ScalarCollection

class ScalarCollection(*args)

Collection.

Methods

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.
getSize()	Get the collection's dimension (or size).
isEmpty()	Tell if the collection is empty.
resize(newSize)	Change the size of the collection.
select(marginalIndices)	Selection from indices.

Examples

>>> import openturns as ot

• Collection of real values:

>>> ot.ScalarCollection(2)
[0,0]
>>> ot.ScalarCollection(2, 3.25)
[3.25,3.25]
>>> vector = ot.ScalarCollection([2.0, 1.5, 2.6])
>>> vector
[2,1.5,2.6]
>>> vector[1] = 4.2
>>> vector
[2,4.2,2.6]
>>> vector.add(3.8)
>>> vector
[2,4.2,2.6,3.8]

• Collection of complex values:

>>> ot.ComplexCollection(2)
[(0,0),(0,0)]
>>> ot.ComplexCollection(2, 3+4j)
[(3,4),(3,4)]
>>> vector = ot.ComplexCollection([2+3j, 1-4j, 3.0])
>>> vector
[(2,3),(1,-4),(3,0)]
>>> vector[1] = 4+3j
>>> vector
[(2,3),(4,3),(3,0)]
>>> vector.add(5+1j)
>>> vector
[(2,3),(4,3),(3,0),(5,1)]

• Collection of booleans:

>>> ot.BoolCollection(3)
[0,0,0]
>>> ot.BoolCollection(3, 1)
[1,1,1]
>>> vector = ot.BoolCollection([0, 1, 0])
>>> vector
[0,1,0]
>>> vector[1] = 0
>>> vector
[0,0,0]
>>> vector.add(1)
>>> vector
[0,0,0,1]

• Collection of distributions:

>>> print(ot.DistributionCollection(2))
[Uniform(a = -1, b = 1),Uniform(a = -1, b = 1)]
>>> print(ot.DistributionCollection(2, ot.Gamma(2.75, 1.0)))
[Gamma(k = 2.75, lambda = 1, gamma = 0),Gamma(k = 2.75, lambda = 1, gamma = 0)]
>>> vector = ot.DistributionCollection([ot.Normal(), ot.Uniform()])
>>> print(vector)
[Normal(mu = 0, sigma = 1),Uniform(a = -1, b = 1)]
>>> vector[1] = ot.Uniform(-0.5, 1)
>>> print(vector)
[Normal(mu = 0, sigma = 1),Uniform(a = -0.5, b = 1)]
>>> vector.add(ot.Gamma(2.75, 1.0))
>>> print(vector)
[Normal(mu = 0, sigma = 1),Uniform(a = -0.5, b = 1),Gamma(k = 2.75, lambda = 1, gamma = 0)]

__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.

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.

getSize()

Get the collection’s dimension (or size).

Returns:

nint

The number of components in the collection.

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.

Examples using the class

A quick start guide to the Point and Sample classes

A quick start guide to the Point and Sample classes

Estimate a GEV on race times data

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Distribution of estimators in linear regression

Distribution of estimators in linear regression

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

Polynomial chaos expansion cross-validation

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Sequentially adding new points to a Kriging

Sequentially adding new points to a Kriging

Using the FORM - SORM algorithms on a nonlinear function

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Calibration without observed inputs

Calibration without observed inputs

Sampling from an unnormalized probability density

Sampling from an unnormalized probability density

Linear Regression with interval-censored observations

Linear Regression with interval-censored observations

Integrate a function with Gauss-Kronrod algorithm

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Compute leave-one-out error of a polynomial chaos expansion

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Quick start guide to optimization

Quick start guide to optimization