TrendEvaluationImplementation

class TrendEvaluationImplementation(*args)

Proxy of C++ OT::TrendEvaluationImplementation

Methods

__call__(*args)
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.
getFunction()
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.NumericalMathFunction('x', 'sin(2*_pi*x)*exp(-x^2/2)', 'y')
>>> 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 : NumericalSample

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 : NumericalSample

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.NumericalMathFunction(['x1', 'x2'], ['y'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y]
getHistoryInput()

Accessor to the history of the input values.

Returns:

input_history : NumericalSample

All the input numerical points stored in the history mecanism.

getHistoryOutput()

Accessor to the history of the output values.

Returns:

output_history : NumericalSample

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.NumericalMathFunction(['x1', 'x2'], ['y'],
...                          ['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.NumericalMathFunction(['x1', 'x2'], ['y'],
...                          ['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 : NumericalSample

All the input parameters stored in the history mecanism.

getInputPointHistory()

Accessor to the history of the input points values.

Returns:

history : NumericalSample

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 : NumericalMathFunction

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.NumericalMathFunction(['x1', 'x2'], ['y'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDescription())
[y]
getOutputDimension()

Accessor to the number of the outputs.

Returns:

number_outputs : int

Number of outputs.

Examples

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

Accessor to the parameter values.

Returns:

parameter : NumericalPoint

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.NumericalMathFunction(['x1', 'x2'], ['y'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y]
>>> 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.