com.yworks.yfiles.graph.labelmodels

Interface ILabelCandidateDescriptor

All Known Implementing Classes:

LabelCandidateDescriptor

public interface ILabelCandidateDescriptor

Interface that provides more information about a potential label geometry (a candidate) to an automatic labeling algorithm.

Typically, instances of this interface are created by an implementation of ILabelCandidateDescriptorProvider for a pair of an ILabel and a ILabelModelParameter that describes a potential placement of that label (a 'candidate').

See Also:

ILabelCandidateDescriptorProvider

Method Summary

Modifier and Type	Method and Description
double	getEdgeOverlapPenalty() Gets a penalty value that is used by labeling algorithms to judge the severity if this candidate overlaps with an edge path.
double	getNodeOverlapPenalty() Gets a penalty value that is used by labeling algorithms to judge the severity if this label candidate overlaps with a node's bounds.
double	getProfit() Gets a profit value that is used by labeling algorithms to judge how good this label candidate is.
boolean	isExternalCandidate() Gets a value indicating whether this candidate is deemed an external candidate.

Method Detail

getEdgeOverlapPenalty

double getEdgeOverlapPenalty()

Gets a penalty value that is used by labeling algorithms to judge the severity if this candidate overlaps with an edge path.

This value must be between 0 and 1 (inclusive). A value of 0 means that an overlap is considered not important, while a value of 1 means that an overlap is considered severe.

Returns:

The edge overlap penalty.

getNodeOverlapPenalty

double getNodeOverlapPenalty()

Gets a penalty value that is used by labeling algorithms to judge the severity if this label candidate overlaps with a node's bounds.

This value must be between 0 and 1 (inclusive). A value of 0 means that an overlap is considered not important, while a value of 1 means that an overlap is considered severe.

Returns:

The node overlap penalty.

getProfit

double getProfit()

Gets a profit value that is used by labeling algorithms to judge how good this label candidate is.

This value must be between 0 and 1 (inclusive). Higher profit means better candidates. Hence, there is a higher probability that the candidate is chosen by a labeling algorithm.

Returns:

The profit to be used by labeling algorithms.

isExternalCandidate

boolean isExternalCandidate()

Gets a value indicating whether this candidate is deemed an external candidate.

This is mainly for INode labels that are a visual part of the node's internals. It could be used for edge labels, too, if the edge occupies significant amounts of visual space.

Returns:

true if this describes an external candidate; false otherwise.