ProcessSample¶

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class ProcessSample(*args)¶

Collection of fields.

Available constructors:

ProcessSample(mesh, K, d)

ProcessSample(K, field)

Parameters

meshMesh: The mesh shared by all the fields in the collection.
Kint: Number of fields in the collection.
dint: Dimension of the values of the field.
fieldField: One field.

Notes

A ProcessSample stores a sample of fields. We note K the number of fields contained in the process sample and d the dimension of the values associated to each vertex of the common mesh $\cM \in \Rset^n$ .

These fields can be generated by a stochastic process.

In the first usage, we fix the common mesh with mesh, the number of fields contained in the sample with K and the dimension of the values with d. The values of the fields are by default fixed to zero.
In the second usage, the collection of fields is filled with K copies of the given field field.

We note $\vect{x}_i^k \in \Rset^d$ the value of the field k at the vertex i. We note N the number of vertices of $\cM$ , with $0 \leq i \leq N-1$ and $1 \leq k \leq K$ .

Examples

Create a bi dimensional mesh as a box:

>>> import openturns as ot
>>> myIndices = [10, 5]
>>> myMesher = ot.IntervalMesher(myIndices)
>>> lowerBound = [0.0, 0.0]
>>> upperBound = [2.0, 1.0]
>>> myInterval = ot.Interval(lowerBound, upperBound)
>>> myMesh = myMesher.build(myInterval)

Create a second order normal porcess of dimension 3:

>>> amplitude = [5]
>>> scale = [3, 3]
>>> model = ot.ExponentialModel(scale, amplitude)
>>> myProcess = ot.GaussianProcess(model, myMesh)

Generate a sample of different fields:

>>> n = 10
>>> mySampleFields = myProcess.getSample(n)

Duplicate the same field:

>>> myField = myProcess.getRealization()
>>> n = 10
>>> mySampleFields2 = ot.ProcessSample(n, myField)

Methods

`add`(self, \*args)	Add a field to the collection.
`computeMean`(self)	Compute the mean field of the collection of fields.
`computeQuantilePerComponent`(self, \*args)	Compute the temporal mean of the values of the fields.
`computeSpatialMean`(self)	Compute the spatial mean of the values of the fields.
`computeTemporalMean`(self)	Compute the temporal mean of the values of the fields.
`drawMarginal`(self[, index, interpolate])	Draw the selected field.
`getClassName`(self)	Accessor to the object’s name.
`getDimension`(self)	Get the dimension of the values of fields.
`getId`(self)	Accessor to the object’s id.
`getImplementation`(self)	Accessor to the underlying implementation.
`getMarginal`(self, \*args)	Marginal sample accessor.
`getMesh`(self)	Get the mesh of the fields.
`getName`(self)	Accessor to the object’s name.
`getSize`(self)	Get the size of the collection of fields.
`getTimeGrid`(self)	Get the time grid of the fields.
`setName`(self, name)	Accessor to the object’s name.

__init__(self, \*args)¶: Initialize self. See help(type(self)) for accurate signature.

add(self, \*args)¶

Add a field to the collection.

Parameters

fieldField: A new field to add. This field shares the same mesh and the same dimension as the other fields of the collection.

computeMean(self)¶

Compute the mean field of the collection of fields.

Returns

meanField: The mean field has the same dimension d and the same mesh as the fields contained in the collection. At each vertex of the mesh, we calculate the mean of the values at this vertex of the K fields contained in the process sample:

$\forall i \in [0,N-1], \quad \dfrac{1}{K} \sum_{k=1}^K \vect{x}_i^k$

computeQuantilePerComponent(self, \*args)¶

Compute the temporal mean of the values of the fields.

Parameters

pfloat, $0 \leq p \leq 1$ , or sequence of float: Order of the quantile.

Returns

quantileField or ProcessSample: This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we estimate the component-wise quantile of order p, using the empirical quantile.

computeSpatialMean(self)¶

Compute the spatial mean of the values of the fields.

Returns

spatialMeanSample: Its size is the number K of fields in the collection. Its dimension is d. The k numerical point is the spatial mean of the field k:

$\forall k \in [1,K], \quad \dfrac{1}{N}\sum_{i=0}^{N-1} \vect{x}_i^k$

computeTemporalMean(self)¶

Compute the temporal mean of the values of the fields.

Returns

spatialMeanSample: Its size is the number K of fields in the collection. Its dimension is d. The k numerical point is the temporal mean of the field k:

$\forall k \in [1,K], \quad \dfrac{1}{N}\sum_{i=0}^{N-1} \vect{x}_i^k$
This method can be used only when the mesh can be interpreted as a regular grid.

drawMarginal(self, index=0, interpolate=True)¶

Draw the selected field.

Parameters

indiceint: Index of the field that is drawn in the graph.
interpolatebool (optional, default=True): Whether to draw interpolated values.

Returns

graphGraph: The graph of the selected field using the interpolate method.

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getDimension(self)¶

Get the dimension of the values of fields.

Returns

dint: Dimension of the values of the fields.

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getImplementation(self)¶

Accessor to the underlying implementation.

Returns

implImplementation: The implementation class.

getMarginal(self, \*args)¶

Marginal sample accessor.

Parameters

indiceint or sequence of int: Index of the marginals.

Returns

sampleProcessSample: The marginal sample.

getMesh(self)¶

Get the mesh of the fields.

Returns

meshMesh: The mesh shared by all the fields of the collection.

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getSize(self)¶

Get the size of the collection of fields.

Returns

Kint: Number of fields in the collection.

getTimeGrid(self)¶

Get the time grid of the fields.

Returns

meshRegularGrid: The time grid shared by all the fields of the collection. Can be used only if the mesh can be interpreted as a regular time grid.

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

OpenTURNS

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

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