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diff --git a/LICENSE b/LICENSE
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-MIT License
-
-Copyright (c) 2018 GraphicsPrinciples
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/README.html b/README.html
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This is the home page for the Graphics Principles cheatsheet. The cheatsheet is available on GitHub along with R code for many of the examples. The link to the cheatsheet github repository can be found here.
We also have prepared a tutorial on “Effective visual communication for the quantitative scientist” which is in submission and can be found at CPT:PSP here. Example code for the case studies is available here.
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-# Graphics principles
-
-This is the home page for effective visual communication and graphical principles for quantitative scientists.
-
-Effective visual communication is a core skill for all quantitative scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions.
-
-The goal of these pages is to help quantitative scientists to get this right.
-More specifically, you will find:
-
-* The [three laws](threelaws.html) of effective visual communication
-* A graphics principles [cheat sheet](cheatsheet.html)
-* A [tutorial](tutorial.html) covering both of the above more comprehensively
-* Example [case studies](https://graphicsprinciples.github.io/tutorial-case-studies) from this tutorial with programming code for download
-* The concept of [Question-Based Visualizations (QBV)](qbv.html)
-* A [blog](https://graphicsprinciples.github.io/gallery/) with various related articles
-* Some further background of this [initiative](initiative.html), including a related [overview paper](https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1912), that aimed to increase the expertise and productivity of quantitative scientists at [Novartis](https://www.novartis.com/our-science).
-* Links to further [resources & references](resources.html)
-
-We hope that this page proves beneficial for your work. It will evolve further over time.
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- - text: "QBV"
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+
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+graphics principles - The graphics principles cheat sheet
+
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Cheat sheet
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The graphics principles cheat sheet is a single-page reference sheet providing detailed practical recommendations for creating effective graphs. Have it at your fingertips!
The cheatsheet is available on GitHub along with R code for many of the examples.
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Corrections or questions
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If you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly
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diff --git a/index.Rmd b/index.Rmd
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----
-title: "Graphics principles"
-description: |
- This is the home page for effective visual communication and good graphical principles for quantitative scientists.
-site: distill::distill_website
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = FALSE)
-```
-
-```{r, fig.align = 'center'}
-knitr::include_graphics("assets/2018-11-06_thumbnail.png")
-```
-
-Effective visual communication is a core skill for all quantitative scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions.
-
-The goal of these pages is to help quantitative scientists to get this right.
-More specifically, you will find:
-
-* The [three laws](threelaws.html) of effective visual communication
-* A graphics principles [cheat sheet](cheatsheet.html)
-* A [tutorial](tutorial.html) covering both of the above more comprehensively
-* Example [case studies](https://graphicsprinciples.github.io/tutorial-case-studies) from this tutorial with programming code for download
-* The concept of [Question-Based Visualizations (QBV)](qbv.html)
-* A [blog](https://graphicsprinciples.github.io/gallery/) with various related articles
-* Some further background of this [initiative](initiative.html), including a related [overview paper](https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1912)
-* Links to further [resources & references](resources.html)
-
-We hope that these page prove beneficial for your work. They will evolve further over time.
-
-## Corrections or questions {.appendix}
-
-If you see mistakes, want to suggest changes or ask us a question, please [create an issue](https://github.com/GraphicsPrinciples/GraphicsPrinciples.github.io/issues) on the source repository or [email directly](mailto:bailliem@gmail.com)
-
diff --git a/index.html b/index.html
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Graphics principles
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This is the home page for effective visual communication and good graphical principles for quantitative scientists.
-
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Effective visual communication is a core skill for all quantitative scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions.
-
The goal of these pages is to help quantitative scientists to get this right.
-More specifically, you will find:
We hope that these page prove beneficial for your work. They will evolve further over time.
-
Corrections or questions
-
If you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly
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-
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diff --git a/initiative.Rmd b/initiative.Rmd
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----
-title: "Initiative"
-description: |
- These pages originate from an initiative at Novartis which aims at fostering the impact of quantitative scientists on decision-making based on data. While this motivation is set in pharmaceutical development, we hope this effort is of benefit to all quantitative scientists.
