters:

outputNamessequence of str: Names of the outputs to be evaluated (optional)

Returns:

functionopenturns.PointToFieldFunction: Function evaluating the outputs along the mesh nodes

getProcessNumber()¶

Number of concurrent processes.

Returns:

process_numberint: Maximum number of processes

getPythonScript()¶

Python script accessor.

Returns:

scriptstr: Python script to replay the model

getRestrictedFunction(*args)¶

Accessor to the function restricted to its stochastic inputs.

Parameters:

outputNamessequence of str: Output variables (optional)

Returns:

restrictedopenturns.Function: Stochastic function

getRestrictedPointToFieldFunction(outputNames)¶

Accessor to the field function restricted to its stochastic inputs.

Parameters:

outputNamessequence of str: Output variables (optional)

Returns:

restrictedopenturns.PointToFieldFunction: Stochastic field function (for model with mesh)

getSelectedOutputsNames()¶

Accessor to the selected outputs names.

Returns:

namesopenturns.Description: Selected outputs names

getStochasticInputNames()¶

Accessor to the names of the stochastic input variables.

Returns:

namesopenturns.Description: Names of the stochastic input variables

hasInputNamed(inputName)¶

Check if an input has the given name.

Parameters:

namestr: Variable name

Returns:

hasbool: Whether an input has the given name

hasMesh()¶

Whether the model has a mesh.

Returns:

hasMeshbool: Whether the model has a mesh

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasOutputNamed(outputName)¶

Check if an output has the given name.

Parameters:

namestr: Variable name

Returns:

hasbool: Whether an output has the given name

hasStochasticInputs()¶

Whether it contains stochastic variables.

Returns:

hasbool: Whether it contains stochastic variables

isParallel()¶

Whether the evaluations of the model are parallelized.

Returns:

isParallelbool: Whether the evaluations of the model are parallelized (available only for the Python model)

isValid()¶

Whether it is valid.

Returns:

isValidbool: Whether it is valid

removeInput(inputName)¶

Remove an input variable.

Parameters:

namestr: Variable name

removeOutput(outputName)¶

Remove an output variable.

Parameters:

namestr: Variable name

selectOutput(outputName, selected)¶

Select output.

Parameters:

namestr: Output name
isSelectedbool: Whether it is selected

setCopula(inputNames, copula)¶

Copula accessor.

Parameters:

copulaopenturns.Distribution: The copula

setDistribution(inputName, distribution)¶

Input distribution accessor.

Parameters:

namestr: Variable name
distributionopenturns.Distribution: Variable distribution

setDistributionParametersType(inputName, distributionParametersType)¶

Input distribution parameter type accessor.

Parameters:

namestr: Variable name
parametersTypeint: Distribution parameter type accessor

setEvalTime(evalTime)¶

Evaluation time accessor.

Parameters:

evalTimefloat: Evaluation time

setFiniteDifferenceStep(inputName, step)¶

Finite difference step accessor.

Parameters:

namestr: Variable name
stepfloat: Finite difference step used to define the gradient of the model’s function

setFormula(outputName, formula)¶

Output formula accessor.

Parameters:

namestr: Variable name
formulastr: Formula text

setFormulas(formulas)¶

Formulas accessor.

Parameters:

formulassequence of str: Output formulas

setInputDescription(inputName, description)¶

Input description accessor.

Parameters:

namestr: Variable name
descriptionstr: Description text

setInputName(inputName, newName)¶

Input variable name accessor.

Parameters:

namestr: Variable name
newNamestr: New name

setInputStochastic(inputName, stoch)¶

Whether the input variable is stochastic.

Parameters:

namestr: Variable name
isStochasticbool: Whether the input variable is stochastic

setInputValue(inputName, value)¶

Input variable value accessor.

Parameters:

namestr: Variable name
valuefloat: New value

setInputs(inputs)¶

Input variables accessor.

Parameters:

inputssequence of Input: Input variables

setMeshModel(meshModel)¶

Mesh model accessor.

Parameters:

meshMeshModel: Mesh model

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setOutputDescription(outputName, description)¶

Output description accessor.

Parameters:

namestr: Variable name
descriptionstr: Description text

setOutputName(outputName, newName)¶

Output variable name accessor.

Parameters:

namestr: Variable name
newNamestr: New name

setOutputValue(outputName, value)¶

Output variable name accessor.

Parameters:

namestr: Variable name
valuefloat: New value

setOutputs(outputs)¶

Output variables accessor.

Parameters:

outputssequence of Output: Output variables

setParallel(flag)¶

Whether the evaluations of the model are parallelized.

Parameters:

isParallelbool: Whether the evaluations of the model are parallelized

Notes

This is only implemented for the Python model. Also, on Windows platforms, ProcessPoolExecutor startup time being much higher, you may want to evaluate enough points in batch rather than many small blocks. Refer to the setBlockSize method of the various algorithms.

setProcessNumber(processNumber)¶

Number of concurrent processes.

Parameters:

process_numberint: Maximum number of processes

SymbolicFieldModel¶

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