InverseNatafIndependentCopulaEvaluation¶

class InverseNatafIndependentCopulaEvaluation(*args)¶

Proxy of C++ OT::InverseNatafIndependentCopulaEvaluation.

Methods

`draw`(*args)	Draw the output of function as a `Graph`.
`getCallsNumber`()	Accessor to the number of times the function has been called.
`getClassName`()	Accessor to the object’s name.
`getDescription`()	Accessor to the description of the inputs and outputs.
`getId`()	Accessor to the object’s id.
`getInputDescription`()	Accessor to the description of the inputs.
`getInputDimension`()	Accessor to the number of the inputs.
`getMarginal`(*args)	Accessor to marginal.
`getName`()	Accessor to the object’s name.
`getOutputDescription`()	Accessor to the description of the outputs.
`getOutputDimension`()	Accessor to the number of the outputs.
`getParameter`()	Accessor to the parameter values.
`getParameterDescription`()	Accessor to the parameter description.
`getParameterDimension`()	Accessor to the dimension of the parameter.
`getShadowedId`()	Accessor to the object’s shadowed id.
`getVisibility`()	Accessor to the object’s visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isActualImplementation`()	Accessor to the validity flag.
`parameterGradient`(inP)	Gradient against the parameters.
`setDescription`(description)	Accessor to the description of the inputs and outputs.
`setInputDescription`(inputDescription)	Accessor to the description of the inputs.
`setName`(name)	Accessor to the object’s name.
`setOutputDescription`(outputDescription)	Accessor to the description of the outputs.
`setParameter`(parameters)	Accessor to the parameter values.
`setParameterDescription`(description)	Accessor to the parameter description.
`setShadowedId`(id)	Accessor to the object’s shadowed id.
`setVisibility`(visible)	Accessor to the object’s visibility state.

__call__

__init__(*args)¶: Initialize self. See help(type(self)) for accurate signature.

draw(*args)¶

Draw the output of function as a Graph.

Available usages:

draw(inputMarg, outputMarg, CP, xiMin, xiMax, ptNb)

draw(firstInputMarg, secondInputMarg, outputMarg, CP, xiMin_xjMin, xiMax_xjMax, ptNbs)

draw(xiMin, xiMax, ptNb)

draw(xiMin_xjMin, xiMax_xjMax, ptNbs)

Parameters:

outputMarg, inputMarg : int, $outputMarg, inputMarg \geq 0$: outputMarg is the index of the marginal to draw as a function of the marginal with index inputMarg.
firstInputMarg, secondInputMarg : int, $firstInputMarg, secondInputMarg \geq 0$: In the 2D case, the marginal outputMarg is drawn as a function of the two marginals with indexes firstInputMarg and secondInputMarg.
CP : sequence of float: Central point.
xiMin, xiMax : float: Define the interval where the curve is plotted.
xiMin_xjMin, xiMax_xjMax : sequence of float of dimension 2.: In the 2D case, define the intervals where the curves are plotted.
ptNb : int $ptNb > 0$ or list of ints of dimension 2 $ptNb_k > 0, k=1,2$: The number of points to draw the curves.

Notes

We note $f: \Rset^n \rightarrow \Rset^p$ where $\vect{x} = (x_1, \dots, x_n)$ and $f(\vect{x}) = (f_1(\vect{x}), \dots,f_p(\vect{x}))$ , with $n\geq 1$ and $p\geq 1$ .

In the first usage:

Draws graph of the given 1D outputMarg marginal $f_k: \Rset^n \rightarrow \Rset$ as a function of the given 1D inputMarg marginal with respect to the variation of $x_i$ in the interval $[x_i^{min}, x_i^{max}]$ , when all the other components of $\vect{x}$ are fixed to the corresponding ones of the central point CP. Then it draws the graph: $t\in [x_i^{min}, x_i^{max}] \mapsto f_k(CP_1, \dots, CP_{i-1}, t, CP_{i+1} \dots, CP_n)$ .

