From 020ac2562934f85c4e80fc8e3aa46e94b34ada8c Mon Sep 17 00:00:00 2001 From: Nees Jan van Eck Date: Mon, 29 Aug 2022 21:58:29 +0200 Subject: [PATCH] Add docs --- docs/.nojekyll | 1 + docs/assets/highlight.css | 64 + docs/assets/icons.css | 1043 ++++++++++++ docs/assets/icons.png | Bin 0 -> 9615 bytes docs/assets/icons@2x.png | Bin 0 -> 28144 bytes docs/assets/main.js | 52 + docs/assets/search.js | 1 + docs/assets/style.css | 1414 +++++++++++++++++ docs/assets/widgets.png | Bin 0 -> 480 bytes docs/assets/widgets@2x.png | Bin 0 -> 855 bytes .../classes/index.CPMClusteringAlgorithm.html | 82 + docs/classes/index.Clustering.html | 72 + docs/classes/index.ComponentsAlgorithm.html | 8 + .../index.FastLocalMovingAlgorithm.html | 153 ++ ...dex.GradientDescentVOSLayoutAlgorithm.html | 177 +++ ...dex.IncrementalCPMClusteringAlgorithm.html | 91 ++ ...index.IterativeCPMClusteringAlgorithm.html | 122 ++ docs/classes/index.Layout.html | 78 + docs/classes/index.LeidenAlgorithm.html | 193 +++ docs/classes/index.LocalMergingAlgorithm.html | 135 ++ docs/classes/index.LouvainAlgorithm.html | 168 ++ docs/classes/index.Network.html | 205 +++ .../index.StandardLocalMovingAlgorithm.html | 117 ++ docs/classes/index.VOSLayoutAlgorithm.html | 72 + docs/classes/run.NetworkAnalysis.html | 5 + docs/classes/run.NetworkClustering.html | 27 + docs/classes/run.NetworkHelper.html | 1 + docs/classes/run.NetworkLayout.html | 32 + docs/classes/utils.Random.html | 17 + docs/enums/run.ClusteringAlgorithms.html | 3 + .../enums/run.ClusteringQualityFunctions.html | 3 + docs/enums/run.LayoutQualityFunctions.html | 3 + docs/enums/run.NormalizationMethods.html | 3 + docs/index.html | 7 + .../interfaces/index.ClusteringAlgorithm.html | 8 + ...ndex.ClusteringParametersWithClusters.html | 6 + .../index.ClusteringParametersWithNNodes.html | 5 + .../index.IncrementalClusteringAlgorithm.html | 16 + ...tConstructorParametersWithCoordinates.html | 6 + ...LayoutConstructorParametersWithNNodes.html | 5 + ...structorParametersWithNNodesAndRandom.html | 8 + .../index.NetworkConstructorParameters.html | 21 + .../index.QualityClusteringAlgorithm.html | 15 + .../index.QualityLayoutAlgorithm.html | 15 + docs/modules.html | 1 + docs/modules/index.html | 8 + docs/modules/run.html | 28 + docs/modules/utils.html | 83 + 48 files changed, 4574 insertions(+) create mode 100644 docs/.nojekyll create mode 100644 docs/assets/highlight.css create mode 100644 docs/assets/icons.css create mode 100644 docs/assets/icons.png create mode 100644 docs/assets/icons@2x.png create mode 100644 docs/assets/main.js create mode 100644 docs/assets/search.js create mode 100644 docs/assets/style.css create mode 100644 docs/assets/widgets.png create mode 100644 docs/assets/widgets@2x.png create mode 100644 docs/classes/index.CPMClusteringAlgorithm.html create mode 100644 docs/classes/index.Clustering.html create mode 100644 docs/classes/index.ComponentsAlgorithm.html create mode 100644 docs/classes/index.FastLocalMovingAlgorithm.html create mode 100644 docs/classes/index.GradientDescentVOSLayoutAlgorithm.html create mode 100644 docs/classes/index.IncrementalCPMClusteringAlgorithm.html create mode 100644 docs/classes/index.IterativeCPMClusteringAlgorithm.html create mode 100644 docs/classes/index.Layout.html create mode 100644 docs/classes/index.LeidenAlgorithm.html create mode 100644 docs/classes/index.LocalMergingAlgorithm.html create mode 100644 docs/classes/index.LouvainAlgorithm.html create mode 100644 docs/classes/index.Network.html create mode 100644 docs/classes/index.StandardLocalMovingAlgorithm.html create mode 100644 docs/classes/index.VOSLayoutAlgorithm.html create mode 100644 docs/classes/run.NetworkAnalysis.html create mode 100644 docs/classes/run.NetworkClustering.html create mode 100644 docs/classes/run.NetworkHelper.html create mode 100644 docs/classes/run.NetworkLayout.html create mode 100644 docs/classes/utils.Random.html create mode 100644 docs/enums/run.ClusteringAlgorithms.html create mode 100644 docs/enums/run.ClusteringQualityFunctions.html create mode 100644 docs/enums/run.LayoutQualityFunctions.html create mode 100644 docs/enums/run.NormalizationMethods.html create mode 100644 docs/index.html create mode 100644 docs/interfaces/index.ClusteringAlgorithm.html create mode 100644 docs/interfaces/index.ClusteringParametersWithClusters.html create mode 100644 docs/interfaces/index.ClusteringParametersWithNNodes.html create mode 100644 docs/interfaces/index.IncrementalClusteringAlgorithm.html create mode 100644 docs/interfaces/index.LayoutConstructorParametersWithCoordinates.html create mode 100644 docs/interfaces/index.LayoutConstructorParametersWithNNodes.html create mode 100644 docs/interfaces/index.LayoutConstructorParametersWithNNodesAndRandom.html create mode 100644 docs/interfaces/index.NetworkConstructorParameters.html create mode 100644 docs/interfaces/index.QualityClusteringAlgorithm.html create mode 100644 docs/interfaces/index.QualityLayoutAlgorithm.html create mode 100644 docs/modules.html create mode 100644 docs/modules/index.html create mode 100644 docs/modules/run.html create mode 100644 docs/modules/utils.html diff --git a/docs/.nojekyll b/docs/.nojekyll new file mode 100644 index 0000000..e2ac661 --- /dev/null +++ b/docs/.nojekyll @@ -0,0 +1 @@ +TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false. \ No newline at end of file diff --git a/docs/assets/highlight.css b/docs/assets/highlight.css new file mode 100644 index 0000000..b1b8926 --- /dev/null +++ b/docs/assets/highlight.css @@ -0,0 +1,64 @@ +:root { + --light-hl-0: #098658; + --dark-hl-0: #B5CEA8; + --light-hl-1: #000000; + --dark-hl-1: #D4D4D4; + --light-hl-2: #001080; + --dark-hl-2: #9CDCFE; + --light-hl-3: #795E26; + --dark-hl-3: #DCDCAA; + --light-hl-4: #A31515; + --dark-hl-4: #CE9178; + --light-hl-5: #CD3131; + --dark-hl-5: #F44747; + --light-code-background: #F5F5F5; + --dark-code-background: #1E1E1E; +} + +@media (prefers-color-scheme: light) { :root { + --hl-0: var(--light-hl-0); + --hl-1: var(--light-hl-1); + --hl-2: var(--light-hl-2); + --hl-3: var(--light-hl-3); + --hl-4: var(--light-hl-4); + --hl-5: var(--light-hl-5); + --code-background: var(--light-code-background); +} } + +@media (prefers-color-scheme: dark) { :root { + --hl-0: var(--dark-hl-0); + --hl-1: var(--dark-hl-1); + --hl-2: var(--dark-hl-2); + --hl-3: var(--dark-hl-3); + --hl-4: var(--dark-hl-4); + --hl-5: var(--dark-hl-5); + --code-background: var(--dark-code-background); +} } + +body.light { + --hl-0: var(--light-hl-0); + --hl-1: var(--light-hl-1); + --hl-2: var(--light-hl-2); + --hl-3: var(--light-hl-3); + --hl-4: var(--light-hl-4); + --hl-5: var(--light-hl-5); + --code-background: var(--light-code-background); +} + +body.dark { + --hl-0: var(--dark-hl-0); + --hl-1: var(--dark-hl-1); + --hl-2: var(--dark-hl-2); + --hl-3: var(--dark-hl-3); + --hl-4: var(--dark-hl-4); + --hl-5: var(--dark-hl-5); + --code-background: var(--dark-code-background); +} + +.hl-0 { color: var(--hl-0); } +.hl-1 { color: var(--hl-1); } +.hl-2 { color: var(--hl-2); } +.hl-3 { color: var(--hl-3); } +.hl-4 { color: var(--hl-4); } +.hl-5 { color: var(--hl-5); } +pre, code { background: var(--code-background); } diff --git a/docs/assets/icons.css b/docs/assets/icons.css new file mode 100644 index 0000000..776a356 --- /dev/null +++ b/docs/assets/icons.css @@ -0,0 +1,1043 @@ +.tsd-kind-icon { + display: block; + position: relative; + padding-left: 20px; + text-indent: -20px; +} +.tsd-kind-icon:before { + content: ""; + display: inline-block; + vertical-align: middle; + width: 17px; + height: 17px; + margin: 0 3px 2px 0; + background-image: url(./icons.png); +} +@media (-webkit-min-device-pixel-ratio: 1.5), (min-resolution: 144dpi) { + .tsd-kind-icon:before { + background-image: url(./icons@2x.png); + background-size: 238px 204px; + } +} + +.tsd-signature.tsd-kind-icon:before { + background-position: 0 -153px; +} + +.tsd-kind-object-literal > .tsd-kind-icon:before { + background-position: 0px -17px; +} +.tsd-kind-object-literal.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -17px; +} +.tsd-kind-object-literal.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -17px; +} + +.tsd-kind-class > .tsd-kind-icon:before { + background-position: 0px -34px; +} +.tsd-kind-class.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -34px; +} +.tsd-kind-class.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -34px; +} + +.tsd-kind-class.tsd-has-type-parameter > .tsd-kind-icon:before { + background-position: 0px -51px; +} +.tsd-kind-class.tsd-has-type-parameter.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -17px -51px; +} +.tsd-kind-class.tsd-has-type-parameter.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -51px; +} + +.tsd-kind-interface > .tsd-kind-icon:before { + background-position: 0px -68px; +} +.tsd-kind-interface.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -68px; +} +.tsd-kind-interface.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -68px; +} + +.tsd-kind-interface.tsd-has-type-parameter > .tsd-kind-icon:before { + background-position: 0px -85px; +} +.tsd-kind-interface.tsd-has-type-parameter.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -17px -85px; +} +.tsd-kind-interface.tsd-has-type-parameter.tsd-is-private + > .tsd-kind-icon:before { + background-position: -34px -85px; +} + +.tsd-kind-namespace > .tsd-kind-icon:before { + background-position: 0px -102px; +} +.tsd-kind-namespace.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -102px; +} +.tsd-kind-namespace.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -102px; +} + +.tsd-kind-module > .tsd-kind-icon:before { + background-position: 0px -102px; +} +.tsd-kind-module.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -102px; +} +.tsd-kind-module.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -102px; +} + +.tsd-kind-enum > .tsd-kind-icon:before { + background-position: 0px -119px; +} +.tsd-kind-enum.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -119px; +} +.tsd-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -119px; +} + +.tsd-kind-enum-member > .tsd-kind-icon:before { + background-position: 0px -136px; +} +.tsd-kind-enum-member.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -136px; +} +.tsd-kind-enum-member.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -136px; +} + +.tsd-kind-signature > .tsd-kind-icon:before { + background-position: 0px -153px; +} +.tsd-kind-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -153px; +} +.tsd-kind-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -153px; +} + +.tsd-kind-type-alias > .tsd-kind-icon:before { + background-position: 0px -170px; +} +.tsd-kind-type-alias.tsd-is-protected > .tsd-kind-icon:before { + background-position: -17px -170px; +} +.tsd-kind-type-alias.tsd-is-private > .tsd-kind-icon:before { + background-position: -34px -170px; +} + +.tsd-kind-type-alias.tsd-has-type-parameter > .tsd-kind-icon:before { + background-position: 0px -187px; +} +.tsd-kind-type-alias.tsd-has-type-parameter.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -17px -187px; +} +.tsd-kind-type-alias.tsd-has-type-parameter.tsd-is-private + > .tsd-kind-icon:before { + background-position: -34px -187px; +} + +.tsd-kind-variable > .tsd-kind-icon:before { + background-position: -136px -0px; +} +.tsd-kind-variable.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -0px; +} +.tsd-kind-variable.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-variable.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -0px; +} +.tsd-kind-variable.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -0px; +} +.tsd-kind-variable.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -0px; +} +.tsd-kind-variable.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -0px; +} +.tsd-kind-variable.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-variable.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -0px; +} +.tsd-kind-variable.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -0px; +} +.tsd-kind-variable.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-variable.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -0px; +} +.tsd-kind-variable.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -0px; +} + +.tsd-kind-property > .tsd-kind-icon:before { + background-position: -136px -0px; +} +.tsd-kind-property.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -0px; +} +.tsd-kind-property.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-property.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -0px; +} +.tsd-kind-property.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -0px; +} +.tsd-kind-property.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -0px; +} +.tsd-kind-property.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -0px; +} +.tsd-kind-property.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-property.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -0px; +} +.tsd-kind-property.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -0px; +} +.tsd-kind-property.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -0px; +} +.tsd-kind-property.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -0px; +} +.tsd-kind-property.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -0px; +} + +.tsd-kind-get-signature > .tsd-kind-icon:before { + background-position: -136px -17px; +} +.tsd-kind-get-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -17px; +} +.tsd-kind-get-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -17px; +} +.tsd-kind-get-signature.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -17px; +} + +.tsd-kind-set-signature > .tsd-kind-icon:before { + background-position: -136px -34px; +} +.tsd-kind-set-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -34px; +} +.tsd-kind-set-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -34px; +} +.tsd-kind-set-signature.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -34px; +} + +.tsd-kind-accessor > .tsd-kind-icon:before { + background-position: -136px -51px; +} +.tsd-kind-accessor.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -51px; +} +.tsd-kind-accessor.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -51px; +} +.tsd-kind-accessor.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -51px; +} + +.tsd-kind-function > .tsd-kind-icon:before { + background-position: -136px -68px; +} +.tsd-kind-function.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -68px; +} +.tsd-kind-function.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-function.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -68px; +} +.tsd-kind-function.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -68px; +} +.tsd-kind-function.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -68px; +} +.tsd-kind-function.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -68px; +} +.tsd-kind-function.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-function.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -68px; +} +.tsd-kind-function.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -68px; +} +.tsd-kind-function.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-function.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -68px; +} +.tsd-kind-function.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -68px; +} + +.tsd-kind-method > .tsd-kind-icon:before { + background-position: -136px -68px; +} +.tsd-kind-method.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -68px; +} +.tsd-kind-method.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-method.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -68px; +} +.tsd-kind-method.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -68px; +} +.tsd-kind-method.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -68px; +} +.tsd-kind-method.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -68px; +} +.tsd-kind-method.tsd-parent-kind-class.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-method.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -68px; +} +.tsd-kind-method.tsd-parent-kind-enum.tsd-is-protected > .tsd-kind-icon:before { + background-position: -187px -68px; +} +.tsd-kind-method.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-method.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -68px; +} +.tsd-kind-method.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -68px; +} + +.tsd-kind-call-signature > .tsd-kind-icon:before { + background-position: -136px -68px; +} +.tsd-kind-call-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -68px; +} +.tsd-kind-call-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -68px; +} +.tsd-kind-call-signature.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -68px; +} + +.tsd-kind-function.tsd-has-type-parameter > .tsd-kind-icon:before { + background-position: -136px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -153px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-class + > .tsd-kind-icon:before { + background-position: -51px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-enum + > .tsd-kind-icon:before { + background-position: -170px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-interface + > .tsd-kind-icon:before { + background-position: -204px -85px; +} +.tsd-kind-function.tsd-has-type-parameter.