Indices¶

class Indices(*args)¶

Collection of unsigned integers.

Available constructors:

Indices(size=0, value=0)

Indices(sequence)

Parameters:

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

Methods

`add`(*args)	Append a component (in-place).
`at`(*args)	Access to an element of the collection.
`check`(bound)	Check that no value is repeated and no value exceeds the given bound.
`clear`()	Reset the collection to zero dimension.
`complement`(n)	Build the complement of the current indices wrt $\{0,\dots,n-1\}$ .
`fill`([initialValue, stepSize])	Fill the indices with a linear progression.
`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.
`isEmpty`()	Tell if the collection is empty.
`isIncreasing`()	Check if the indices are increasing.
`norm1`()	Compute the 1-norm of the indices.
`normInf`()	Compute the infinite norm of the indices.
`resize`(newSize)	Change the size of the collection.
`select`(marginalIndices)	Selection from indices.
`setName`(name)	Accessor to the object's name.

Examples

>>> import openturns as ot

Use the first constructor:

>>> ot.Indices(3)
[0,0,0]
>>> ot.Indices(3, 4)
[4,4,4]

Use the second constructor:

>>> vector = ot.Indices([100, 30, 70])
>>> vector
[100,30,70]

Use some functionalities:

>>> vector[1] = 20
>>> vector
[100,20,70]
>>> vector.add(50)
>>> vector
[100,20,70,50]

The Indices class has a method that can search for an element and return True if the element is in the indices, and False otherwise. We can access to it using the in keyword.

>>> indices = ot.Indices([3, 5, 7, 9])
>>> print(0 in indices)
False
>>> print(3 in indices)
True

__init__(*args)¶

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.

check(bound)¶

Check that no value is repeated and no value exceeds the given bound.

Parameters:

boundpositive int: The bound value.

Returns:

checkbool: True if no value is repeated and all values are < bound.

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=[]

complement(n)¶

Build the complement of the current indices wrt $\{0,\dots,n-1\}$ .

Parameters:

boundpositive int: The value of $n$ .

Returns:

complementIndices: The increasing collection of integers in $\{0,\dots,n-1\}$ not in the current indices.

Examples

>>> import openturns as ot
>>> indices = ot.Indices([1, 3, 4])
>>> print(indices.complement(7))
[0,2,5,6]

fill(initialValue=0, stepSize=1)¶

Fill the indices with a linear progression.

Starting from the start value initialValue by step stepSize.

Parameters:

initialValuepositive int: Initial value. By default it is equal to 0.
stepSizepositive int: Step size. By default it is equal to 1.

Examples

>>> import openturns as ot
>>> indices = ot.Indices(3)
>>> indices.fill()
>>> print(indices)
[0,1,2]
>>> indices = ot.Indices(3)
>>> indices.fill(2, 4)
>>> print(indices)
[2,6,10]

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.

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

isIncreasing()¶

Check if the indices are increasing.

Returns:

isIncreasingbool: True if the indices are increasing.

Examples

>>> import openturns as ot
>>> indices = ot.Indices(3)
>>> indices.fill()
>>> indices.isIncreasing()
True

norm1()¶

Compute the 1-norm of the indices.

Returns:

norm: int: The sum of the indices.

Examples

>>> import openturns as ot
>>> indices = ot.Indices([3, 5, 7, 9])
>>> print(indices.norm1())
24

normInf()¶

Compute the infinite norm of the indices.

Returns:

norm: int: The maximum of the indices.

Examples

>>> import openturns as ot
>>> indices = ot.Indices([3, 5, 7, 9])
>>> print(indices.normInf())
9

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.

Examples using the class¶

Compute squared SRC indices confidence intervals

Polynomial chaos exploitation

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

Plot enumeration rules

Cross Entropy Importance Sampling

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

Multi-objective optimization using Pagmo

OpenTURNS

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

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Indices¶

Examples using the class¶