Compact

class Compact(*args)

Compact history storage strategy.

Available constructors:
Compact(N)
Parameters:

N : integer

minimum number of points to store.

Notes

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

  1. it stores the first 2N simulations : the size of the stored sample is 2N,
  2. it selects only 1 out of 2 of the stored simulations : then the size of the stored sample decreases to N (this is the compact step),
  3. it stores the next N simulations when selecting 1 out of 2 of the next simulations : the size of the stored sample is 2N,
  4. it selects only 1 out of 2 of the stored simulations : then the size of the stored sample decreases to N,
  5. it stores the next N simulations when selecting 1 out of 4 of the next simulations : the size of the stored sample is 2N,
  6. then it keeps on until reaching the stopping criteria.

The stored numerical sample will have a size within N and 2N if at least one cycle has been done, else it will be at most N.

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.
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)
clear()

Clear the stored points.

Notes

It erases the previously stored points

getClassName()

Accessor to the object’s name.

Returns:

class_name : str

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

getHalfMaximumSize()

Accessor to the half maximum number of points to store.

Returns:

N : integer

The half maximum number of points to store.

getId()

Accessor to the object’s id.

Returns:

id : int

Internal unique identifier.

getIndex()

Accessor to the index.

Returns:

index : integer

The number of the stored points.

getName()

Accessor to the object’s name.

Returns:

name : str

The name of the object.

getSample()

Accessor to the stored sample.

Returns:

sample : Sample

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

getShadowedId()

Accessor to the object’s shadowed id.

Returns:

id : int

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns:

visible : bool

Visibility flag.

hasName()

Test if the object is named.

Returns:

hasName : bool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:

hasVisibleName : bool

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

setName(name)

Accessor to the object’s name.

Parameters:

name : str

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:

id : int

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:

visible : bool

Visibility flag.

store(*args)

Store points or samples.

Parameters:

data : sequence 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.