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -85px; +} + +.tsd-kind-method.tsd-has-type-parameter > .tsd-kind-icon:before { + background-position: -136px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -153px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-class + > .tsd-kind-icon:before { + background-position: -51px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-enum + > .tsd-kind-icon:before { + background-position: -170px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-interface + > .tsd-kind-icon:before { + background-position: -204px -85px; +} +.tsd-kind-method.tsd-has-type-parameter.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -85px; +} + +.tsd-kind-constructor > .tsd-kind-icon:before { + background-position: -136px -102px; +} +.tsd-kind-constructor.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -102px; +} +.tsd-kind-constructor.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -102px; +} +.tsd-kind-constructor.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -102px; +} + +.tsd-kind-constructor-signature > .tsd-kind-icon:before { + background-position: -136px -102px; +} +.tsd-kind-constructor-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -102px; +} +.tsd-kind-constructor-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-interface + > .tsd-kind-icon:before { + background-position: -204px -102px; +} +.tsd-kind-constructor-signature.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -102px; +} + +.tsd-kind-index-signature > .tsd-kind-icon:before { + background-position: -136px -119px; +} +.tsd-kind-index-signature.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -119px; +} +.tsd-kind-index-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -119px; +} +.tsd-kind-index-signature.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -119px; +} + +.tsd-kind-event > .tsd-kind-icon:before { + background-position: -136px -136px; +} +.tsd-kind-event.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -136px; +} +.tsd-kind-event.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -136px; +} +.tsd-kind-event.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -136px; +} +.tsd-kind-event.tsd-parent-kind-class.tsd-is-inherited > .tsd-kind-icon:before { + background-position: -68px -136px; +} +.tsd-kind-event.tsd-parent-kind-class.tsd-is-protected > .tsd-kind-icon:before { + background-position: -85px -136px; +} +.tsd-kind-event.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -136px; +} +.tsd-kind-event.tsd-parent-kind-class.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -136px; +} +.tsd-kind-event.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -136px; +} +.tsd-kind-event.tsd-parent-kind-enum.tsd-is-protected > .tsd-kind-icon:before { + background-position: -187px -136px; +} +.tsd-kind-event.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -136px; +} +.tsd-kind-event.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -136px; +} +.tsd-kind-event.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -136px; +} + +.tsd-is-static > .tsd-kind-icon:before { + background-position: -136px -153px; +} +.tsd-is-static.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -153px; +} +.tsd-is-static.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -153px; +} +.tsd-is-static.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -153px; +} +.tsd-is-static.tsd-parent-kind-class.tsd-is-inherited > .tsd-kind-icon:before { + background-position: -68px -153px; +} +.tsd-is-static.tsd-parent-kind-class.tsd-is-protected > .tsd-kind-icon:before { + background-position: -85px -153px; +} +.tsd-is-static.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -153px; +} +.tsd-is-static.tsd-parent-kind-class.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -153px; +} +.tsd-is-static.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -153px; +} +.tsd-is-static.tsd-parent-kind-enum.tsd-is-protected > .tsd-kind-icon:before { + background-position: -187px -153px; +} +.tsd-is-static.tsd-parent-kind-enum.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -153px; +} +.tsd-is-static.tsd-parent-kind-interface > .tsd-kind-icon:before { + background-position: -204px -153px; +} +.tsd-is-static.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -153px; +} + +.tsd-is-static.tsd-kind-function > .tsd-kind-icon:before { + background-position: -136px -170px; +} +.tsd-is-static.tsd-kind-function.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -170px; +} +.tsd-is-static.tsd-kind-function.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -170px; +} +.tsd-is-static.tsd-kind-function.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -170px; +} 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+.tsd-is-static.tsd-kind-function.tsd-parent-kind-interface + > .tsd-kind-icon:before { + background-position: -204px -170px; +} +.tsd-is-static.tsd-kind-function.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -170px; +} + +.tsd-is-static.tsd-kind-method > .tsd-kind-icon:before { + background-position: -136px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-is-protected > .tsd-kind-icon:before { + background-position: -153px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-class > .tsd-kind-icon:before { + background-position: -51px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-class.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-enum > .tsd-kind-icon:before { + background-position: -170px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-enum.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -187px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-enum.tsd-is-private + > .tsd-kind-icon:before { + background-position: -119px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-interface + > .tsd-kind-icon:before { + background-position: -204px -170px; +} +.tsd-is-static.tsd-kind-method.tsd-parent-kind-interface.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -221px -170px; +} + +.tsd-is-static.tsd-kind-call-signature > .tsd-kind-icon:before { + background-position: -136px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -153px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-is-private > .tsd-kind-icon:before { + background-position: -119px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-parent-kind-class + > .tsd-kind-icon:before { + background-position: -51px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -68px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-protected + > .tsd-kind-icon:before { + background-position: -85px -170px; +} +.tsd-is-static.tsd-kind-call-signature.tsd-parent-kind-class.tsd-is-protected.tsd-is-inherited + > .tsd-kind-icon:before { + background-position: -102px -170px; +} 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--color-ts-enum: var(--light-color-ts-enum); + --color-ts-class: var(--light-color-ts-class); + --color-ts-private: var(--light-color-ts-private); + --color-toolbar: var(--light-color-toolbar); + --color-toolbar-text: var(--light-color-toolbar-text); + --icon-filter: var(--light-icon-filter); + --external-icon: var(--light-external-icon); +} + +body.dark { + --color-background: var(--dark-color-background); + --color-secondary-background: var(--dark-color-secondary-background); + --color-text: var(--dark-color-text); + --color-text-aside: var(--dark-color-text-aside); + --color-link: var(--dark-color-link); + --color-menu-divider: var(--dark-color-menu-divider); + --color-menu-divider-focus: var(--dark-color-menu-divider-focus); + --color-menu-label: var(--dark-color-menu-label); + --color-panel: var(--dark-color-panel); + --color-panel-divider: var(--dark-color-panel-divider); + --color-comment-tag: var(--dark-color-comment-tag); + --color-comment-tag-text: var(--dark-color-comment-tag-text); + --color-ts: var(--dark-color-ts); + --color-ts-interface: var(--dark-color-ts-interface); + --color-ts-enum: var(--dark-color-ts-enum); + --color-ts-class: var(--dark-color-ts-class); + --color-ts-private: var(--dark-color-ts-private); + --color-toolbar: var(--dark-color-toolbar); + --color-toolbar-text: var(--dark-color-toolbar-text); + --icon-filter: var(--dark-icon-filter); + --external-icon: var(--dark-external-icon); +} + +h1, +h2, +h3, +h4, +h5, +h6 { + line-height: 1.2; +} + +h1 { + font-size: 2em; + margin: 0.67em 0; +} + +h2 { + font-size: 1.5em; + margin: 0.83em 0; +} + +h3 { + font-size: 1.17em; + margin: 1em 0; +} + +h4, +.tsd-index-panel h3 { + font-size: 1em; + margin: 1.33em 0; +} + +h5 { + font-size: 0.83em; + margin: 1.67em 0; +} + +h6 { + font-size: 0.67em; + margin: 2.33em 0; +} + +pre { + white-space: pre; + white-space: pre-wrap; + word-wrap: break-word; +} + +dl, +menu, +ol, +ul { + margin: 1em 0; +} + +dd { + margin: 0 0 0 40px; +} + +.container { + max-width: 1200px; + margin: 0 auto; + padding: 0 40px; +} +@media (max-width: 640px) { + .container { + padding: 0 20px; + } +} + +.container-main { + padding-bottom: 200px; +} + +.row { + display: flex; + position: relative; + margin: 0 -10px; +} +.row:after { + visibility: hidden; + display: block; + content: ""; + clear: both; + height: 0; +} + +.col-4, +.col-8 { + box-sizing: border-box; + float: left; + padding: 0 10px; +} + +.col-4 { + width: 33.3333333333%; +} +.col-8 { + width: 66.6666666667%; +} + +ul.tsd-descriptions > li > :first-child, +.tsd-panel > :first-child, +.col-8 > :first-child, +.col-4 > :first-child, +ul.tsd-descriptions > li > :first-child > :first-child, +.tsd-panel > :first-child > :first-child, +.col-8 > :first-child > :first-child, +.col-4 > :first-child > :first-child, +ul.tsd-descriptions > li > :first-child > :first-child > :first-child, +.tsd-panel > :first-child > :first-child > :first-child, +.col-8 > :first-child > :first-child > :first-child, +.col-4 > :first-child > :first-child > :first-child { + margin-top: 0; +} +ul.tsd-descriptions > li > :last-child, +.tsd-panel > :last-child, +.col-8 > :last-child, +.col-4 > :last-child, +ul.tsd-descriptions > li > :last-child > :last-child, +.tsd-panel > :last-child > :last-child, +.col-8 > :last-child > :last-child, +.col-4 > :last-child > :last-child, +ul.tsd-descriptions > li > :last-child > :last-child > :last-child, +.tsd-panel > :last-child > :last-child > :last-child, +.col-8 > :last-child > :last-child > :last-child, +.col-4 > :last-child > :last-child > :last-child { + margin-bottom: 0; +} + +@keyframes fade-in { + from { + opacity: 0; + } + to { + opacity: 1; + } +} +@keyframes fade-out { + from { + opacity: 1; + visibility: visible; + } + to { + opacity: 0; + } +} +@keyframes fade-in-delayed { + 0% { + opacity: 0; + } + 33% { + opacity: 0; + } + 100% { + opacity: 1; + } +} +@keyframes fade-out-delayed { + 0% { + opacity: 1; + visibility: visible; + } + 66% { + opacity: 0; + } + 100% { + opacity: 0; + } +} +@keyframes shift-to-left { + from { + transform: translate(0, 0); + } + to { + transform: translate(-25%, 0); + } +} +@keyframes unshift-to-left { + from { + transform: translate(-25%, 0); + } + to { + transform: translate(0, 0); + } +} +@keyframes pop-in-from-right { + from { + transform: translate(100%, 0); + } + to { + transform: translate(0, 0); + } +} +@keyframes pop-out-to-right { + from { + transform: translate(0, 0); + visibility: visible; + } + to { + transform: translate(100%, 0); + } +} +body { + background: var(--color-background); + font-family: "Segoe UI", sans-serif; + font-size: 16px; + color: var(--color-text); +} + +a { + color: var(--color-link); + text-decoration: none; +} +a:hover { + text-decoration: underline; +} +a.external[target="_blank"] { + background-image: var(--external-icon); + background-position: top 3px right; + background-repeat: no-repeat; + padding-right: 13px; +} + +code, +pre { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + padding: 0.2em; + margin: 0; + font-size: 14px; +} + +pre { + padding: 10px; +} +pre code { + padding: 0; + font-size: 100%; +} + +blockquote { + margin: 1em 0; + padding-left: 1em; + border-left: 4px solid gray; +} + +.tsd-typography { + line-height: 1.333em; +} +.tsd-typography ul { + list-style: square; + padding: 0 0 0 20px; + margin: 0; +} +.tsd-typography h4, +.tsd-typography .tsd-index-panel h3, +.tsd-index-panel .tsd-typography h3, +.tsd-typography h5, +.tsd-typography h6 { + font-size: 1em; + margin: 0; +} +.tsd-typography h5, +.tsd-typography h6 { + font-weight: normal; +} +.tsd-typography p, +.tsd-typography ul, +.tsd-typography ol { + margin: 1em 0; +} + +@media (min-width: 901px) and (max-width: 1024px) { + html .col-content { + width: 72%; + } + html .col-menu { + width: 28%; + } + html .tsd-navigation { + padding-left: 10px; + } +} +@media (max-width: 900px) { + html .col-content { + float: none; + width: 100%; + } + html .col-menu { + position: fixed !important; + overflow: auto; + -webkit-overflow-scrolling: touch; + z-index: 1024; + top: 0 !important; + bottom: 0 !important; + left: auto !important; + right: 0 !important; + width: 100%; + padding: 20px 20px 0 0; + max-width: 450px; + visibility: hidden; + background-color: var(--color-panel); + transform: translate(100%, 0); + } + html .col-menu > *:last-child { + padding-bottom: 20px; + } + html .overlay { + content: ""; + display: block; + position: fixed; + z-index: 1023; + top: 0; + left: 0; + right: 0; + bottom: 0; + background-color: rgba(0, 0, 0, 0.75); + visibility: hidden; + } + + .to-has-menu .overlay { + animation: fade-in 0.4s; + } + + .to-has-menu :is(header, footer, .col-content) { + animation: shift-to-left 0.4s; + } + + .to-has-menu .col-menu { + animation: pop-in-from-right 0.4s; + } + + .from-has-menu .overlay { + animation: fade-out 0.4s; + } + + .from-has-menu :is(header, footer, .col-content) { + animation: unshift-to-left 0.4s; + } + + .from-has-menu .col-menu { + animation: pop-out-to-right 0.4s; + } + + .has-menu body { + overflow: hidden; + } + .has-menu .overlay { + visibility: visible; + } + .has-menu :is(header, footer, .col-content) { + transform: translate(-25%, 0); + } + .has-menu .col-menu { + visibility: visible; + transform: translate(0, 0); + display: grid; + grid-template-rows: auto 1fr; + max-height: 100vh; + } + .has-menu .tsd-navigation { + max-height: 100%; + } +} + +.tsd-page-title { + padding: 70px 0 20px 0; + margin: 0 0 40px 0; + background: var(--color-panel); + box-shadow: 0 0 5px rgba(0, 0, 0, 0.35); +} +.tsd-page-title h1 { + margin: 0; +} + +.tsd-breadcrumb { + margin: 0; + padding: 0; + color: var(--color-text-aside); +} +.tsd-breadcrumb a { + color: var(--color-text-aside); + text-decoration: none; +} +.tsd-breadcrumb a:hover { + text-decoration: underline; +} +.tsd-breadcrumb li { + display: inline; +} +.tsd-breadcrumb li:after { + content: " / "; +} + +dl.tsd-comment-tags { + overflow: hidden; +} +dl.tsd-comment-tags dt { + float: left; + padding: 1px 5px; + margin: 0 10px 0 0; + border-radius: 4px; + border: 1px solid var(--color-comment-tag); + color: var(--color-comment-tag); + font-size: 0.8em; + font-weight: normal; +} +dl.tsd-comment-tags dd { + margin: 0 0 10px 0; +} +dl.tsd-comment-tags dd:before, +dl.tsd-comment-tags dd:after { + display: table; + content: " "; +} +dl.tsd-comment-tags dd pre, +dl.tsd-comment-tags dd:after { + clear: both; +} +dl.tsd-comment-tags p { + margin: 0; +} + +.tsd-panel.tsd-comment .lead { + font-size: 1.1em; + line-height: 1.333em; + margin-bottom: 2em; +} +.tsd-panel.tsd-comment .lead:last-child { + margin-bottom: 0; +} + +.toggle-protected .tsd-is-private { + display: none; +} + +.toggle-public .tsd-is-private, +.toggle-public .tsd-is-protected, +.toggle-public .tsd-is-private-protected { + display: none; +} + +.toggle-inherited .tsd-is-inherited { + display: none; +} + +.toggle-externals .tsd-is-external { + display: none; +} + +#tsd-filter { + position: relative; + display: inline-block; + height: 40px; + vertical-align: bottom; +} +.no-filter #tsd-filter { + display: none; +} +#tsd-filter .tsd-filter-group { + display: inline-block; + height: 40px; + vertical-align: bottom; + white-space: nowrap; +} +#tsd-filter input { + display: none; +} +@media (max-width: 900px) { + #tsd-filter .tsd-filter-group { + display: block; + position: absolute; + top: 40px; + right: 20px; + height: auto; + background-color: var(--color-panel); + visibility: hidden; + transform: translate(50%, 0); + box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); + } + .has-options #tsd-filter .tsd-filter-group { + visibility: visible; + } + .to-has-options #tsd-filter .tsd-filter-group { + animation: fade-in 0.2s; + } + .from-has-options #tsd-filter .tsd-filter-group { + animation: fade-out 0.2s; + } + #tsd-filter label, + #tsd-filter .tsd-select { + display: block; + padding-right: 20px; + } +} + +footer { + border-top: 1px solid var(--color-panel-divider); + background-color: var(--color-panel); +} +footer:after { + content: ""; + display: table; +} +footer.with-border-bottom { + border-bottom: 1px solid