-site: distill::distill_website
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = FALSE)
-```
-
-[Here](https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1912) is a paper which summarizes several facets of the initiative (as of 2018), specifically:
-
-* A graph gallery, i.e. a resource to browse good graphs and download corresponding software code (at this point only available within Novartis)
-* The [graphics principles cheat sheet](cheatsheet.html)
-* The graphics newsletter, a bi‐monthly journal to promote graphical awareness and skills throughout Novartis (these have given rise to the [Blog](https://graphicsprinciples.github.io/gallery/))
-* The creation and standardization of Analysis Results Datasets for drug development to allow reuse of analysis results for further display (work in progress)
-* Standardization efforts for the most common and important graphs in drug development (Novartis only)
-* The concept of [Question‐Based Visualizations (QBV)](qbv.html) and interactive graphics
-* Stakeholder management and communication
-
-The initiative lives by the contributions of Mark Baillie, David Carr, Allison Florance, Julie Jones, Linda Kanitra, Jürgen Löffler, Baldur Magnusson, Alison Margolskee, Oliver Sander, Marc Vandemeulebroecke, Ivan-Toma Vranesic, Andrew Wright, and many others.
-
-## Corrections or questions {.appendix}
-
-If you see mistakes, want to suggest changes or ask us a question, please [create an issue](https://github.com/GraphicsPrinciples/GraphicsPrinciples.github.io/issues) on the source repository or [email directly](mailto:bailliem@gmail.com)
diff --git a/initiative.html b/initiative.html
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These pages originate from an initiative at Novartis which aims at fostering the impact of quantitative scientists on decision-making based on data. While this motivation is set in pharmaceutical development, we hope this effort is of benefit to all quantitative scientists.
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Here is a paper which summarizes several facets of the initiative (as of 2018), specifically:
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A graph gallery, i.e. a resource to browse good graphs and download corresponding software code (at this point only available within Novartis)
The graphics newsletter, a bi‐monthly journal to promote graphical awareness and skills throughout Novartis (these have given rise to the Blog)
-
The creation and standardization of Analysis Results Datasets for drug development to allow reuse of analysis results for further display (work in progress)
-
Standardization efforts for the most common and important graphs in drug development (Novartis only)
The initiative lives by the contributions of Mark Baillie, David Carr, Allison Florance, Julie Jones, Linda Kanitra, Jürgen Löffler, Baldur Magnusson, Alison Margolskee, Oliver Sander, Marc Vandemeulebroecke, Ivan-Toma Vranesic, Andrew Wright, and many others.
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diff --git a/qbv.Rmd b/qbv.Rmd
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----
-title: "Question-based visualizations"
-description: |
- The concept of question-based visualizations posits to first carve out the scientific question of interest clearly and explicitly, and then only to shift attention to the data at hand and any possible visualizations.
-site: distill::distill_website
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = FALSE)
-```
-
-```{r, fig.cap="Screenshot from the question‐based visualization pilot (data anonymized)"}
-knitr::include_graphics("assets/QBV.png")
-```
-
-Presently, graphics are often designed “bottom up,” that is, we start from the data and decide how best to display it. However, a graph is most impactful if it is designed to answer a question, so it may be more meaningful to start from the question. To this end, consider a “top down” approach. The first step is to explicitly identify and agree on the question of interest before any data are even considered. Then, and only then, it is decided how to best craft a graph (or set of graphs) to answer this question. This subtle yet important shift in the process of graph creation helps focus the graph on answering the question of interest most clearly.
-
-For more details, please see this [presentation](assets/QBV-Wright-RSS2019.pdf) and the relevant section of the paper of this [initiative](initiative.html). Take a look at the [RSS 2019 session with a presentation](https://www.youtube.com/watch?v=fQOJEmzXqMg&list=PLi_-RNsPXDTKAZqdUHI7m6Bf8mJVM4LOz&index=23) on the topic.
-
-## Corrections or questions {.appendix}
-
-If you see mistakes, want to suggest changes or ask us a question, please [create an issue](https://github.com/GraphicsPrinciples/GraphicsPrinciples.github.io/issues) on the source repository or [email directly](mailto:bailliem@gmail.com)
diff --git a/qbv.html b/qbv.html
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Question-based visualizations
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The concept of question-based visualizations posits to first carve out the scientific question of interest clearly and explicitly, and then only to shift attention to the data at hand and any possible visualizations.
