Description¶

class Description(*args)¶

Collection of strings.

Available constructors:

Description(size=0, value=’ ‘)

Description(sequence)

Parameters:

sizeint, $size \geq 0$: Size of the collection.
valuestr: Value set to the size elements.
sequencesequence of str: Components of the vector.

Examples

>>> import openturns as ot

Use the first constructor:

>>> ot.Description()
[]
>>> ot.Description(2)
[,]
>>> ot.Description(2, 'C')
[C,C]

Use the second constructor:

>>> vector = ot.Description(['P1', 'P2', 'P3'])
>>> vector
[P1,P2,P3]

Use some functionalities:

>>> vector[1] = 'P4'
>>> vector
[P1,P4,P3]
>>> vector.add('P5')
>>> vector
[P1,P4,P3,P5]

Methods

`BuildDefault`(*args)	Build a default description.
`add`(*args)	Append a component (in-place).
`at`(*args)	Access to an element of the collection.
`clear`()	Reset the collection to zero dimension.
`find`(val)	Find the index of a given value.
`getClassName`()	Accessor to the object's name.
`getId`()	Accessor to the object's id.
`getName`()	Accessor to the object's name.
`getShadowedId`()	Accessor to the object's shadowed id.
`getSize`()	Get the collection's dimension (or size).
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isBlank`()	Check if the description is blank.
`isEmpty`()	Tell if the collection is empty.
`resize`(newSize)	Change the size of the collection.
`select`(marginalIndices)	Selection from indices.
`setName`(name)	Accessor to the object's name.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setVisibility`(visible)	Accessor to the object's visibility state.

sort

__init__(*args)¶

static BuildDefault(*args)¶

Build a default description.

Parameters:

sizeint, $size \geq 0$: Size of the collection.
valuestr: Prefixed label of the final labels. By default, it is equal to Component.

Returns:

descriptionDescription: Description of dimension size built as: value0, …, valueN with N=size-1.

Examples

>>> import openturns as ot
>>> ot.Description.BuildDefault(3)
[Component0,Component1,Component2]
>>> ot.Description.BuildDefault(3, 'C')
[C0,C1,C2]

add(*args)¶

Append a component (in-place).

Parameters:

valuetype depends on the type of the collection.: The component to append.

Examples

>>> import openturns as ot
>>> x = ot.Point(2)
>>> x.add(1.)
>>> print(x)
[0,0,1]

at(*args)¶

Access to an element of the collection.

Parameters:

indexpositive int: Position of the element to access.

Returns:

elementtype depends on the type of the collection: Element of the collection at the position index.

clear()¶

Reset the collection to zero dimension.

Examples

>>> import openturns as ot
>>> x = ot.Point(2)
>>> x.clear()
>>> x
class=Point name=Unnamed dimension=0 values=[]

find(val)¶

Find the index of a given value.

Parameters:

valcollection value type: The value to find

Returns:

indexint: The index of the first occurrence of the value, or the size of the container if not found. When several values match, only the first index is returned.

getClassName()¶

Accessor to the object’s name.

Returns:

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

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getSize()¶

Get the collection’s dimension (or size).

Returns:

nint: The number of components in the collection.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool: True if the name is not empty and not the default one.

isBlank()¶

Check if the description is blank.

Returns:

isBlankbool: True if all the components are empty.

Examples

>>> import openturns as ot
>>> vector = ot.Description(3)
>>> vector.isBlank()
True
>>> vector = ot.Description(['P1', 'P2', ' '])
>>> vector.isBlank()
False

isEmpty()¶

Tell if the collection is empty.

Returns:

isEmptybool: True if there is no element in the collection.

Examples

>>> import openturns as ot
>>> x = ot.Point(2)
>>> x.isEmpty()
False
>>> x.clear()
>>> x.isEmpty()
True

resize(newSize)¶

Change the size of the collection.

Parameters:

newSizepositive int: New size of the collection.

Notes

If the new size is smaller than the older one, the last elements are thrown away, else the new elements are set to the default value of the element type.

Examples

>>> import openturns as ot
>>> x = ot.Point(2, 4)
>>> print(x)
[4,4]
>>> x.resize(1)
>>> print(x)
[4]
>>> x.resize(4)
>>> print(x)
[4,0,0,0]

select(marginalIndices)¶

Selection from indices.

Parameters:

indicessequence of int: Indices to select

Returns:

collsequence: Sub-collection of values at the selection indices.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

Examples using the class¶

Estimate correlation coefficients

Compute SRC indices confidence intervals

Estimate a GEV on the Venice sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on race times data

Perform stepwise regression

Compute grouped indices for the Ishigami function

Validate a polynomial chaos

Create a polynomial chaos metamodel

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Plot enumeration rules

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Kriging: metamodel of the Branin-Hoo function

Non parametric Adaptive Importance Sampling (NAIS)

Subset Sampling

Cross Entropy Importance Sampling

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Estimate Sobol’ indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

Estimate Sobol' indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

Example of sensitivity analyses on the wing weight model

Plot the Smolyak quadrature

Merge nodes in Smolyak quadrature

Use the Smolyak quadrature

Calibration without observed inputs

Calibration of the logistic model

Customize your Metropolis-Hastings algorithm

Compute confidence intervals of a regression model from data

Compute confidence intervals of a univariate noisy function

Multi-objective optimization using Pagmo

Estimate extrema iteratively

Estimate threshold exceedance iteratively

Plot the log-likelihood contours of a distribution

OpenTURNS

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

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Description¶

Examples using the class¶