var(--color-panel-divider); +} +footer .tsd-legend-group { + font-size: 0; +} +footer .tsd-legend { + display: inline-block; + width: 25%; + padding: 0; + font-size: 16px; + list-style: none; + line-height: 1.333em; + vertical-align: top; +} +@media (max-width: 900px) { + footer .tsd-legend { + width: 50%; + } +} + +.tsd-hierarchy { + list-style: square; + padding: 0 0 0 20px; + margin: 0; +} +.tsd-hierarchy .target { + font-weight: bold; +} + +.tsd-index-panel .tsd-index-content { + margin-bottom: -30px !important; +} +.tsd-index-panel .tsd-index-section { + margin-bottom: 30px !important; +} +.tsd-index-panel h3 { + margin: 0 -20px 10px -20px; + padding: 0 20px 10px 20px; + border-bottom: 1px solid var(--color-panel-divider); +} +.tsd-index-panel ul.tsd-index-list { + -webkit-column-count: 3; + -moz-column-count: 3; + -ms-column-count: 3; + -o-column-count: 3; + column-count: 3; + -webkit-column-gap: 20px; + -moz-column-gap: 20px; + -ms-column-gap: 20px; + -o-column-gap: 20px; + column-gap: 20px; + padding: 0; + list-style: none; + line-height: 1.333em; +} +@media (max-width: 900px) { + .tsd-index-panel ul.tsd-index-list { + -webkit-column-count: 1; + -moz-column-count: 1; + -ms-column-count: 1; + -o-column-count: 1; + column-count: 1; + } +} +@media (min-width: 901px) and (max-width: 1024px) { + .tsd-index-panel ul.tsd-index-list { + -webkit-column-count: 2; + -moz-column-count: 2; + -ms-column-count: 2; + -o-column-count: 2; + column-count: 2; + } +} +.tsd-index-panel ul.tsd-index-list li { + -webkit-page-break-inside: avoid; + -moz-page-break-inside: avoid; + -ms-page-break-inside: avoid; + -o-page-break-inside: avoid; + page-break-inside: avoid; +} +.tsd-index-panel a, +.tsd-index-panel .tsd-parent-kind-module a { + color: var(--color-ts); +} +.tsd-index-panel .tsd-parent-kind-interface a { + color: var(--color-ts-interface); +} +.tsd-index-panel .tsd-parent-kind-enum a { + color: var(--color-ts-enum); +} +.tsd-index-panel .tsd-parent-kind-class a { + color: var(--color-ts-class); +} +.tsd-index-panel .tsd-kind-module a { + color: var(--color-ts); +} +.tsd-index-panel .tsd-kind-interface a { + color: var(--color-ts-interface); +} +.tsd-index-panel .tsd-kind-enum a { + color: var(--color-ts-enum); +} +.tsd-index-panel .tsd-kind-class a { + color: var(--color-ts-class); +} +.tsd-index-panel .tsd-is-private a { + color: var(--color-ts-private); +} + +.tsd-flag { + display: inline-block; + padding: 0.25em 0.4em; + border-radius: 4px; + color: var(--color-comment-tag-text); + background-color: var(--color-comment-tag); + text-indent: 0; + font-size: 75%; + line-height: 1; + font-weight: normal; +} + +.tsd-anchor { + position: absolute; + top: -100px; +} + +.tsd-member { + position: relative; +} +.tsd-member .tsd-anchor + h3 { + margin-top: 0; + margin-bottom: 0; + border-bottom: none; +} +.tsd-member [data-tsd-kind] { + color: var(--color-ts); +} +.tsd-member [data-tsd-kind="Interface"] { + color: var(--color-ts-interface); +} +.tsd-member [data-tsd-kind="Enum"] { + color: var(--color-ts-enum); +} +.tsd-member [data-tsd-kind="Class"] { + color: var(--color-ts-class); +} +.tsd-member [data-tsd-kind="Private"] { + color: var(--color-ts-private); +} + +.tsd-navigation { + margin: 0 0 0 40px; +} +.tsd-navigation a { + display: block; + padding-top: 2px; + padding-bottom: 2px; + border-left: 2px solid transparent; + color: var(--color-text); + text-decoration: none; + transition: border-left-color 0.1s; +} +.tsd-navigation a:hover { + text-decoration: underline; +} +.tsd-navigation ul { + margin: 0; + padding: 0; + list-style: none; +} +.tsd-navigation li { + padding: 0; +} + +.tsd-navigation.primary { + padding-bottom: 40px; +} +.tsd-navigation.primary a { + display: block; + padding-top: 6px; + padding-bottom: 6px; +} +.tsd-navigation.primary ul li a { + padding-left: 5px; +} +.tsd-navigation.primary ul li li a { + padding-left: 25px; +} +.tsd-navigation.primary ul li li li a { + padding-left: 45px; +} +.tsd-navigation.primary ul li li li li a { + padding-left: 65px; +} +.tsd-navigation.primary ul li li li li li a { + padding-left: 85px; +} +.tsd-navigation.primary ul li li li li li li a { + padding-left: 105px; +} +.tsd-navigation.primary > ul { + border-bottom: 1px solid var(--color-panel-divider); +} +.tsd-navigation.primary li { + border-top: 1px solid var(--color-panel-divider); +} +.tsd-navigation.primary li.current > a { + font-weight: bold; +} +.tsd-navigation.primary li.label span { + display: block; + padding: 20px 0 6px 5px; + color: var(--color-menu-label); +} +.tsd-navigation.primary li.globals + li > span, +.tsd-navigation.primary li.globals + li > a { + padding-top: 20px; +} + +.tsd-navigation.secondary { + max-height: calc(100vh - 1rem - 40px); + overflow: auto; + position: sticky; + top: calc(0.5rem + 40px); + transition: 0.3s; +} +.tsd-navigation.secondary.tsd-navigation--toolbar-hide { + max-height: calc(100vh - 1rem); + top: 0.5rem; +} +.tsd-navigation.secondary ul { + transition: opacity 0.2s; +} +.tsd-navigation.secondary ul li a { + padding-left: 25px; +} +.tsd-navigation.secondary ul li li a { + padding-left: 45px; +} +.tsd-navigation.secondary ul li li li a { + padding-left: 65px; +} +.tsd-navigation.secondary ul li li li li a { + padding-left: 85px; +} +.tsd-navigation.secondary ul li li li li li a { + padding-left: 105px; +} +.tsd-navigation.secondary ul li li li li li li a { + padding-left: 125px; +} +.tsd-navigation.secondary ul.current a { + border-left-color: var(--color-panel-divider); +} +.tsd-navigation.secondary li.focus > a, +.tsd-navigation.secondary ul.current li.focus > a { + border-left-color: var(--color-menu-divider-focus); +} +.tsd-navigation.secondary li.current { + margin-top: 20px; + margin-bottom: 20px; + border-left-color: var(--color-panel-divider); +} +.tsd-navigation.secondary li.current > a { + font-weight: bold; +} + +@media (min-width: 901px) { + .menu-sticky-wrap { + position: static; + } +} + +.tsd-panel { + margin: 20px 0; + padding: 20px; + background-color: var(--color-panel); + box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); +} +.tsd-panel:empty { + display: none; +} +.tsd-panel > h1, +.tsd-panel > h2, +.tsd-panel > h3 { + margin: 1.5em -20px 10px -20px; + padding: 0 20px 10px 20px; + border-bottom: 1px solid var(--color-panel-divider); +} +.tsd-panel > h1.tsd-before-signature, +.tsd-panel > h2.tsd-before-signature, +.tsd-panel > h3.tsd-before-signature { + margin-bottom: 0; + border-bottom: 0; +} +.tsd-panel table { + display: block; + width: 100%; + overflow: auto; + margin-top: 10px; + word-break: normal; + word-break: keep-all; + border-collapse: collapse; +} +.tsd-panel table th { + font-weight: bold; +} +.tsd-panel table th, +.tsd-panel table td { + padding: 6px 13px; + border: 1px solid var(--color-panel-divider); +} +.tsd-panel table tr { + background: var(--color-background); +} +.tsd-panel table tr:nth-child(even) { + background: var(--color-secondary-background); +} + +.tsd-panel-group { + margin: 60px 0; +} +.tsd-panel-group > h1, +.tsd-panel-group > h2, +.tsd-panel-group > h3 { + padding-left: 20px; + padding-right: 20px; +} + +#tsd-search { + transition: background-color 0.2s; +} +#tsd-search .title { + position: relative; + z-index: 2; +} +#tsd-search .field { + position: absolute; + left: 0; + top: 0; + right: 40px; + height: 40px; +} +#tsd-search .field input { + box-sizing: border-box; + position: relative; + top: -50px; + z-index: 1; + width: 100%; + padding: 0 10px; + opacity: 0; + outline: 0; + border: 0; + background: transparent; + color: var(--color-text); +} +#tsd-search .field label { + position: absolute; + overflow: hidden; + right: -40px; +} +#tsd-search .field input, +#tsd-search .title { + transition: opacity 0.2s; +} +#tsd-search .results { + position: absolute; + visibility: hidden; + top: 40px; + width: 100%; + margin: 0; + padding: 0; + list-style: none; + box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); +} +#tsd-search .results li { + padding: 0 10px; + background-color: var(--color-background); +} +#tsd-search .results li:nth-child(even) { + background-color: var(--color-panel); +} +#tsd-search .results li.state { + display: none; +} +#tsd-search .results li.current, +#tsd-search .results li:hover { + background-color: var(--color-panel-divider); +} +#tsd-search .results a { + display: block; +} +#tsd-search .results a:before { + top: 10px; +} +#tsd-search .results span.parent { + color: var(--color-text-aside); + font-weight: normal; +} +#tsd-search.has-focus { + background-color: var(--color-panel-divider); +} +#tsd-search.has-focus .field input { + top: 0; + opacity: 1; +} +#tsd-search.has-focus .title { + z-index: 0; + opacity: 0; +} +#tsd-search.has-focus .results { + visibility: visible; +} +#tsd-search.loading .results li.state.loading { + display: block; +} +#tsd-search.failure .results li.state.failure { + display: block; +} + +.tsd-signature { + margin: 0 0 1em 0; + padding: 10px; + border: 1px solid var(--color-panel-divider); + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 14px; + overflow-x: auto; +} +.tsd-signature.tsd-kind-icon { + padding-left: 30px; +} +.tsd-signature.tsd-kind-icon:before { + top: 10px; + left: 10px; +} +.tsd-panel > .tsd-signature { + margin-left: -20px; + margin-right: -20px; + border-width: 1px 0; +} +.tsd-panel > .tsd-signature.tsd-kind-icon { + padding-left: 40px; +} +.tsd-panel > .tsd-signature.tsd-kind-icon:before { + left: 20px; +} + +.tsd-signature-symbol { + color: var(--color-text-aside); + font-weight: normal; +} + +.tsd-signature-type { + font-style: italic; + font-weight: normal; +} + +.tsd-signatures { + padding: 0; + margin: 0 0 1em 0; + border: 1px solid var(--color-panel-divider); +} +.tsd-signatures .tsd-signature { + margin: 0; + border-width: 1px 0 0 0; + transition: background-color 0.1s; +} +.tsd-signatures .tsd-signature:first-child { + border-top-width: 0; +} +.tsd-signatures .tsd-signature.current { + background-color: var(--color-panel-divider); +} +.tsd-signatures.active > .tsd-signature { + cursor: pointer; +} +.tsd-panel > .tsd-signatures { + margin-left: -20px; + margin-right: -20px; + border-width: 1px 0; +} +.tsd-panel > .tsd-signatures .tsd-signature.tsd-kind-icon { + padding-left: 40px; +} +.tsd-panel > .tsd-signatures .tsd-signature.tsd-kind-icon:before { + left: 20px; +} +.tsd-panel > a.anchor + .tsd-signatures { + border-top-width: 0; + margin-top: -20px; +} + +ul.tsd-descriptions { + position: relative; + overflow: hidden; + padding: 0; + list-style: none; +} +ul.tsd-descriptions.active > .tsd-description { + display: none; +} +ul.tsd-descriptions.active > .tsd-description.current { + display: block; +} +ul.tsd-descriptions.active > .tsd-description.fade-in { + animation: fade-in-delayed 0.3s; +} +ul.tsd-descriptions.active > .tsd-description.fade-out { + animation: fade-out-delayed 0.3s; + position: absolute; + display: block; + top: 0; + left: 0; + right: 0; + opacity: 0; + visibility: hidden; +} +ul.tsd-descriptions h4, +ul.tsd-descriptions .tsd-index-panel h3, +.tsd-index-panel ul.tsd-descriptions h3 { + font-size: 16px; + margin: 1em 0 0.5em 0; +} + +ul.tsd-parameters, +ul.tsd-type-parameters { + list-style: square; + margin: 0; + padding-left: 20px; +} +ul.tsd-parameters > li.tsd-parameter-signature, +ul.tsd-type-parameters > li.tsd-parameter-signature { + list-style: none; + margin-left: -20px; +} +ul.tsd-parameters h5, +ul.tsd-type-parameters h5 { + font-size: 16px; + margin: 1em 0 0.5em 0; +} +ul.tsd-parameters .tsd-comment, +ul.tsd-type-parameters .tsd-comment { + margin-top: -0.5em; +} + +.tsd-sources { + font-size: 14px; + color: var(--color-text-aside); + margin: 0 0 1em 0; +} +.tsd-sources a { + color: var(--color-text-aside); + text-decoration: underline; +} +.tsd-sources ul, +.tsd-sources p { + margin: 0 !important; +} +.tsd-sources ul { + list-style: none; + padding: 0; +} + +.tsd-page-toolbar { + position: fixed; + z-index: 1; + top: 0; + left: 0; + width: 100%; + height: 40px; + color: var(--color-toolbar-text); + background: var(--color-toolbar); + border-bottom: 1px solid var(--color-panel-divider); + transition: transform 0.3s linear; +} +.tsd-page-toolbar a { + color: var(--color-toolbar-text); + text-decoration: none; +} +.tsd-page-toolbar a.title { + font-weight: bold; +} +.tsd-page-toolbar a.title:hover { + text-decoration: underline; +} +.tsd-page-toolbar .table-wrap { + display: table; + width: 100%; + height: 40px; +} +.tsd-page-toolbar .table-cell { + display: table-cell; + position: relative; + white-space: nowrap; + line-height: 40px; +} +.tsd-page-toolbar .table-cell:first-child { + width: 100%; +} + +.tsd-page-toolbar--hide { + transform: translateY(-100%); +} + +.tsd-select .tsd-select-list li:before, +.tsd-select .tsd-select-label:before, +.tsd-widget:before { + content: ""; + display: inline-block; + width: 40px; + height: 40px; + margin: 0 -8px 0 0; + background-image: url(./widgets.png); + background-repeat: no-repeat; + text-indent: -1024px; + vertical-align: bottom; + filter: var(--icon-filter); +} +@media (-webkit-min-device-pixel-ratio: 1.5), (min-resolution: 144dpi) { + .tsd-select .tsd-select-list li:before, + .tsd-select .tsd-select-label:before, + .tsd-widget:before { + background-image: url(./widgets@2x.png); + background-size: 320px 40px; + } +} + +.tsd-widget { + display: inline-block; + overflow: hidden; + opacity: 0.8; + height: 40px; + transition: opacity 0.1s, background-color 0.2s; + vertical-align: bottom; + cursor: pointer; +} +.tsd-widget:hover { + opacity: 0.9; +} +.tsd-widget.active { + opacity: 1; + background-color: var(--color-panel-divider); +} +.tsd-widget.no-caption { + width: 40px; +} +.tsd-widget.no-caption:before { + margin: 0; +} +.tsd-widget.search:before { + background-position: 0 0; +} +.tsd-widget.menu:before { + background-position: -40px 0; +} +.tsd-widget.options:before { + background-position: -80px 0; +} +.tsd-widget.options, +.tsd-widget.menu { + display: none; +} +@media (max-width: 900px) { + .tsd-widget.options, + .tsd-widget.menu { + display: inline-block; + } +} +input[type="checkbox"] + .tsd-widget:before { + background-position: -120px 0; +} +input[type="checkbox"]:checked + .tsd-widget:before { + background-position: -160px 0; +} + +.tsd-select { + position: relative; + display: inline-block; + height: 40px; + transition: opacity 0.1s, background-color 0.2s; + vertical-align: bottom; + cursor: pointer; +} +.tsd-select .tsd-select-label { + opacity: 0.6; + transition: opacity 0.2s; +} +.tsd-select .tsd-select-label:before { + background-position: -240px 0; +} +.tsd-select.active .tsd-select-label { + opacity: 0.8; +} +.tsd-select.active .tsd-select-list { + visibility: visible; + opacity: 1; + transition-delay: 0s; +} +.tsd-select .tsd-select-list { + position: absolute; + visibility: hidden; + top: 40px; + left: 0; + margin: 0; + padding: 0; + opacity: 0; + list-style: none; + box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); + transition: visibility 0s 0.2s, opacity 0.2s; +} +.tsd-select .tsd-select-list li { + padding: 0 20px 0 0; + background-color: var(--color-background); +} +.tsd-select .tsd-select-list li:before { + background-position: 40px 0; +} +.tsd-select .tsd-select-list li:nth-child(even) { + background-color: var(--color-panel); +} +.tsd-select .tsd-select-list li:hover { + background-color: var(--color-panel-divider); +} +.tsd-select .tsd-select-list li.selected:before { + background-position: -200px 0; +} +@media (max-width: 900px) { + .tsd-select .tsd-select-list { + top: 0; + left: auto; + right: 100%; + margin-right: -5px; + } + .tsd-select .tsd-select-label:before { + background-position: -280px 0; + } +} + +img { + max-width: 100%; +} + +.tsd-anchor-icon { + margin-left: 10px; + vertical-align: middle; + color: var(--color-text); +} + +.tsd-anchor-icon svg { + width: 1em; + height: 1em; + visibility: hidden; +} + +.tsd-anchor-link:hover > .tsd-anchor-icon svg { + visibility: visible; +} diff --git a/docs/assets/widgets.png b/docs/assets/widgets.png new file mode 100644 index 0000000000000000000000000000000000000000..c7380532ac1b45400620011c37c4dcb7aec27a4c GIT binary patch literal 480 zcmeAS@N?(olHy`uVBq!ia0y~yU~~YoH8@y+q^jrZML>b&o-U3d6^w6h1+IPUz|;DW zIZ;96kdsD>Qv^q=09&hp0GpEni<1IR%gvP3v%OR9*{MuRTKWHZyIbuBt)Ci`cU_&% z1T+i^Y)o{%281-<3TpPAUTzw5v;RY=>1rvxmPl96#kYc9hX!6V^nB|ad#(S+)}?8C zr_H+lT3B#So$T=?$(w3-{rbQ4R<@nsf$}$hwSO)A$8&`(j+wQf=Jwhb0`CvhR5DCf z^OgI)KQemrUFPH+UynC$Y~QHG%DbTVh-Skz{enNU)cV_hPu~{TD7TPZl>0&K>iuE| z7AYn$7)Jrb9GE&SfQW4q&G*@N|4cHI`VakFa5-C!ov&XD)J(qp$rJJ*9e z-sHv}#g*T7Cv048d1v~BEAzM5FztAse#q78WWC^BUCzQ U&wLp6h6BX&boFyt=akR{0G%$)mH+?% literal 0 HcmV?d00001 diff --git a/docs/assets/widgets@2x.png 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b/docs/classes/index.CPMClusteringAlgorithm.html new file mode 100644 index 0000000..824cde1 --- /dev/null +++ b/docs/classes/index.CPMClusteringAlgorithm.html @@ -0,0 +1,82 @@ +CPMClusteringAlgorithm | networkanalysis-ts
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Class CPMClusteringAlgorithm Abstract

