MediumSafe

class MediumSafe(*args)

MediumSafe method.

Available constructors:

MediumSafe(solver=ot.Brent())

MediumSafe(solver, maximumDistance, stepSize)

Parameters:

solverSolver

Non linear solver used to research the intersection of the limit state function with the direction, on each segment of length stepSize, between the center of the space and maximumDistance (root research).

maximumDistancepositive float

Distance from the center of the standard space until which we research an intersection with the limit state function along each direction. By default, the maximum distance is equal to the value defined through the key RootStrategyImplementation-DefaultMaximumDistance of the ResourceMap.

stepSizefloat

Length of each segment inside which the root research is performed. By default, the step size is equal to the value defined through the key RootStrategyImplementation-DefaultStepSize of the ResourceMap.

See also

DirectionalSampling

Notes

The MediumSafe strategy is the following: for each direction, we go along the direction by step of length stepSize from the origin to the maximum distant point (at distance maximumDistance from the center of the standard space) and we check whether there is a sign changement on each segment so formed.

At the first sign changement, we research one root in the concerned segment with the selected non linear solver. Then, the segment [root, maximum distant point] is considered within the failure space.

If stepSize is small enough, this strategy guarantees us to find the root which is the nearest from the origin.

Methods

getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getMaximumDistance()	Get the maximum distance.
getName()	Accessor to the object's name.
getOriginValue()	Get the origin value.
getShadowedId()	Accessor to the object's shadowed id.
getSolver()	Get the solver.
getStepSize()	Get the step size.
getVisibility()	Accessor to the object's visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
setMaximumDistance(maximumDistance)	Set the maximum distance.
setName(name)	Accessor to the object's name.
setOriginValue(originValue)	Set the origin value.
setShadowedId(id)	Accessor to the object's shadowed id.
setSolver(solver)	Set the solver.
setStepSize(stepSize)	Set the step size.
setVisibility(visible)	Accessor to the object's visibility state.
solve(function, value)	Give all the roots found applying the root strategy.

__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.

getMaximumDistance()

Get the maximum distance.

Returns:

maximumDistancepositive float

Distance from the center of the standard space until which we research an intersection with the limit state function along each direction. By default, the maximum distance is equal to the value defined through the key RootStrategyImplementation-DefaultMaximumDistance of the ResourceMap.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getOriginValue()

Get the origin value.

Returns:

originfloat

Value of the limit state function at the center of the standard space.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:

idint

Internal unique identifier.

getSolver()

Get the solver.

Returns:

solverSolver

Non linear solver which will research the root in a segment.

getStepSize()

Get the step size.

Returns:

stepSizefloat

Length of each segment inside which the root research is performed. By default, the step size is equal to the value defined through the key RootStrategyImplementation-DefaultStepSize of the ResourceMap.

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.

setMaximumDistance(maximumDistance)

Set the maximum distance.

Parameters:

maximumDistancepositive float

Distance from the center of the standard space until which we research an intersection with the limit state function along each direction. By default, the maximum distance is equal to the value defined through the key RootStrategyImplementation-DefaultMaximumDistance of the ResourceMap.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setOriginValue(originValue)

Set the origin value.

Parameters:

originfloat

Value of the limit state function at the center of the standard space.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:

idint

Internal unique identifier.

setSolver(solver)

Set the solver.

Parameters:

solverSolver

Non linear solver which will research the root in a segment.

setStepSize(stepSize)

Set the step size.

Parameters:

stepSizefloat

Length of each segment inside which the root research is performed. By default, the step size is equal to the value defined through the key RootStrategyImplementation-DefaultStepSize of the ResourceMap.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:

visiblebool

Visibility flag.

solve(function, value)

Give all the roots found applying the root strategy.

Parameters:

functionFunction

Function from to along the ray, a linear function along the direction.

valuefloat

Returns:

rootsScalarCollection

All the roots found applying the root strategy.

• If SafeAndSlow: all the real values such as function(x) = value researched in each segment of length stepSize, within [origin, maximumDistance].

• If RiskyAndFast: the real value such as function(x) = value researched within [origin, maximumDistance].

• If MediumSafe: the real value such as function(x) = value researched the first segment of length stepSize, within [origin, maximumDistance] where a sign changement of function has been detected.

Examples using the class

Use the Adaptive Directional Stratification Algorithm

Use the Adaptive Directional Stratification Algorithm

Use the Directional Sampling Algorithm

Use the Directional Sampling Algorithm