Compact

class Compact(*args)

Compact history storage strategy.

Available constructors:

Compact(N)

Parameters

Ninteger

minimum number of points to store.

See also

HistoryStrategy, Null, Full, Last

Notes

The compact strategy stores a regularly spaced sub-sample where the minimum size of the stored numerical sample is . OpenTURNS proceeds as follows :

1. it stores the first simulations : the size of the stored sample is ,

2. it selects only 1 out of 2 of the stored simulations : then the size of the stored sample decreases to (this is the compact step),

3. it stores the next simulations when selecting 1 out of 2 of the next simulations : the size of the stored sample is ,

4. it selects only 1 out of 2 of the stored simulations : then the size of the stored sample decreases to ,

5. it stores the next simulations when selecting 1 out of 4 of the next simulations : the size of the stored sample is ,

6. then it keeps on until reaching the stopping criteria.

The stored numerical sample will have a size within and if at least one cycle has been done, else it will be at most .

Methods

clear()	Clear the stored points.
getClassName()	Accessor to the object’s name.
getHalfMaximumSize()	Accessor to the half maximum number of points to store.
getId()	Accessor to the object’s id.
getIndex()	Accessor to the index.
getName()	Accessor to the object’s name.
getSample()	Accessor to the stored sample.
getShadowedId()	Accessor to the object’s shadowed id.
getVisibility()	Accessor to the object’s visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
setDimension(dimension)	Set the dimension of points to store.
setName(name)	Accessor to the object’s name.
setShadowedId(id)	Accessor to the object’s shadowed id.
setVisibility(visible)	Accessor to the object’s visibility state.
store(*args)	Store points or samples.

__init__(*args)

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

clear()

Clear the stored points.

Notes

It erases the previously stored points

getClassName()

Accessor to the object’s name.

Returns

class_namestr

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

getHalfMaximumSize()

Accessor to the half maximum number of points to store.

Returns

Ninteger

The half maximum number of points to store.

getId()

Accessor to the object’s id.

Returns

idint

Internal unique identifier.

getIndex()

Accessor to the index.

Returns

indexinteger

The number of the stored points.

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getSample()

Accessor to the stored sample.

Returns

sampleSample

Numerical sample which is the collection of points stored by the history strategy.

getShadowedId()

Accessor to the object’s shadowed id.

Returns

idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns

visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns

hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool

True if the name is not empty and not the default one.

setDimension(dimension)

Set the dimension of points to store.

This method must be called before calling the store method.

Parameters

dimension: int

Dimension of points to store

Notes

It erases the previously stored points

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters

idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters

visiblebool

Visibility flag.

store(*args)

Store points or samples.

Parameters

datasequence of float or 2-d sequence of float

Point or sample to store.

Notes

It adds a unique point or all the point of the sample in the natural order to the history.