+

Abstract base class for clustering algorithms that use the CPM quality +function.

+

Hierarchy

Implements

Index

Properties

Constructors

Methods

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Constructors

constructor

Methods

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void
  • +

    Initializes a CPM clustering algorithm with a specified resolution +parameter.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +

    Returns void

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster contains at least a certain minimum number of nodes.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minNNodesPerCluster: number
      +

      Minimum number of nodes per cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

Abstract clone

Abstract findClustering

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\ No newline at end of file diff --git a/docs/classes/index.Clustering.html b/docs/classes/index.Clustering.html new file mode 100644 index 0000000..f40a6d5 --- /dev/null +++ b/docs/classes/index.Clustering.html @@ -0,0 +1,72 @@ +Clustering | networkanalysis-ts
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Class Clustering

+

Clustering of the nodes in a network.

+

Each node belongs to exactly one cluster.

+

Hierarchy

  • Clustering

Index

Properties

Constructors

Methods

Properties

clusters

clusters: number[]
+

Cluster of each node.

+

nClusters

nClusters: number
+

Number of clusters.

+

Protected nNodes

nNodes: number
+

Number of nodes.

+

Constructors

constructor

Methods

initializeBasedOnNNodes

  • initializeBasedOnNNodes(nNodes: number): void
  • +

    Initializes a singleton clustering for a specified number of nodes.

    +

    Parameters

    • nNodes: number
      +

      Number of nodes

      +

    Returns void

initializeBasedOnClusters

  • initializeBasedOnClusters(clusters: number[]): void
  • +

    Initializes a clustering using a specified cluster for each node.

    +

    Parameters

    • clusters: number[]
      +

      Cluster of each node

      +

    Returns void

clone

getNNodes

  • getNNodes(): number
  • +

    Returns the number of nodes.

    +

    Returns number

    Number of nodes

    +

getNClusters

  • getNClusters(): number
  • +

    Returns the number of clusters.

    +

    Returns number

    Number of clusters

    +

getClusters

  • getClusters(): number[]
  • +

    Returns the cluster of each node.

    +

    Returns number[]

    Cluster of each node

    +

getCluster

  • getCluster(node: number): number
  • +

    Returns the cluster of a node.

    +

    Parameters

    • node: number
      +

      Node

      +

    Returns number

    Cluster

    +

getNNodesPerCluster

  • getNNodesPerCluster(): number[]
  • +

    Returns the number of nodes per cluster.

    +

    Returns number[]

    Number of nodes per cluster

    +

getNodesPerCluster

  • getNodesPerCluster(): number[][]
  • +

    Returns a list of nodes per cluster.

    +

    Returns number[][]

    List of nodes per cluster

    +

setCluster

  • setCluster(node: number, cluster: number): void
  • +

    Assigns a node to a cluster.

    +

    Parameters

    • node: number
      +

      Node

      +
    • cluster: number
      +

      Cluster

      +

    Returns void

initSingletonClusters

  • initSingletonClusters(): void
  • +

    Initializes a singleton clustering.

    +

    Each node i is assigned to a cluster i.

    +

    Returns void

removeEmptyClusters

  • removeEmptyClusters(): void
  • +

    Removes empty clusters.

    +

    Clusters are relabeled to follow a strictly consecutive numbering +0, ..., nClusters - 1.

    +

    Returns void

orderClustersByNNodes

  • orderClustersByNNodes(): void

orderClustersByWeight

  • orderClustersByWeight(nodeWeights: number[]): void
  • +

    Orders the clusters in decreasing order of their total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +
    see

    orderClustersByNNodes

    +

    Parameters

    • nodeWeights: number[]
      +

      Node weights

      +

    Returns void

mergeClusters

  • +

    Merges the clusters based on a clustering of the clusters.