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-Figure 1: Screenshot from the question‐based visualization pilot (data anonymized)
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Presently, graphics are often designed “bottom up,” that is, we start from the data and decide how best to display it. However, a graph is most impactful if it is designed to answer a question, so it may be more meaningful to start from the question. To this end, consider a “top down” approach. The first step is to explicitly identify and agree on the question of interest before any data are even considered. Then, and only then, it is decided how to best craft a graph (or set of graphs) to answer this question. This subtle yet important shift in the process of graph creation helps focus the graph on answering the question of interest most clearly.
If you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly
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diff --git a/robots.txt b/robots.txt
new file mode 100644
index 0000000..c13a288
--- /dev/null
+++ b/robots.txt
@@ -0,0 +1 @@
+Sitemap: https://graphicsprinciples.github.io/sitemap.xml
diff --git a/search.json b/search.json
index 532e8f3..ea685a0 100644
--- a/search.json
+++ b/search.json
@@ -1,67 +1,93 @@
-{
- "articles": [
- {
- "path": "cheatsheet.html",
- "title": "Cheat sheet",
- "description": "The graphics principles cheat sheet is a single-page reference sheet providing detailed practical recommendations for creating effective graphs. [Have it at your fingertips!](https://github.com/GraphicsPrinciples/CheatSheet/blob/master/NVSCheatSheet.pdf)\n",
- "author": [],
- "contents": "\n\n\n\nFigure 1: https://github.com/GraphicsPrinciples/CheatSheet/blob/master/NVSCheatSheet.pdf\n\n\n\nThe cheatsheet is available on GitHub along with R code for many of the examples.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:36+02:00"
- },
- {
- "path": "index.html",
- "title": "Graphics principles",
- "description": "This is the home page for effective visual communication and good graphical principles for quantitative scientists. \n",
- "author": [],
- "contents": "\n\n\n\nEffective visual communication is a core skill for all quantitative scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions.\nThe goal of these pages is to help quantitative scientists to get this right.\nMore specifically, you will find:\nThe three laws of effective visual communication\nA graphics principles cheat sheet\nA tutorial covering both of the above more comprehensively\nExample case studies from this tutorial with programming code for download\nThe concept of Question-Based Visualizations (QBV)\nA blog with various related articles\nSome further background of this initiative, including a related overview paper\nLinks to further resources & references\nWe hope that these page prove beneficial for your work. They will evolve further over time.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:37+02:00"
- },
- {
- "path": "initiative.html",
- "title": "Initiative",
- "description": "These pages originate from an initiative at Novartis which aims at fostering the impact of quantitative scientists on decision-making based on data. While this motivation is set in pharmaceutical development, we hope this effort is of benefit to all quantitative scientists. \n",
- "author": [],
- "contents": "\nHere is a paper which summarizes several facets of the initiative (as of 2018), specifically:\nA graph gallery, i.e. a resource to browse good graphs and download corresponding software code (at this point only available within Novartis)\nThe graphics principles cheat sheet\nThe graphics newsletter, a bi‐monthly journal to promote graphical awareness and skills throughout Novartis (these have given rise to the Blog)\nThe creation and standardization of Analysis Results Datasets for drug development to allow reuse of analysis results for further display (work in progress)\nStandardization efforts for the most common and important graphs in drug development (Novartis only)\nThe concept of Question‐Based Visualizations (QBV) and interactive graphics\nStakeholder management and communication\nThe initiative lives by the contributions of Mark Baillie, David Carr, Allison Florance, Julie Jones, Linda Kanitra, Jürgen Löffler, Baldur Magnusson, Alison Margolskee, Oliver Sander, Marc Vandemeulebroecke, Ivan-Toma Vranesic, Andrew Wright, and many others.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:37+02:00"
- },
- {
- "path": "qbv.html",
- "title": "Question-based visualizations",
