ProcessSample

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../../_images/ProcessSample.png
class ProcessSample(*args)

Collection of fields.

Available constructors:

ProcessSample(mesh, K, d)

ProcessSample(mesh, coll)

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.

collsequence of Sample

List of field values.

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(*args)

Add a field to the collection.

computeCenteredMoment(k)

Compute the centered moment field of the collection of fields.

computeEmpiricalCDF(point[, tail])

Compute the empirical CDF field of the collection of fields.

computeKurtosis()

Compute the kurtosis field of the collection of fields.

computeMean()

Compute the mean field of the collection of fields.

computeMedian()

Compute the median field of the collection of fields.

computeQuantilePerComponent(*args)

Compute the quantile field of the collection of fields.

computeRange()

Compute the range field of the collection of fields.

computeRawMoment(k)

Compute the raw moment field of the collection of fields.

computeSkewness()

Compute the skewness field of the collection of fields.

computeSpatialMean()

Compute the spatial mean of the values of the fields.

computeStandardDeviation()

Compute the standard deviation field of the collection of fields.

computeTemporalMean()

Compute the temporal mean of the values of the fields.

computeVariance()

Compute the variance field of the collection of fields.

draw([interpolate])

Draw the whole field.

drawCorrelation()

Draw correlation between all marginals.

drawMarginal([index, interpolate])

Draw the selected field.

drawMarginalCorrelation(i, j)

Draw correlation between 2 marginals.

getClassName()

Accessor to the object’s name.

getDimension()

Get the dimension of the values of fields.

getField(i)

Field accessor.

getId()

Accessor to the object’s id.

getImplementation()

Accessor to the underlying implementation.

getMarginal(*args)

Marginal sample accessor.

getMax()

Compute the max field of the collection of fields.

getMesh()

Get the mesh of the fields.

getMin()

Compute the min field of the collection of fields.

getName()

Accessor to the object’s name.

getSize()

Get the size of the collection of fields.

getTimeGrid()

Get the time grid of the fields.

setField(field, i)

Field accessor.

setName(name)

Accessor to the object’s name.

__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

add(*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.

computeCenteredMoment(k)

Compute the centered moment field of the collection of fields.

Parameters
kint

Order of the moment.

Returns
momentField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise centered moment of order n.

computeEmpiricalCDF(point, tail=False)

Compute the empirical CDF field of the collection of fields.

Parameters
pointsequence of float

The point at which all the marginal CDF are computed. Its dimension must match the process sample dimension.

tailbool (optional, default=False)

Flag to if it is the CDF or the complementary CDF which is computed.

Returns
cdfField

This field is scalar and has the same size as the process sample. At each vertex of the mesh, we compute the component-wise empirical CDF at the given point.

computeKurtosis()

Compute the kurtosis field of the collection of fields.

Returns
kurtosisField

The squewness field has the same dimension d and the same mesh as the fields contained in the collection. At each vertex of the mesh, we compute the kurtosis of the values at this vertex of the K fields contained in the process sample.

computeMean()

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 compute 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

computeMedian()

Compute the median field of the collection of fields.

Returns
medianField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise empirical median. It is the same as computeQuantilePerComponent(0.5).

computeQuantilePerComponent(*args)

Compute the quantile field of the collection of 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 compute the component-wise empirical quantile of order p.

computeRange()

Compute the range field of the collection of fields.

Returns
rangeField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise range, i.e. the difference between the largest and the lowest value in the field collection.

computeRawMoment(k)

Compute the raw moment field of the collection of fields.

Parameters
kint

Order of the moment.

Returns
momentField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise raw moment of order n.

computeSkewness()

Compute the skewness field of the collection of fields.

Returns
skewnessField

The squewness field has the same dimension d and the same mesh as the fields contained in the collection. At each vertex of the mesh, we compute the skewness of the values at this vertex of the K fields contained in the process sample.

computeSpatialMean()

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

computeStandardDeviation()

Compute the standard deviation field of the collection of fields.

Returns
stddevField

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

\forall i \in [0,N-1], \quad \sqrt{\dfrac{1}{K-1} \sum_{k=1}^K (\vect{x}_i^k - \hat{\mu}_i)^2}

computeTemporalMean()

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.
computeVariance()

Compute the variance field of the collection of fields.

Returns
varianceField

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

draw(interpolate=True)

Draw the whole field.

Parameters
interpolatebool (optional, default=True)

Whether to draw interpolated values.

Returns
graphGridLayout

The graph of the whole field using the interpolate method.

drawCorrelation()

Draw correlation between all marginals.

This is only available on a 1-d mesh.

Returns
graphGridLayout

The graph of the correlation between all marginals.

drawMarginal(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.

drawMarginalCorrelation(i, j)

Draw correlation between 2 marginals.

This is only available on a 1-d mesh.

Parameters
i, jint, i, j < d

Marginal indices.

Returns
graphGraph

The graph of the correlation between 2 marginals.

getClassName()

Accessor to the object’s name.

Returns
class_namestr

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

getDimension()

Get the dimension of the values of fields.

Returns
dint

Dimension of the values of the fields.

getField(i)

Field accessor.

Parameters
iint

Field index

Returns
fieldField

i-th field in the sample.

getId()

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns
implImplementation

The implementation class.

getMarginal(*args)

Marginal sample accessor.

Parameters
indiceint or sequence of int

Index of the marginals.

Returns
sampleProcessSample

The marginal sample.

getMax()

Compute the max field of the collection of fields.

Returns
maxField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise maximum.

getMesh()

Get the mesh of the fields.

Returns
meshMesh

The mesh shared by all the fields of the collection.

getMin()

Compute the min field of the collection of fields.

Returns
minField

This field has the same size and the same dimension as the fields of the collection. At each vertex of the mesh, we compute the component-wise minimum.

getName()

Accessor to the object’s name.

Returns
namestr

The name of the object.

getSize()

Get the size of the collection of fields.

Returns
Kint

Number of fields in the collection.

getTimeGrid()

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.

setField(field, i)

Field accessor.

Parameters
iint

Field index

fieldField

i-th field in the sample.

setName(name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.