    +

    Parameters

    • clustering: Clustering
      +

      Clustering of the clusters

      +

    Returns void

Private initSingletonClustersHelper

  • initSingletonClustersHelper(): void

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\ No newline at end of file diff --git a/docs/classes/index.ComponentsAlgorithm.html b/docs/classes/index.ComponentsAlgorithm.html new file mode 100644 index 0000000..d39f38a --- /dev/null +++ b/docs/classes/index.ComponentsAlgorithm.html @@ -0,0 +1,8 @@ +ComponentsAlgorithm | networkanalysis-ts
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Class ComponentsAlgorithm

+

Algorithm for finding the connected components of a network.

+

Hierarchy

  • ComponentsAlgorithm

Implements

Index

Constructors

Methods

Constructors

constructor

Methods

findClustering

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\ No newline at end of file diff --git a/docs/classes/index.FastLocalMovingAlgorithm.html b/docs/classes/index.FastLocalMovingAlgorithm.html new file mode 100644 index 0000000..1fc07be --- /dev/null +++ b/docs/classes/index.FastLocalMovingAlgorithm.html @@ -0,0 +1,153 @@ +FastLocalMovingAlgorithm | networkanalysis-ts
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Class FastLocalMovingAlgorithm

+

Fast local moving algorithm.

+

The fast local moving algorithm first adds all nodes in a network to a +queue. It then removes a node from the queue. The node is moved to the +cluster that results in the largest increase in the quality function. If the +current cluster assignment of the node is already optimal, the node is not +moved. If the node is moved to a different cluster, the neighbors of the +node that do not belong to the node's new cluster and that are not yet in +the queue are added to the queue. The algorithm continues removing nodes +from the queue until the queue is empty.

+

The fast local moving algorithm provides a fast variant of the StandardLocalMovingAlgorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Static Readonly DEFAULT_N_ITERATIONS

DEFAULT_N_ITERATIONS: number = 1
+

Default number of iterations.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected random

random: Random
+

Random number generator.

+

Protected nIterations

nIterations: number
+

Number of iterations.

+

Methods

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnResolutionAndNIterationsAndRandom

  • initializeBasedOnResolutionAndNIterationsAndRandom(resolution: number, nIterations: number, random: Random): void
  • +

    Initializes a fast local merging algorithm for a specified resolution +parameter and number of iterations.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

clone

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

initializeBasedOnResolutionAndNIterations

  • initializeBasedOnResolutionAndNIterations(resolution: number, nIterations: number): void

getNIterations

  • getNIterations(): number

setNIterations

  • setNIterations(nIterations: number): void

improveClustering

Protected improveClusteringOneIteration

  • +

    Improves a clustering by performing one iteration of the fast local +moving algorithm.

    +

    The fast local moving algorithm first adds all nodes in a network to a +queue. It then removes a node from the queue. The node is moved to the +cluster that results in the largest increase in the quality function. If +the current cluster assignment of the node is already optimal, the node +is not moved. If the node is moved to a different cluster, the neighbors +of the node that do not belong to the node's new cluster and that are +not yet in the queue are added to the queue. The algorithm continues +removing nodes from the queue until the queue is empty.

    +

    Parameters

    Returns boolean

    Boolean indicating whether the clustering has been improved

    +

Constructors

constructor

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Class GradientDescentVOSLayoutAlgorithm

+

Gradient descent VOS layout algorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_ATTRACTION

DEFAULT_ATTRACTION: number = 2
+

Default attraction parameter.

+

Static Readonly DEFAULT_REPULSION

DEFAULT_REPULSION: number = 1
+

Default repulsion parameter.

+

Static Readonly DEFAULT_EDGE_WEIGHT_INCREMENT

DEFAULT_EDGE_WEIGHT_INCREMENT: number = 0
+

Default edge weight increment parameter.

+

Static Readonly DEFAULT_MAX_N_ITERATIONS

DEFAULT_MAX_N_ITERATIONS: number = 1000
+

Default maximum number of iterations.

+

Static Readonly DEFAULT_INITIAL_STEP_SIZE

DEFAULT_INITIAL_STEP_SIZE: number = 1
+

Default initial step size.

+

Static Readonly DEFAULT_MIN_STEP_SIZE

DEFAULT_MIN_STEP_SIZE: number = 0.001
+

Default minimum step size.

+

Static Readonly DEFAULT_STEP_SIZE_REDUCTION

DEFAULT_STEP_SIZE_REDUCTION: number = 0.75
+

Default step size reduction.

+

Static Readonly DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS

DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS: number = 5
+

Default required number of quality value improvements.

+

Protected attraction

attraction: number
+

Attraction parameter.

+

Protected repulsion

repulsion: number
+

Repulsion parameter.

+

Protected edgeWeightIncrement

edgeWeightIncrement: number
+

Edge weight increment parameter.

+

Protected maxNIterations

maxNIterations: number
+

Maximum number of iterations.

+

Protected initialStepSize

initialStepSize: number
+

Initial step size.

+

Protected minStepSize

minStepSize: number
+

Minimum step size.

+

Protected stepSizeReduction

stepSizeReduction: number
+

Step size reduction.

+

Protected requiredNQualityValueImprovements

requiredNQualityValueImprovements: number
+

Required number of quality value improvements.

+

Protected random

random: Random
+

Random number generator.

+

Methods

initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement

  • initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement(attraction: number, repulsion: number, edgeWeightIncrement: number): void

getAttraction

  • getAttraction(): number

getRepulsion

  • getRepulsion(): number

getEdgeWeightIncrement

  • getEdgeWeightIncrement(): number

setAttraction

  • setAttraction(attraction: number): void

setRepulsion

  • setRepulsion(repulsion: number): void

setEdgeWeightIncrement

  • setEdgeWeightIncrement(edgeWeightIncrement: number): void

calcQuality

  • +

    Calculates the quality of a layout using the VOS quality function.

    +

    The VOS quality function is given by

    +
    1 / attraction * sum(a[i][j] * d(x[i], x[j]) ^
    attraction) - 1 / repulsion * sum(d(x[i], x[j]) ^
    repulsion),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j and +x[i] = (x[i][1], x[i][2]) are the coordinates of node i. The function +d(x[i], x[j]) is the Euclidean distance between nodes i and j. The +sum is taken over all pairs of nodes i and j with j < i. The +attraction parameter must be greater than the repulsion parameter. The +lower the value of the VOS quality function, the higher the quality of the +layout.

    +

    Parameters

    Returns number

    Quality of the layout

    +

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndRandom

  • initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndRandom(attraction: number, repulsion: number, edgeWeightIncrement: number, random: Random): void
  • +

    Initializes a gradient descent VOS layout algorithm for a specified +attraction parameter, repulsion parameter, and edge weight increment +parameter.

    +

    Parameters

    • attraction: number
      +

      Attraction parameter

      +
    • repulsion: number
      +

      Repulsion parameter

      +
    • edgeWeightIncrement: number
      +

      Edge weight increment parameter

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndMaxNIterationsAndInitialStepSizeAndMinStepSizeAndStepSizeReductionAndRequiredNQualityValueImprovementsAndRandom

  • initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndMaxNIterationsAndInitialStepSizeAndMinStepSizeAndStepSizeReductionAndRequiredNQualityValueImprovementsAndRandom(attraction: number, repulsion: number, edgeWeightIncrement: number, maxNIterations: number, initialStepSize: number, minStepSize: number, stepSizeReduction: number, requiredNQualityValueImprovements: number, random: Random): void
  • +

    Initializes a gradient descent VOS layout algorithm for a specified +attraction parameter, repulsion parameter, edge weight increment +parameter, maximum number of iterations, initial step size, minimum step +size, step size reduction, and required number of quality value +improvements.

    +

    Parameters

    • attraction: number
      +

      Attraction parameter

      +
    • repulsion: number
      +

      Repulsion parameter

      +
    • edgeWeightIncrement: number
      +

      Edge weight increment parameter

      +
    • maxNIterations: number
      +

      Maximum number of iterations

      +
    • initialStepSize: number
      +

      Initial step size

      +
    • minStepSize: number
      +

      Minimum step size

      +
    • stepSizeReduction: number
      +

      Step size reduction

      +
    • requiredNQualityValueImprovements: number
      +

      Required number of quality value + improvements

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

clone

getMaxNIterations

  • getMaxNIterations(): number

getInitialStepSize

  • getInitialStepSize(): number

getMinStepSize

  • getMinStepSize(): number

getStepSizeReduction

  • getStepSizeReduction(): number

getRequiredNQualityValueImprovements

  • getRequiredNQualityValueImprovements(): number

setMaxNIterations

  • setMaxNIterations(maxNIterations: number): void

setInitialStepSize

  • setInitialStepSize(initialStepSize: number): void

setMinStepSize

  • setMinStepSize(minStepSize: number): void

setStepSizeReduction

  • setStepSizeReduction(stepSizeReduction: number): void

setRequiredNQualityValueImprovements

  • setRequiredNQualityValueImprovements(requiredNQualityValueImprovements: number): void
  • +

    Sets the required number of quality value improvements.

    +

    Parameters

    • requiredNQualityValueImprovements: number
      +

      Required number of quality value + improvements

      +

    Returns void

findLayout

improveLayout

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Class IncrementalCPMClusteringAlgorithm Abstract

+

Abstract base class for incremental clustering algorithms that use the CPM +quality function.

+

Hierarchy

Implements

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Methods

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

Abstract clone

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

Abstract improveClustering

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Class IterativeCPMClusteringAlgorithm Abstract

+

Abstract base class for iterative clustering algorithms that use the CPM +quality function.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Static Readonly DEFAULT_N_ITERATIONS

DEFAULT_N_ITERATIONS: number = 1
+

Default number of iterations.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected nIterations

nIterations: number
+

Number of iterations.

+

Methods

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

Abstract clone

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

initializeBasedOnResolutionAndNIterations

  • initializeBasedOnResolutionAndNIterations(resolution: number, nIterations: number): void
  • +

    Initializes an iterative CPM clustering algorithm with a specified +resolution parameter and number of iterations.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +

    Returns void

getNIterations

  • getNIterations(): number

setNIterations

  • setNIterations(nIterations: number): void

improveClustering

Protected Abstract improveClusteringOneIteration

  • +

    Improves a clustering by performing one iteration of an iterative +clustering algorithm.

    +

    Parameters

    Returns boolean

    Boolean indicating whether the clustering has been improved

    +

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Class Layout

+

Layout of the nodes in a network.

+

Hierarchy

  • Layout

Index

Properties

Constructors

Methods

Properties

coordinates

coordinates: number[][]
+

Coordinates of each node.

+

Protected nNodes

nNodes: number
+

Number of nodes.

+

Constructors

constructor

Methods

initializeBasedOnNNodes

  • initializeBasedOnNNodes(nNodes: number): void
  • +

    Initializes a random layout for a specified number of nodes.

    +

    Parameters

    • nNodes: number
      +

      Number of nodes

      +

    Returns void

initializeBasedOnNNodesAndRandom

  • initializeBasedOnNNodesAndRandom(nNodes: number, random: Random): void
  • +

    Initializes a random layout for a specified number of nodes.

    +

    Parameters

    • nNodes: number
      +

      Number of nodes

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

initializeBasedOnCoordinates

  • initializeBasedOnCoordinates(coordinates: number[][]): void
  • +

    Initializes a layout using specified coordinates for each node.

    +

    Parameters

    • coordinates: number[][]
      +

      Coordinates of each node

      +

    Returns void

clone

getNNodes

  • getNNodes(): number
  • +

    Returns the number of nodes.

    +

    Returns number

    Number of nodes

    +

getCoordinates

  • getCoordinates(): number[][]
  • +

    Returns the coordinates of each node.

    +

    Returns number[][]

    Coordinates of each node

    +

getCoordinatesNode

  • getCoordinatesNode(node: number): number[]
  • +

    Returns the coordinates of a node.

    +

    Parameters

    • node: number
      +

      Node

      +

    Returns number[]

    Coordinates

    +

getMinCoordinates

  • getMinCoordinates(): number[]
  • +

    Returns the minimum of the coordinates of all node.

    +

    Returns number[]

    Minimum coordinates

    +

getMaxCoordinates

  • getMaxCoordinates(): number[]
  • +

    Returns the maximum of the coordinates of all node.

    +

    Returns number[]

    Maximum coordinates

    +

getAverageDistance

  • getAverageDistance(): number
  • +

    Returns the average distance between all pairs of nodes.

    +

    Returns number

    Average distance

    +

setCoordinates

  • setCoordinates(node: number, coordinates: number[]): void
  • +

    Positions a node at coordinates.

    +

    Parameters

    • node: number
      +

      Node

      +
    • coordinates: number[]
      +

      Coordinates

      +

    Returns void

initRandomCoordinates

  • initRandomCoordinates(random?: Random): void
  • +

    Initializes a random layout.

    +

    Each node is positioned at random coordinates.

    +

    Parameters

    • random: Random = ...
      +

      Random number generator

      +

    Returns void

standardize

  • standardize(standardizeDistances: boolean): void
  • +

    Standardizes a layout.

    +

    Standardization involves translation, rotation, reflection, and +optionally dilation. The layout is translated so that it is centered at +the origin. The layout is rotated so that the variance in the horizontal +dimension is maximized. The layout is reflected so that in both the +horizontal and the vertical dimension the median of the coordinates is +non-positive. If standardizeDistances = true, the layout is dilated so +that the average distance between nodes equals one.

    +

    Parameters

    • standardizeDistances: boolean
      +

      Standardize distances

      +

    Returns void

rotate

  • rotate(angle: number): void
  • +

    Rotates a layout.

    +

    Parameters

    • angle: number
      +

      Angle

      +

    Returns void

flip

  • flip(dimension: number): void
  • +

    Flips a layout.