- "description": "The concept of question-based visualizations posits to first carve out the scientific question of interest clearly and explicitly, and then only to shift attention to the data at hand and any possible visualizations. \n",
- "author": [],
- "contents": "\n\n\n\nFigure 1: Screenshot from the question‐based visualization pilot (data anonymized)\n\n\n\nPresently, graphics are often designed “bottom up,” that is, we start from the data and decide how best to display it. However, a graph is most impactful if it is designed to answer a question, so it may be more meaningful to start from the question. To this end, consider a “top down” approach. The first step is to explicitly identify and agree on the question of interest before any data are even considered. Then, and only then, it is decided how to best craft a graph (or set of graphs) to answer this question. This subtle yet important shift in the process of graph creation helps focus the graph on answering the question of interest most clearly.\nFor more details, please see this presentation and the relevant section of the paper of this initiative. Take a look at the RSS 2019 session with a presentation on the topic.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:37+02:00"
- },
- {
- "path": "README.html",
- "author": [],
- "contents": "\nGraphics principles cheatsheet\nThis is the home page for the Graphics Principles cheatsheet. The cheatsheet is available on GitHub along with R code for many of the examples. The link to the cheatsheet github repository can be found here.\nThe Graphics Principles cheatsheet is one deliverable of a graphics initiative to increase the graphical expertise and productivity of quantitative scientists at Novartis.\nFor more information on the initiative please check out our publication: https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1912\nWe also have prepared a tutorial on “Effective visual communication for the quantitative scientist” which is in submission and can be found at CPT:PSP here. Example code for the case studies is available here.\nA pre-print of the tutorial can also be found at: https://arxiv.org/abs/1903.09512.\n\n\n",
- "last_modified": "2023-05-12T21:44:03+02:00"
- },
- {
- "path": "resources.html",
- "title": "Resources & references",
- "description": "Further resources and references are listed here. \n",
- "author": [],
- "contents": "\nA list of useful resources and related initiatives:\nGraphical principles initiative:\nA Rstudio Rviews webinar on graphics principles and visR, and blog post\nR in Pharma 2020 talk on EVC, slides and video.\nThe xGx initiative from Novartis PMX.\nWebinar resources\nEVC webinar materials:\nFor the (ASCPT Pharmacometrics & Pharmacokinetcs (PMK) Community Webinar 15Jan2021 (part1) and 22Jan2021 (part2)\nData visualization initiatives:\nPSI special interest group on visualisation (VIS SIG).\nThe PSI VIS SIG Wonderful Wednesdays webinar series, datasets and blog.\nThe CTSpedia initiative.\nThe STRengthening Analytical Thinking for Observational Studies (STRATOS) visualisation panel\nSlides from ROeS 2021 STRATOS session introducing the visualisation panel.\nThe visR initiative.\nOnline resources:\nTrees, maps and theorems by Jean-luc Doumont\nData visualization by Kieran Healy - includes R code with examples.\nFundamwentals of data vizualisation by Claus Wilke - also includes R code with examples.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:38+02:00"
- },
- {
- "path": "threelaws.html",
- "title": "Three laws",
- "description": "This short video introduces three laws of effective visual communication: have a clear **purpose**, show the data **clearly**, and make the **message** obvious.\n",
- "author": [],
- "contents": "\n\n\nThe three laws of effective visual communication correspond to the three main ingredients of any quantitative work: purpose, data, and message. Getting these right leads to success; failing in any of them leads to overall failure. Watch the video or read our tutorial or online post to learn more.\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:38+02:00"
- },
- {
- "path": "tutorial.html",
- "title": "Tutorial",
- "description": "Effective Visual Communication is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. The goal of the [tutorial](https://ascpt.onlinelibrary.wiley.com/doi/full/10.1002/psp4.12455) is to convey this competency. \n",
- "author": [],
