InverseNatafIndependentCopulaEvaluation¶

class InverseNatafIndependentCopulaEvaluation(*args)¶

Proxy of C++ OT::InverseNatafIndependentCopulaEvaluation

Methods

`__call__`(inP)
`addCacheContent`(inSample, outSample)	Add input numerical points and associated output to the cache.
`clearCache`()	Empty the content of the cache.
`clearHistory`()	Empty the content of the history.
`disableCache`()	Disable the cache mechanism.
`disableHistory`()	Disable the history mechanism.
`draw`(*args)	Draw the output of function as a `Graph`.
`enableCache`()	Enable the cache mechanism.
`enableHistory`()	Enable the history mechanism.
`getCacheHits`()	Accessor to the number of computations saved thanks to the cache mecanism.
`getCacheInput`()	Accessor to all the input numerical points stored in the cache mecanism.
`getCacheOutput`()	Accessor to all the output numerical points stored in the cache mecanism.
`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.
`getHistoryInput`()	Accessor to the history of the input values.
`getHistoryOutput`()	Accessor to the history of the output values.
`getId`()	Accessor to the object’s id.
`getInputDescription`()	Accessor to the description of the inputs.
`getInputDimension`()	Accessor to the number of the inputs.
`getInputParameterHistory`()	Accessor to the history of the input parameter values.
`getInputPointHistory`()	Accessor to the history of the input points values.
`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.
`isCacheEnabled`()	Test whether the cache mechanism is enabled or not.
`isHistoryEnabled`()	Test whether the history mechanism is enabled or not.
`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.

__init__(*args)¶

addCacheContent(inSample, outSample)¶

Add input numerical points and associated output to the cache.

Parameters:

input_sample : 2-d sequence of float

Input numerical points to be added to the cache.

output_sample : 2-d sequence of float

Output numerical points associated with the input_sample to be added to the cache.

clearCache()¶: Empty the content of the cache.

clearHistory()¶: Empty the content of the history.

disableCache()¶: Disable the cache mechanism.

disableHistory()¶: Disable the history mechanism.

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 OpenTURNS 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 OpenTURNS 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()

enableCache()¶: Enable the cache mechanism.

enableHistory()¶: Enable the history mechanism.

getCacheHits()¶

Accessor to the number of computations saved thanks to the cache mecanism.

Returns:

cacheHits : int

Integer that counts the number of computations saved thanks to the cache mecanism.

getCacheInput()¶

Accessor to all the input numerical points stored in the cache mecanism.

Returns:

cacheInput : Sample

All the input numerical points stored in the cache mecanism.

getCacheOutput()¶

Accessor to all the output numerical points stored in the cache mecanism.

Returns:

cacheInput : Sample

All the output numerical points stored in the cache mecanism.

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]

getHistoryInput()¶

Accessor to the history of the input values.

Returns:

input_history : Sample

All the input numerical points stored in the history mecanism.

getHistoryOutput()¶

Accessor to the history of the output values.

Returns:

output_history : Sample

All the output numerical points stored in the history mecanism.

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

getInputParameterHistory()¶

Accessor to the history of the input parameter values.

Returns:

history : Sample

All the input parameters stored in the history mecanism.

getInputPointHistory()¶

Accessor to the history of the input points values.

Returns:

history : Sample

All the input points stored in the history mecanism.

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.

isCacheEnabled()¶

Test whether the cache mechanism is enabled or not.

Returns:

isCacheEnabled : bool

Flag telling whether the cache mechanism is enabled. It is disabled by default.

isHistoryEnabled()¶

Test whether the history mechanism is enabled or not.

Returns:

isHistoryEnabled : bool

Flag telling whether the history mechanism is enabled. It is disabled by default.

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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