    +

    Parameters

    • dimension: number
      +

      Dimension

      +

    Returns void

Private initRandomCoordinatesHelper

  • initRandomCoordinatesHelper(random: Random): void

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Class LeidenAlgorithm

+

Leiden algorithm.

+

The Leiden algorithm consists of three phases:

+
    +
  1. local moving of nodes between clusters,
    2. +
  2. refinement of the clusters,
    3. +
  3. aggregation of the network based on the refined clusters, using the +non-refined clusters to create an initial clustering for the aggregate +network.
    4. +
+ +

These phases are repeated until no further improvements can be made. By +default, local moving of nodes is performed using the FastLocalMovingAlgorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Static Readonly DEFAULT_N_ITERATIONS

DEFAULT_N_ITERATIONS: number = 1
+

Default number of iterations.

+

Static Readonly DEFAULT_RANDOMNESS

DEFAULT_RANDOMNESS: number = LocalMergingAlgorithm.DEFAULT_RANDOMNESS
+

Default randomness parameter.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected nIterations

nIterations: number
+

Number of iterations.

+

Protected randomness

randomness: number
+

Randomness parameter.

+

Protected localMovingAlgorithm

localMovingAlgorithm: IncrementalCPMClusteringAlgorithm
+

Local moving algorithm.

+

Protected random

random: Random
+

Random number generator.

+

Methods

getResolution

  • getResolution(): number

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

initializeBasedOnResolutionAndNIterations

  • initializeBasedOnResolutionAndNIterations(resolution: number, nIterations: number): void

getNIterations

  • getNIterations(): number

setNIterations

  • setNIterations(nIterations: number): void

improveClustering

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom

  • initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom(resolution: number, nIterations: number, randomness: number, random: Random): void
  • +

    Initializes a Leiden algorithm for a specified resolution parameter, +number of iterations, and randomness parameter.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +
    • randomness: number
      +

      Randomness parameter

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

initializeBasedOnResolutionAndNIterationsAndRandomnessAndLocalMovingAlgorithmAndRandom

  • initializeBasedOnResolutionAndNIterationsAndRandomnessAndLocalMovingAlgorithmAndRandom(resolution: number, nIterations: number, randomness: number, localMovingAlgorithm: IncrementalCPMClusteringAlgorithm, random: Random): void
  • +

    Initializes a Leiden algorithm for a specified resolution parameter, +number of iterations, randomness parameter, and local moving algorithm.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +
    • randomness: number
      +

      Randomness parameter

      +
    • localMovingAlgorithm: IncrementalCPMClusteringAlgorithm
      +

      Local moving algorithm

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

clone

getRandomness

  • getRandomness(): number

getLocalMovingAlgorithm

setResolution

  • setResolution(resolution: number): void

setRandomness

  • setRandomness(randomness: number): void
  • +

    Sets the randomness parameter.

    +

    Parameters

    • randomness: number
      +

      Randomness parameter

      +

    Returns void

setLocalMovingAlgorithm

Protected improveClusteringOneIteration

  • +

    Improves a clustering by performing one iteration of the Leiden +algorithm.

    +

    The Leiden algorithm consists of three phases:

    +
      +
    1. local moving of nodes between clusters,
      2. +
    2. refinement of the clusters,
      3. +
    3. aggregation of the network based on the refined clusters, using the +non-refined clusters to create an initial clustering for the aggregate +network.
      4. +
    + +

    These phases are repeated until no further improvements can be made.

    +

    Parameters

    Returns boolean

    Boolean indicating whether the clustering has been improved

    +

Constructors

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Class LocalMergingAlgorithm

+

Local merging algorithm.

+

The local merging algorithm starts from a singleton partition. It performs a +single iteration over the nodes in a network. Each node belonging to a +singleton cluster is considered for merging with another cluster. This +cluster is chosen randomly from all clusters that do not result in a +decrease in the quality function. The larger the increase in the quality +function, the more likely a cluster is to be chosen. The strength of this +effect is determined by the randomness parameter. The higher the value of +the randomness parameter, the stronger the randomness in the choice of a +cluster. The lower the value of the randomness parameter, the more likely +the cluster resulting in the largest increase in the quality function is to +be chosen. A node is merged with a cluster only if both are sufficiently +well connected to the rest of the network.

+

The local merging algorithm is used in the cluster refinement phase of the +LeidenAlgorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Static Readonly DEFAULT_RANDOMNESS

DEFAULT_RANDOMNESS: number = 1e-2
+

Default randomness parameter.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected randomness

randomness: number
+

Randomness parameter.

+

Protected random

random: Random
+

Random number generator.

+

Methods

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnResolutionAndRandomnessAndRandom

  • initializeBasedOnResolutionAndRandomnessAndRandom(resolution: number, randomness: number, random: Random): void
  • +

    Initializes a local merging algorithm for a specified resolution +parameter and randomness parameter.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • randomness: number
      +

      Randomness parameter

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

clone

getRandomness

  • getRandomness(): number

setRandomness

  • setRandomness(randomness: number): void

findClustering

  • +

    Finds a clustering of the nodes in a network using the local merging +algorithm.

    +

    The local merging algorithm starts from a singleton partition. It +performs a single iteration over the nodes in a network. Each node +belonging to a singleton cluster is considered for merging with another +cluster. This cluster is chosen randomly from all clusters that do not +result in a decrease in the quality function. The larger the increase in +the quality function, the more likely a cluster is to be chosen. The +strength of this effect is determined by the randomness parameter. The +higher the value of the randomness parameter, the stronger the +randomness in the choice of a cluster. The lower the value of the +randomness parameter, the more likely the cluster resulting in the +largest increase in the quality function is to be chosen. A node is +merged with a cluster only if both are sufficiently well connected to +the rest of the network.

    +

    Parameters

    Returns Clustering

    Clustering

    +

Constructors

constructor

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Class LouvainAlgorithm

+

Louvain algorithm.

+

The Louvain algorithm consists of two phases:

+
    +
  1. local moving of nodes between clusters,
    2. +
  2. aggregation of the network based on the clusters.
    3. +
+ +

These phases are repeated until no further improvements can be made. By +default, local moving of nodes is performed using the StandardLocalMovingAlgorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Static Readonly DEFAULT_N_ITERATIONS

DEFAULT_N_ITERATIONS: number = 1
+

Default number of iterations.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected nIterations

nIterations: number
+

Number of iterations.

+

Protected localMovingAlgorithm

localMovingAlgorithm: IncrementalCPMClusteringAlgorithm
+

Local moving algorithm.

+

Methods

getResolution

  • getResolution(): number

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

initializeBasedOnResolutionAndNIterations

  • initializeBasedOnResolutionAndNIterations(resolution: number, nIterations: number): void

getNIterations

  • getNIterations(): number

setNIterations

  • setNIterations(nIterations: number): void

improveClustering

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnResolutionAndNIterationsAndRandom

  • initializeBasedOnResolutionAndNIterationsAndRandom(resolution: number, nIterations: number, random: Random): void
  • +

    Initializes a Louvain algorithm for a specified resolution parameter and +number of iterations.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

initializeBasedOnResolutionAndNIterationsAndLocalMovingAlgorithm

  • initializeBasedOnResolutionAndNIterationsAndLocalMovingAlgorithm(resolution: number, nIterations: number, localMovingAlgorithm: IncrementalCPMClusteringAlgorithm): void
  • +

    Initializes a Louvain algorithm for a specified resolution parameter, +number of iterations, and local moving algorithm.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • nIterations: number
      +

      Number of iterations

      +
    • localMovingAlgorithm: IncrementalCPMClusteringAlgorithm
      +

      Local moving algorithm

      +

    Returns void

clone

getLocalMovingAlgorithm

setResolution

  • setResolution(resolution: number): void

setLocalMovingAlgorithm

Protected improveClusteringOneIteration

  • +

    Improves a clustering by performing one iteration of the Louvain +algorithm.

    +

    The Louvain algorithm consists of two phases:

    +
      +
    1. local moving of nodes between clusters,
      2. +
    2. aggregation of the network based on the clusters.
      3. +
    + +

    These phases are repeated until no further improvements can be made.

    +

    Parameters

    Returns boolean

    Boolean indicating whether the clustering has been improved

    +

Constructors

constructor

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Class Network

+

Network.

+

Weighted nodes and weighted edges are supported. Directed edges are not +supported.

+

Network objects are immutable.

+

The adjacency matrix of the network is stored in a sparse compressed format.

+

Hierarchy

  • Network

Index

Properties

Constructors

Methods

Properties

nNodes

nNodes: number
+

Number of nodes.

+

nodeWeights

nodeWeights: number[]
+

Node weights.

+

firstNeighborIndices

firstNeighborIndices: number[]
+

Index of the first neighbor of each node in the neighbors array.

+

The neighbors of node i are given by +neighbors[firstNeighborIndices[i]], ..., neighbors[firstNeighborIndices[i + 1] - 1].

+

neighbors

neighbors: number[]
+

Neighbors of each node.

+

edgeWeights

edgeWeights: number[]
+

Edge weights.

+

totalEdgeWeightSelfLinks

totalEdgeWeightSelfLinks: number
+

Total edge weight of self links.

+

Protected nEdges

nEdges: number
+

Number of edges.

+

Each edge is counted twice, once in each direction.

+

Constructors

constructor

Methods

getNNodes

  • getNNodes(): number
  • +

    Returns the number of nodes.

    +

    Returns number

    Number of nodes

    +

getTotalNodeWeight

  • getTotalNodeWeight(): number
  • +

    Returns the total node weight.

    +

    Returns number

    Total node weight

    +

getNodeWeights

  • getNodeWeights(): number[]
  • +

    Returns the weight of each node.

    +

    Returns number[]

    Weight of each node

    +

getNodeWeight

  • getNodeWeight(node: number): number
  • +

    Returns the weight of a node.

    +

    Parameters

    • node: number
      +

      Node

      +

    Returns number

    Weight

    +

getNEdges

  • getNEdges(): number
  • +

    Returns the number of edges.

    +

    Each edge is counted only once, even though an edge runs in two +directions. This means that the number of edges returned by +getEdges equals twice the number of edges returned by +getNEdges.

    +

    Returns number

    Number of edges

    +

getNNeighborsPerNode

  • getNNeighborsPerNode(): number[]
  • +

    Returns the number of neighbors per node.

    +

    Returns number[]

    Number of neighbors per node

    +

getNNeighbors

  • getNNeighbors(node: number): number
  • +

    Returns the number of neighbors of a node.

    +

    Parameters

    • node: number
      +

      Node

      +

    Returns number

    Number of neighbors

    +

getEdges

  • getEdges(): number[][]
  • +

    Returns the list of edges.

    +

    Each edge is included twice, once in each direction. This means that the +number of edges returned by getEdges equals twice the number of +edges returned by getNEdges.

    +

    The list of edges is returned in a two-dimensional array edges. +Edge i connects nodes edges[0][i] and edges[1][i].

    +

    Returns number[][]

    List of edges

    +

getNeighborsPerNode

  • getNeighborsPerNode(): number[][]
  • +

    Returns a list of neighbors per node.

    +

    Returns number[][]

    List of neighbors per node

    +

getNeighbors

  • getNeighbors(node: number): number[]
  • +

    Returns the list of neighbors of a node.

    +

    Parameters

    • node: number
      +

      Node

      +

    Returns number[]

    List of neighbors

    +

getTotalEdgeWeightPerNode

  • getTotalEdgeWeightPerNode(): number[]
  • +

    Returns the total edge weight per node. The total edge weight of a node +equals the sum of the weights of the edges between the node and its +neighbors.

    +

    Returns number[]

    Total edge weight per node

    +

getTotalEdgeWeight

  • getTotalEdgeWeight(node?: number): number
  • +

    Returns the total edge weight.

    +

    Each edge is considered only once, even though an edge runs in two +directions. This means that the sum of the edge weights returned by +getEdgeWeights equals twice the total edge weight returned by +getTotalEdgeWeight.

    +

    Edge weights of self links are not included.

    +

    Parameters

    • Optional node: number
      +

      Node

      +

    Returns number

    Total edge weight

    +

getEdgeWeightsPerNode

  • getEdgeWeightsPerNode(): number[][]
  • +

    Returns a list of edge weights per node. These are the weights of the +edges between a node and its neighbors.

    +

    Returns number[][]

    List of edge weights per node

    +

getEdgeWeights

  • getEdgeWeights(node?: number): number[]
  • +

    Returns the list of edge weights of a node. These are the weights of the +edges between the node and its neighbors.

    +

    Parameters

    • Optional node: number
      +

      Node

      +

    Returns number[]

    List of edge weights

    +

getTotalEdgeWeightSelfLinks

  • getTotalEdgeWeightSelfLinks(): number
  • +

    Returns the total edge weight of self links.

    +

    Returns number

    Total edge weight of self links

    +

createNetworkWithoutNodeWeights

  • createNetworkWithoutNodeWeights(): Network
  • +

    Creates a copy of the network, but without node weights.

    +

    Each node is assigned a weight of 1.

    +

    Returns Network

    Network without node weights

    +

createNetworkWithoutEdgeWeights

  • createNetworkWithoutEdgeWeights(): Network
  • +

    Creates a copy of the network, but without edge weights.

    +

    Each edge is assigned a weight of 1.

    +

    Returns Network

    Network without edge weights

    +

createNetworkWithoutNodeAndEdgeWeights

  • createNetworkWithoutNodeAndEdgeWeights(): Network
  • +

    Creates a copy of the network, but without node and edge weights.

    +

    Each node is assigned a weight of 1, and each edge is assigned a weight +of 1.

    +

    Returns Network

    Network without node and edge weights

    +

createNormalizedNetworkUsingAssociationStrength

  • createNormalizedNetworkUsingAssociationStrength(): Network
  • +

    Creates a copy of the network in which the edge weights have been +normalized using the association strength.

