Description

class Description(*args)

Collection of strings.

Available constructors:

Description(size=0, value=’ ‘)

Description(sequence)

Parameters:

sizeint,

Size of the collection.

valuestr

Value set to the size elements.

sequencesequence of str

Components of the vector.

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.
getName()	Accessor to the object's name.
getSize()	Get the collection's dimension (or size).
hasName()	Test if the object is named.
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.
sort()	Sort the list.

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]

__init__(*args)

static BuildDefault(*args)

Build a default description.

Parameters:

sizeint,

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__).

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getSize()

Get the collection’s dimension (or size).

Returns:

nint

The number of components in the collection.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

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.

sort()

Sort the list.

The list is sorted in-place.

Examples using the class

Estimate correlation coefficients

Estimate correlation coefficients

Compute squared SRC indices confidence intervals

Compute squared SRC indices confidence intervals

Estimate a GEV on the Venice sea-levels data

Estimate a GEV on the Venice sea-levels data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the Dow Jones Index data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the daily rainfall data

Estimate a GEV on race times data

Estimate a GEV on race times data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Perform stepwise regression

Perform stepwise regression

Compute grouped indices for the Ishigami function

Compute grouped indices for the Ishigami function

Validate a polynomial chaos

Validate a polynomial chaos

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos metamodel from a data set

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

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

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Conditional expectation of a polynomial chaos expansion

Conditional expectation of a polynomial chaos expansion

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Kriging: choose a polynomial trend

Kriging: choose a polynomial trend

Subset Sampling

Subset Sampling

Non parametric Adaptive Importance Sampling (NAIS)

Non parametric Adaptive Importance Sampling (NAIS)

Cross Entropy Importance 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

Example of sensitivity analyses on the wing weight model

Plot the Smolyak quadrature

Plot the Smolyak quadrature

Calibration without observed inputs

Calibration without observed inputs

Customize your Metropolis-Hastings algorithm

Customize your Metropolis-Hastings algorithm

Bayesian calibration of hierarchical fission gas release models

Bayesian calibration of hierarchical fission gas release models

Compute confidence intervals of a regression model from data

Compute confidence intervals of a regression model from data

Compute confidence intervals of a univariate noisy function

Compute confidence intervals of a univariate noisy function

Plot the log-likelihood contours of a distribution

Plot the log-likelihood contours of a distribution