In the second usage:

Draws the iso-curves of the given outputMarg marginal $f_k$ as a function of the given 2D firstInputMarg and secondInputMarg marginals with respect to the variation of $(x_i, x_j)$ in the interval $[x_i^{min}, x_i^{max}] \times [x_j^{min}, x_j^{max}]$ , when all the other components of $\vect{x}$ are fixed to the corresponding ones of the central point CP. Then it draws the graph: $(t,u) \in [x_i^{min}, x_i^{max}] \times [x_j^{min}, x_j^{max}] \mapsto f_k(CP_1, \dots, CP_{i-1}, t, CP_{i+1}, \dots, CP_{j-1}, u, CP_{j+1} \dots, CP_n)$ .

In the third usage:

The same as the first usage but only for function $f: \Rset \rightarrow \Rset$ .

In the fourth usage:

The same as the second usage but only for function $f: \Rset^2 \rightarrow \Rset$ .

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> f = ot.SymbolicFunction(['x'], ['sin(2*pi_*x)*exp(-x^2/2)'])
>>> graph = f.draw(-1.2, 1.2, 100)
>>> View(graph).show()

getCallsNumber()¶

Accessor to the number of times the function has been called.

Returns:	calls_number : int Integer that counts the number of times the function has been called since its creation.

getClassName()¶

Accessor to the object’s name.

Returns:	class_name : str The object class name (object.__class__.__name__).

getDescription()¶

Accessor to the description of the inputs and outputs.

Returns:	description : `Description` Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]

getId()¶

Accessor to the object’s id.

Returns:	id : int Internal unique identifier.

getInputDescription()¶

Accessor to the description of the inputs.

Returns:	description : `Description` Description of the inputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDescription())
[x1,x2]

getInputDimension()¶

Accessor to the number of the inputs.

Returns:	number_inputs : int Number of inputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDimension())
2

getMarginal(*args)¶

Accessor to marginal.

Parameters:	indices : int or list of ints Set of indices for which the marginal is extracted.
Returns:	marginal : `Function` Function corresponding to either $f_i$ or $(f_i)_{i \in indices}$ , with $f:\Rset^n \rightarrow \Rset^p$ and $f=(f_0 , \dots, f_{p-1})$ .

getName()¶

Accessor to the object’s name.

Returns:	name : str The name of the object.

getOutputDescription()¶

Accessor to the description of the outputs.

Returns:	description : `Description` Description of the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDescription())
[y0]

getOutputDimension()¶

Accessor to the number of the outputs.

Returns:	number_outputs : int Number of outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDimension())
1

getParameter()¶

Accessor to the parameter values.

Returns:	parameter : `Point` The parameter values.

getParameterDescription()¶

Accessor to the parameter description.

Returns:	parameter : `Description` The parameter description.

getParameterDimension()¶

Accessor to the dimension of the parameter.

Returns:	parameter_dimension : int Dimension of the parameter.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:	id : int Internal unique identifier.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:	visible : bool Visibility flag.

hasName()¶

Test if the object is named.

Returns:	hasName : bool True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:	hasVisibleName : bool True if the name is not empty and not the default one.

isActualImplementation()¶

Accessor to the validity flag.

Returns:	is_impl : bool Whether the implementation is valid.

parameterGradient(inP)¶

Gradient against the parameters.

Parameters:	x : sequence of float Input point
Returns:	parameter_gradient : `Matrix` The parameters gradient computed at x.

setDescription(description)¶

Accessor to the description of the inputs and outputs.

Parameters:	description : sequence of str Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]
>>> f.setDescription(['a','b','y'])
>>> print(f.getDescription())
[a,b,y]

setInputDescription(inputDescription)¶

Accessor to the description of the inputs.

Returns:	description : `Description` Description of the inputs.

setName(name)¶

Accessor to the object’s name.

Parameters:	name : str The name of the object.

setOutputDescription(outputDescription)¶

Accessor to the description of the outputs.

Returns:	description : `Description` Description of the outputs.

setParameter(parameters)¶

Accessor to the parameter values.

Parameters:	parameter : sequence of float The parameter values.

setParameterDescription(description)¶

Accessor to the parameter description.

Parameters:	parameter : `Description` The parameter description.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:	id : int Internal unique identifier.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:	visible : bool Visibility flag.

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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