    +

    The normalized weight a'[i][j] of the edge between nodes i and j is +given by

    +
    a'[i][j] = a[i][j] / (n[i] * n[j] / (2 * m)),
+
    +

    where a[i][j] is the non-normalized weight of the edge between nodes i +and j, n[i] is the weight of node i, and m is half the total node +weight.

    +

    If each node's weight equals the total weight of the edges between the +node and its neighbors, the edge weights are normalized by dividing them +by the expected edge weights in the random configuration model.

    +

    The node weights are set to 1.

    +

    Returns Network

    Normalized network

    +

createNormalizedNetworkUsingFractionalization

  • createNormalizedNetworkUsingFractionalization(): Network
  • +

    Creates a copy of the network in which the edge weights have been +normalized using fractionalization.

    +

    The normalized weight a'[i][j] of the edge between nodes i and j is +given by

    +
    a'[i][j] = a[i][j] * (n / n[i] + n / n[j]) / 2,
+
    +

    where a[i][j] is the non-normalized weight of the edge between nodes i +and j, n[i] is the weight of node i, and n is the number of nodes.

    +

    The node weights are set to 1.

    +

    Returns Network

    Normalized network

    +

createPrunedNetwork

  • +

    Creates a copy of the network that has been pruned in order to have a +specified maximum number of edges.

    +

    Only the edges with the highest weights are retained in the pruned +network. In case of ties, the edges to be retained are selected +randomly.

    +

    Parameters

    • maxNEdges: number
      +

      Maximum number of edges

      +
    • random: Random = ...
      +

      Random number generator

      +

    Returns Network

    Pruned network

    +

createSubnetworkForNodes1

  • createSubnetworkForNodes1(nodes: number[]): Network
  • +

    Creates an induced subnetwork for specified nodes.

    +

    Parameters

    • nodes: number[]
      +

      Nodes

      +

    Returns Network

    Subnetwork

    +

createSubnetworkForNodes2

  • createSubnetworkForNodes2(nodesInSubnetwork: boolean[]): Network
  • +

    Creates an induced subnetwork for specified nodes.

    +

    Parameters

    • nodesInSubnetwork: boolean[]
      +

      Indicates the nodes to be included in the + subnetwork.

      +

    Returns Network

    Subnetwork

    +

createSubnetworkForCluster

  • +

    Creates an induced subnetwork for a specified cluster in a clustering.

    +

    If subnetworks need to be created for all clusters in a clustering, it +is more efficient to use createSubnetworks.

    +

    Parameters

    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster

      +

    Returns Network

    Subnetwork

    +

createSubnetworks

  • +

    Creates induced subnetworks for the clusters in a clustering.

    +

    Parameters

    Returns Network[]

    Subnetworks

    +

createSubnetworkLargestComponent

  • createSubnetworkLargestComponent(): Network
  • +

    Creates an induced subnetwork of the largest connected component.

    +

    Returns Network

    Subnetwork

    +

createReducedNetwork

  • +

    Creates a reduced (or aggregate) network based on a clustering.

    +

    Each node in the reduced network corresponds to a cluster of nodes in +the original network. The weight of a node in the reduced network equals +the sum of the weights of the nodes in the corresponding cluster in the +original network. The weight of an edge between two nodes in the reduced +network equals the sum of the weights of the edges between the nodes in +the two corresponding clusters in the original network.

    +

    Parameters

    Returns Network

    Reduced network

    +

identifyComponents

  • +

    Identifies the connected components of the network.

    +

    Returns Clustering

    Connected components

    +

Private checkIntegrity

  • checkIntegrity(): void
  • +

    Checks the integrity of the network.

    +

    It is checked whether:

    +
      +
    • variables have a correct value,
      • +
    • arrays have a correct length,
      • +
    • edges are sorted correctly,
      • +
    • edges are stored in both directions.
      • +
    + +

    An exception is thrown if the integrity of the network is violated.

    +
    throws

    An illegal argument was provided in the construction of the network.

    +

    Returns void

Private initializeNetworkBasedOnEdges

  • initializeNetworkBasedOnEdges(nNodes: number, nodeWeights: undefined | number[], setNodeWeightsToTotalEdgeWeights: undefined | boolean, edges: number[][], edgeWeights: undefined | number[], sortedEdges: undefined | boolean, checkIntegrity: undefined | boolean): void

  • Parameters

    • nNodes: number
    • nodeWeights: undefined | number[]
    • setNodeWeightsToTotalEdgeWeights: undefined | boolean
    • edges: number[][]
    • edgeWeights: undefined | number[]
    • sortedEdges: undefined | boolean
    • checkIntegrity: undefined | boolean

    Returns void

Private initializeNetworkBasedOnNeighbors

  • initializeNetworkBasedOnNeighbors(nNodes: number, nodeWeights: undefined | number[], setNodeWeightsToTotalEdgeWeights: undefined | boolean, firstNeighborIndices: number[], neighbors: number[], edgeWeights: undefined | number[], checkIntegrity: undefined | boolean): void

  • Parameters

    • nNodes: number
    • nodeWeights: undefined | number[]
    • setNodeWeightsToTotalEdgeWeights: undefined | boolean
    • firstNeighborIndices: number[]
    • neighbors: number[]
    • edgeWeights: undefined | number[]
    • checkIntegrity: undefined | boolean

    Returns void

Private getTotalEdgeWeightPerNodeHelper

  • getTotalEdgeWeightPerNodeHelper(): number[]

Private getRandomNumber

  • getRandomNumber(node1: number, node2: number, randomNumbers: number[]): number

  • Parameters

    • node1: number
    • node2: number
    • randomNumbers: number[]

    Returns number

Private createSubnetwork

  • createSubnetwork(clustering: Clustering, cluster: number, nodes: number[], subnetworkNodes: number[], subnetworkNeighbors: number[], subnetworkEdgeWeights: number[]): Network

  • Parameters

    • clustering: Clustering
    • cluster: number
    • nodes: number[]
    • subnetworkNodes: number[]
    • subnetworkNeighbors: number[]
    • subnetworkEdgeWeights: number[]

    Returns Network

Private sortEdges

  • sortEdges(edges: number[][], edgeWeights?: number[]): void

  • Parameters

    • edges: number[][]
    • Optional edgeWeights: number[]

    Returns void

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Class StandardLocalMovingAlgorithm

+

Standard local moving algorithm.

+

The standard local moving algorithm iterates over the nodes in a network. A +node is moved to the cluster that results in the largest increase in the +quality function. If the current cluster assignment of the node is already +optimal, the node is not moved. The algorithm continues iterating over the +nodes in a network until no more nodes can be moved.

+

A fast variant of the standard local moving algorithm is provided by the +FastLocalMovingAlgorithm.

+

Hierarchy

Index

Properties

Methods

Constructors

Properties

Static Readonly DEFAULT_RESOLUTION

DEFAULT_RESOLUTION: number = 1
+

Default resolution parameter.

+

Protected resolution

resolution: number
+

Resolution parameter.

+

Protected random

random: Random
+

Random number generator.

+

Methods

getResolution

  • getResolution(): number

setResolution

  • setResolution(resolution: number): void

calcQuality

  • +

    Calculates the quality of a clustering using the CPM quality function.

    +

    The CPM quality function is given by

    +
    1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] *
    n[j])),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j, +n[i] is the weight of node i, m is the total edge weight, and +resolution is the resolutionparameter. The function d(c[i], c[j]) +equals 1 if nodes i and j belong to the same cluster and 0 otherwise. +The sum is taken over all pairs of nodes i and j.

    +

    Modularity can be expressed in terms of CPM by setting n[i] equal to +the total weight of the edges between node i and its neighbors and by +rescaling the resolution parameter by 2 * m.

    +

    Parameters

    Returns number

    Quality of the clustering

    +

removeCluster

  • +

    Removes a cluster from a clustering by merging the cluster with another +cluster. If a cluster has no connections with other clusters, it cannot +be removed.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • cluster: number
      +

      Cluster to be removed

      +

    Returns number

    Cluster with which the cluster to be removed has been merged, or + -1 if the cluster could not be removed

    +

removeSmallClustersBasedOnNNodes

  • removeSmallClustersBasedOnNNodes(network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean

removeSmallClustersBasedOnWeight

  • removeSmallClustersBasedOnWeight(network: Network, clustering: Clustering, minClusterWeight: number): boolean
  • +

    Removes small clusters from a clustering. Clusters are merged until each +cluster has at least a certain minimum total node weight.

    +

    The total node weight of a cluster equals the sum of the weights of the +nodes belonging to the cluster.

    +

    Parameters

    • network: Network
      +

      Network

      +
    • clustering: Clustering
      +

      Clustering

      +
    • minClusterWeight: number
      +

      Minimum total node weight of a cluster

      +

    Returns boolean

    Boolean indicating whether any clusters have been removed

    +

initializeBasedOnResolution

  • initializeBasedOnResolution(resolution: number): void

findClustering

initializeBasedOnRandom

  • initializeBasedOnRandom(random: Random): void

initializeBasedOnResolutionAndRandom

  • initializeBasedOnResolutionAndRandom(resolution: number, random: Random): void
  • +

    Initializes a standard local merging algorithm for a specified resolution +parameter.

    +

    Parameters

    • resolution: number
      +

      Resolution parameter

      +
    • random: Random
      +

      Random number generator

      +

    Returns void

clone

improveClustering

  • +

    Improves a clustering of the nodes in a network using the standard local +moving algorithm.

    +

    The standard local moving algorithm iterates over the nodes in a +network. A node is moved to the cluster that results in the largest +increase in the quality function. If the current cluster assignment of +the node is already optimal, the node is not moved. The algorithm +continues iterating over the nodes in a network until no more nodes can +be moved.

    +

    Parameters

    Returns boolean

    Boolean indicating whether the clustering has been improved

    +

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constructor

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Class VOSLayoutAlgorithm Abstract

+

Abstract base class for layout algorithms that use the VOS quality function.

+

Hierarchy

Implements

Index

Properties

Constructors

Methods

Properties

Static Readonly DEFAULT_ATTRACTION

DEFAULT_ATTRACTION: number = 2
+

Default attraction parameter.

+

Static Readonly DEFAULT_REPULSION

DEFAULT_REPULSION: number = 1
+

Default repulsion parameter.

+

Static Readonly DEFAULT_EDGE_WEIGHT_INCREMENT

DEFAULT_EDGE_WEIGHT_INCREMENT: number = 0
+

Default edge weight increment parameter.

+

Protected attraction

attraction: number
+

Attraction parameter.

+

Protected repulsion

repulsion: number
+

Repulsion parameter.

+

Protected edgeWeightIncrement

edgeWeightIncrement: number
+

Edge weight increment parameter.

+

Constructors

constructor

Methods

initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement

  • initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement(attraction: number, repulsion: number, edgeWeightIncrement: number): void
  • +

    Initializes a VOS layout algorithm with a specified attraction parameter, +repulsion parameter, and edge weight increment parameter.

    +

    Parameters

    • attraction: number
      +

      Attraction parameter

      +
    • repulsion: number
      +

      Repulsion parameter

      +
    • edgeWeightIncrement: number
      +

      Edge weight increment parameter

      +

    Returns void

getAttraction

  • getAttraction(): number

getRepulsion

  • getRepulsion(): number

getEdgeWeightIncrement

  • getEdgeWeightIncrement(): number
  • +

    Returns the edge weight increment parameter.

    +

    Returns number

    Edge weight increment parameter

    +

setAttraction

  • setAttraction(attraction: number): void
  • +

    Sets the attraction parameter.

    +

    Parameters

    • attraction: number
      +

      Attraction parameter

      +

    Returns void

setRepulsion

  • setRepulsion(repulsion: number): void
  • +

    Sets the repulsion parameter.

    +

    Parameters

    • repulsion: number
      +

      Repulsion parameter

      +

    Returns void

setEdgeWeightIncrement

  • setEdgeWeightIncrement(edgeWeightIncrement: number): void
  • +

    Sets the edge weight increment parameter.

    +

    Parameters

    • edgeWeightIncrement: number
      +

      Edge weight increment parameter

      +

    Returns void

calcQuality

  • +

    Calculates the quality of a layout using the VOS quality function.

    +

    The VOS quality function is given by

    +
    1 / attraction * sum(a[i][j] * d(x[i], x[j]) ^
    attraction) - 1 / repulsion * sum(d(x[i], x[j]) ^
    repulsion),
+
    +

    where a[i][j] is the weight of the edge between nodes i and j and +x[i] = (x[i][1], x[i][2]) are the coordinates of node i. The function +d(x[i], x[j]) is the Euclidean distance between nodes i and j. The +sum is taken over all pairs of nodes i and j with j < i. The +attraction parameter must be greater than the repulsion parameter. The +lower the value of the VOS quality function, the higher the quality of the +layout.

    +

    Parameters

    Returns number

    Quality of the layout

    +

Abstract clone

Abstract findLayout

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Class NetworkAnalysis Abstract

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Methods

Properties

Constructors

constructor

Methods

data

Abstract run

  • run(): void

Properties

Protected _networkHelper

_networkHelper: undefined | NetworkHelper

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Class NetworkClustering

+

Class for running the Leiden and Louvain algorithms for network clustering.

+

Hierarchy

Index

Constructors

Methods

Properties

Constructors

constructor

Methods

data

qualityFunction

  • +

    Quality function to be optimized. Either the CPM (constant Potts model) or +the modularity quality function can be used.

