Less

class Less(*args)

Less comparison operator.

Available constructors:

Less()

See also

Equal, Greater, GreaterOrEqual, LessOrEqual

Examples

>>> import openturns as ot
>>> operator = ot.Less()
>>> print(operator(1, 2))
True
>>> print(operator(2, 1))
False
>>> print(operator(2, 2))
False

Methods

__call__(a, b)	Call self as a function.
getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getName()	Accessor to the object's name.
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.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

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.

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.

Examples using the class

Estimate a probability with Latin Hypercube Sampling

Estimate a probability with Latin Hypercube Sampling

Estimate a probability with Monte Carlo

Estimate a probability with Monte Carlo

Specify a simulation algorithm

Specify a simulation algorithm

Create a threshold event

Create a threshold event

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Use the FORM algorithm in case of several design points

Use the FORM algorithm in case of several design points

Non parametric Adaptive Importance Sampling (NAIS)

Non parametric Adaptive Importance Sampling (NAIS)

Time variant system reliability problem

Time variant system reliability problem

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

Create unions or intersections of events

Create unions or intersections of events

Cross Entropy Importance Sampling

Cross Entropy Importance Sampling

Control algorithm termination

Control algorithm termination