    +

    Parameters

    • value: "CPM" | "Modularity"

    Returns NetworkClustering

normalization

  • normalization(value: "NoNormalization" | "AssociationStrength" | "Fractionalization"): NetworkClustering

resolution

minClusterSize

algorithm

randomStarts

iterations

randomness

seed

run

  • run(): void

Properties

Protected _networkHelper

_networkHelper: undefined | NetworkHelper

Private _useModularity

_useModularity: boolean = false

Private _normalization

_normalization: NormalizationMethods = NormalizationMethods.NoNormalization

Private _resolution

_resolution: number = CPMClusteringAlgorithm.DEFAULT_RESOLUTION

Private _minClusterSize

_minClusterSize: number = 1

Private _useLouvain

_useLouvain: boolean = false

Private _nRandomStarts

_nRandomStarts: number = 1

Private _nIterations

_nIterations: number = 10

Private _randomness

_randomness: number = LeidenAlgorithm.DEFAULT_RANDOMNESS

Private _useSeed

_useSeed: boolean = false

Private _seed

_seed: number = 0

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Class NetworkHelper

Hierarchy

  • NetworkHelper

Index

Constructors

Methods

Properties

Constructors

constructor

Methods

getNetwork

getInitialClustering

getInitialLayout

  • getInitialLayout(): undefined | Layout

setClusters

  • setClusters(clusters: number[]): void

setCoordinates

  • setCoordinates(coordinates: number[][]): void

Properties

Private _nodes

_nodes: Node[]

Private _network

_network: Network

Private _initialClustering

_initialClustering: undefined | Clustering

Private _initialLayout

_initialLayout: undefined | Layout

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Class NetworkLayout

+

Class for running the gradient descent VOS layout algorithm for network +layout.

+

Hierarchy

Index

Constructors

Methods

Properties

Constructors

constructor

Methods

data

qualityFunction

  • +

    Quality function to be optimized. Either the VOS (visualization of +similarities) or the LinLog quality function can be used.

    +

    Parameters

    • value: "VOS" | "LinLog"

    Returns NetworkLayout

normalization

  • normalization(value: "NoNormalization" | "AssociationStrength" | "Fractionalization"): NetworkLayout
  • +

    Method for normalizing edge weights in the VOS quality function.

    +

    Parameters

    • value: "NoNormalization" | "AssociationStrength" | "Fractionalization"

    Returns NetworkLayout

attraction

maxNIterations

initialStepSize

minStepSize

stepSizeReduction

requiredNQualityValueImprovements

repulsion

randomStarts

seed

run

  • run(): void

Properties

Protected _networkHelper

_networkHelper: undefined | NetworkHelper

Private _useLinLog

_useLinLog: boolean = false

Private _normalization

_normalization: NormalizationMethods = NormalizationMethods.NoNormalization

Private _attraction

_attraction: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_ATTRACTION

Private _repulsion

_repulsion: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_REPULSION

Private _nRandomStarts

_nRandomStarts: number = 1

Private _maxNIterations

_maxNIterations: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_MAX_N_ITERATIONS

Private _initialStepSize

_initialStepSize: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_INITIAL_STEP_SIZE

Private _minStepSize

_minStepSize: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_MIN_STEP_SIZE

Private _stepSizeReduction

_stepSizeReduction: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_STEP_SIZE_REDUCTION

Private _requiredNQualityValueImprovements

_requiredNQualityValueImprovements: number = GradientDescentVOSLayoutAlgorithm.DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS

Private _seed

_seed: number = 0

Private _useSeed

_useSeed: boolean = false

Private _edgeWeightIncrementUnconnectedNodes

_edgeWeightIncrementUnconnectedNodes: number = 0.01

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Class Random

+

An almost complete implementation in JS of the java.util.Random class +from J2SE, designed to so far as possible produce the same output +sequences as the Java original when supplied with the same seed. +See https://www.npmjs.com/package/java-random for more +information.

+

Hierarchy

  • Random

Index

Constructors

Methods

Constructors

constructor

  • new Random(seed?: number): Random

Methods

nextDouble

  • nextDouble(): number
  • +

    Returns a pseudorandom double value between 0.0 and 1.0.

    +

    Returns number

    Pseudorandom double value between 0.0 and 1.0

    +

nextInt

  • nextInt(bound?: number): number
  • +

    Returns a pseudorandom int value (between zero and the specified upper bound).

    +

    Parameters

    • Optional bound: number
      +

      Upper bound (exclusive)

      +

    Returns number

    Pseudorandom int value (between zero and the specified upper bound)

    +

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Enumeration ClusteringAlgorithms

+

Clustering algorithms.

+

Index

Enumeration Members

Enumeration Members

Leiden

Leiden: "Leiden"

Louvain

Louvain: "Louvain"

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Enumeration ClusteringQualityFunctions

+

Clustering quality functions.

+

Index

Enumeration Members

Enumeration Members

CPM

CPM: "CPM"

Modularity

Modularity: "Modularity"

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Enumeration LayoutQualityFunctions

+

Layout quality functions.

+

Index

Enumeration Members

Enumeration Members

VOS

VOS: "VOS"

LinLog

LinLog: "LinLog"

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Enumeration NormalizationMethods

+

Normalization methods.

+

Index

Enumeration Members

Enumeration Members

NoNormalization

NoNormalization: "No normalization"

AssociationStrength

AssociationStrength: "Association strength"

Fractionalization

Fractionalization: "Fractionalization"

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networkanalysis-ts

+ +

networkanalysis-ts

+ +

This package is a TypeScript port of the networkanalysis package written in Java. The package provides algorithms and data structures for network analysis. Currently, the package focuses on clustering (or community detection) and layout (or mapping) of networks. In particular, the package contains an implementation of the Leiden algorithm and the Louvain algorithm for network clustering and the VOS technique for network layout. Only undirected networks are supported.

+

The networkanalysis-ts package was developed by Nees Jan van Eck at the Centre for Science and Technology Studies (CWTS) at Leiden University and benefited from contributions by Olya Stukova and Nikita Rokotyan from Interacta. The networkanalysis package written in Java on which networkanalysis-ts is based was developed by Vincent Traag, Nees Jan van Eck, and Ludo Waltman.

+

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Interface ClusteringAlgorithm

+

Interface for clustering algorithms.

+

Hierarchy

Implemented by

Index

Methods

Methods

findClustering

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Interface ClusteringParametersWithClusters

+

Interface for specifying the cluster for each node when constructing a +clustering.

+

Hierarchy

  • ClusteringParametersWithClusters

Index

Properties

Properties

clusters

clusters: number[]
+

Cluster of each node.

+

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Interface ClusteringParametersWithNNodes

+

Interface for specifying the number of nodes when constructing a clustering.

+

Hierarchy

  • ClusteringParametersWithNNodes

Index

Properties

Properties

nNodes

nNodes: number
+

Number of nodes.

+

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Interface IncrementalClusteringAlgorithm

+

Interface for clustering algorithms that are able to improve an existing +clustering.

+

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Methods

Methods

findClustering

improveClustering

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Interface LayoutConstructorParametersWithCoordinates

+

Interface for specifying the coordinates for each node when constructing a +layout.

+

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  • LayoutConstructorParametersWithCoordinates

Index

Properties

Properties

coordinates

coordinates: number[][]
+

Coordinates of each node.

+

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Interface LayoutConstructorParametersWithNNodes

+

Interface for specifying the number of nodes when constructing a layout.

+

Hierarchy

  • LayoutConstructorParametersWithNNodes

Index

Properties

Properties

nNodes

nNodes: number
+

Number of nodes.

+

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Interface LayoutConstructorParametersWithNNodesAndRandom

+

Interface for specifying the number of nodes and the random number generator +when constructing a layout.

+

Hierarchy

  • LayoutConstructorParametersWithNNodesAndRandom

Index

Properties

Properties

nNodes

nNodes: number
+

Number of nodes.

+

random

random: Random
+

Random number generator.

+

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Interface NetworkConstructorParameters

+

Interface for specifying parameters when constructing a network.

+

Hierarchy

  • NetworkConstructorParameters

Index

Properties

Properties

Optional nNodes

nNodes?: number
+

Number of nodes.

+

Optional setNodeWeightsToTotalEdgeWeights

setNodeWeightsToTotalEdgeWeights?: boolean
+

Indicates whether to set node weights equal to total edge weights.

+

Optional nodeWeights

nodeWeights?: number[]
+

Node weights.

+

Optional edges

edges?: number[][]
+

Edge list.

+

Optional firstNeighborIndices

firstNeighborIndices?: number[]
+

Index of the first neighbor of each node.

+

Optional neighbors

neighbors?: number[]
+

Neighbor list.

+

Optional edgeWeights

edgeWeights?: number[]
+

Edge weights.

+

Optional sortedEdges

sortedEdges?: boolean
+

Indicates whether the edge list is sorted.

+

Optional checkIntegrity

checkIntegrity?: boolean
+

Indicates whether to check the integrity of the network.

+

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Interface QualityClusteringAlgorithm

+

Interface for clustering algorithms that use a quality function.

+

Hierarchy

Implemented by

Index

Methods

Methods

findClustering

calcQuality

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Interface QualityLayoutAlgorithm

+

Interface for layout algorithms that use a quality function.

+

Hierarchy

  • default
    • QualityLayoutAlgorithm

Implemented by

Index

Methods

Methods

findLayout

  • +

    Finds a layout of the nodes in a network.

    +

    Parameters

    Returns Layout

    Layout

    +

calcQuality

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+

Provides data structures and algorithms for network analysis.

+

The classes Network, Clustering, and Layout +represent the core data structures. The classes LeidenAlgorithm and +LouvainAlgorithm represent the core algorithms for network +clustering. The class GradientDescentVOSLayoutAlgorithm represents +the core algorithm for network layout.

+

Index

Classes

Interfaces

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Module run

+

Provides classes and types for running network analysis algorithms.

+

Index

Enumerations

Classes

Type Aliases

Type Aliases

Node

Node: { id: string | number; x?: number; y?: number; cluster?: number }
+

A node in a network.

+

Type declaration

  • id: string | number
    +

    ID of a node.

    +
  • Optional x?: number
    +

    Horizontal coordinate of a node. If the coordinates of a node are specified +before running the layout algorithm, they will be used in the initial +layout.

    +
  • Optional y?: number
    +

    Vertical coordinate of a node. If the coordinates of a node are specified +before running the layout algorithm, they will be used in the initial +layout.

    +
  • Optional cluster?: number
    +

    Cluster to which a node belongs. A cluster is represented by a integer. If +the cluster of a node is specified before running the clustering algorithm, +it will be used in the initial clustering.

    +

Link

Link: { node1: Node; node2: Node; weight?: number }
+

A link in a network.

+

Type declaration

  • node1: Node
    +

    ID of a source node.

    +
  • node2: Node
    +

    ID of a target node.

    +
  • Optional weight?: number
    +

    Weight of the link between a source node and a target node. The weight is a +non-negative number.

    +

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Module utils

+

Provides miscellaneous utility functions.

+

Index

Functions

Classes

Functions

calcSum

  • calcSum(values: number[]): number
  • +

    Calculates the sum of the values in an array.

    +

    Parameters

    • values: number[]
      +

      Values

      +

    Returns number

    Sum of values

    +

calcSumWithinRange

  • calcSumWithinRange(values: number[], beginIndex: number, endIndex: number): number
  • +

    Calculates the sum of the values in an array, considering only array +elements within a specified range.

    +

    The sum is calculated over the elements values[beginIndex], ..., values[endIndex - 1].

    +

    Parameters

    • values: number[]
      +

      Values

      +
    • beginIndex: number
      +

      Begin index

      +
    • endIndex: number
      +

      End index

      +

    Returns number

    Sum of values

    +

calcAverage

  • calcAverage(values: number[]): number
  • +

    Calculates the average of the values in an array.

    +

    Parameters

    • values: number[]
      +

      Values

      +

    Returns number

    Average value

    +

calcMedian

  • calcMedian(values: number[]): number
  • +

    Calculates the median of the values in an array.

    +

    Parameters

    • values: number[]
      +

      Values

      +

    Returns number

    Median value

    +

calcMinimum

  • calcMinimum(values: number[]): number
  • +

    Calculates the minimum of the values in an array.

    +

    Parameters

    • values: number[]
      +

      Values

      +

    Returns number

    Minimum value

    +

calcMaximum

  • calcMaximum(values: number[]): number
  • +

    Calculates the maximum of the values in an array.

    +

    Parameters

    • values: number[]
      +

      Values

      +

    Returns number

    Maximum value

    +

createDoubleArrayOfRandomNumbers

  • createDoubleArrayOfRandomNumbers(nElements: number, random?: Random): number[]
  • +

    Creates a double array of random numbers.

    +

    Parameters

    • nElements: number
      +

      Number of elements

      +
    • random: Random = ...
      +

      Random number generator

      +

    Returns number[]

    Array of random numbers

    +

generateRandomPermutation

  • generateRandomPermutation(nElements: number, random?: Random): number[]
  • +

    Generates a random permutation.

    +

    A random permutation is generated of the integers0, ..., nElements - 1.

    +

    Parameters

    • nElements: number
      +

      Number of elements

      +
    • random: Random = ...
      +

      Random number generator

      +

    Returns number[]

    Random permutation

    +

binarySearch

  • binarySearch(sortedValues: number[], fromIndex: number, toIndex: number, key: number): number
  • +

    Searches a range of the specified array of numbers for the specified value +using the binary search algorithm. The array must be sorted prior to making +this call. If it is not sorted, the results are undefined. If the range +contains multiple elements with the specified value, there is no guarantee +which one will be found.

    +

    Parameters

    • sortedValues: number[]
      +

      Array to be searched (must be sorted)

      +
    • fromIndex: number
      +

      Index of the first element (inclusive) to be searched

      +
    • toIndex: number
      +

      Index of the last element (exclusive) to be searched

      +
    • key: number
      +

      Value to be searched for

      +

    Returns number

    Index at which the key was found, or -n-1 if it was not found, + where n is the index of the first value higher than key or length if + there is no such value.

    +

fastExp

  • fastExp(exponent: number): number
  • +

    Calculates exp(exponent) using a fast implementation.

    +

    Parameters

    • exponent: number
      +

      Exponent

      +

    Returns number

    exp(exponent)

    +

fastPow

  • fastPow(base: number, exponent: number): number
  • +

    Calculates base ^ exponent using a fast implementation.

    +

    Parameters

    • base: number
      +

      Base

      +
    • exponent: number
      +

      Exponent

      +

    Returns number

    base ^ exponent

    +

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