- "contents": "\nIn the tutorial we posit three laws of Effective Visual Communication for the quantitative scientist: have a clear purpose, show the data clearly, and make the message obvious. The tutorial provides more granularity on these laws; it covers the Graphics Principles Cheat Sheet; and it discusses the application of both in four Case Studies.\n\n\n\nFigure 1: Example visualistion from the tutorial\n\n\n\nThese laws and recommendations are illustrated in a collection of case study notebooks.\nExample code from the tutorial is available here.\nAn earlier pre-print of the tutorial can also be found at: https://arxiv.org/abs/1903.09512\nCorrections or questions\nIf you see mistakes, want to suggest changes or ask us a question, please create an issue on the source repository or email directly\n\n\n\n",
- "last_modified": "2023-05-12T21:46:38+02:00"
- }
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this.$menu.find(f.className(this.cssClasses.prefix,this.cssClasses.suggestion))},_getCursor:function(){return this.$menu.find(f.className(this.cssClasses.prefix,this.cssClasses.cursor)).first()},_setCursor:function(a,b){a.first().addClass(f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).attr("aria-selected","true"),this.trigger("cursorMoved",b)},_removeCursor:function(){this._getCursor().removeClass(f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).removeAttr("aria-selected")},_moveCursor:function(a){var b,c,d,e;if(this.isOpen){if(c=this._getCursor(),b=this._getSuggestions(),this._removeCursor(),d=b.index(c)+a,(d=(d+1)%(b.length+1)-1)===-1)return void this.trigger("cursorRemoved");d<-1&&(d=b.length-1),this._setCursor(e=b.eq(d),!0),this._ensureVisible(e)}},_ensureVisible:function(a){var b,c,d,e;b=a.position().top,c=b+a.height()+parseInt(a.css("margin-top"),10)+parseInt(a.css("margin-bottom"),10),d=this.$menu.scrollTop(),e=this.$menu.height()+parseInt(this.$menu.css("padding-top"),10)+parseInt(this.$menu.css("padding-bottom"),10),b<0?this.$menu.scrollTop(d+b):e0?l.element(a.$menu.find(b+"-"+this.name)[0]):l.element(m.dataset.replace("%CLASS%",this.name).replace("%PREFIX%",this.cssClasses.prefix).replace("%DATASET%",this.cssClasses.dataset)),this.$menu=a.$menu,this.clearCachedSuggestions()}function e(a){function b(b){return b[a]}return a=a||"value",k.isFunction(a)?a:b}function f(a,b){function c(a){return"
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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'
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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named 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-`);class Hi extends Pi(HTMLElement){constructor(){super()}connectedCallback(){}listen(e){this.bindDivEvents(this),this.bindTriggerEvents(e)}bindDivEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(500)}),e.addEventListener('touchstart',(e)=>{e.stopPropagation()},{passive:!0}),document.body.addEventListener('touchstart',()=>{this.hide()},{passive:!0})}bindTriggerEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(300)}),e.addEventListener('touchstart',(t)=>{this.visible?this.hide():this.showAtNode(e),t.stopPropagation()},{passive:!0})}show(e){this.visible=!0,this.style.display='block',this.style.top=Pn(e[1]+10)+'px'}showAtNode(e){const t=e.getBoundingClientRect();this.show([e.offsetLeft+t.width,e.offsetTop+t.height])}hide(){this.visible=!1,this.style.display='none',this.stopTimeout()}stopTimeout(){this.timeout&&clearTimeout(this.timeout)}extendTimeout(e){this.stopTimeout(),this.timeout=setTimeout(()=>{this.hide()},e)}}class zi extends HTMLElement{static get is(){return'd-title'}}const Yi=ti('d-references',`
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-`,!1);class Bi extends Yi(HTMLElement){}class Wi extends HTMLElement{static get is(){return'd-toc'}connectedCallback(){this.getAttribute('prerendered')||(window.onload=()=>{const e=document.querySelector('d-article'),t=e.querySelectorAll('h2, h3');k(this,t)})}}class Vi extends HTMLElement{static get is(){return'd-figure'}static get readyQueue(){return Vi._readyQueue||(Vi._readyQueue=[]),Vi._readyQueue}static addToReadyQueue(e){-1===Vi.readyQueue.indexOf(e)&&(Vi.readyQueue.push(e),Vi.runReadyQueue())}static runReadyQueue(){const e=Vi.readyQueue.sort((e,t)=>e._seenOnScreen-t._seenOnScreen).filter((e)=>!e._ready).pop();e&&(e.ready(),requestAnimationFrame(Vi.runReadyQueue))}constructor(){super(),this._ready=!1,this._onscreen=!1,this._offscreen=!0}connectedCallback(){this.loadsWhileScrolling=this.hasAttribute('loadsWhileScrolling'),Vi.marginObserver.observe(this),Vi.directObserver.observe(this)}disconnectedCallback(){Vi.marginObserver.unobserve(this),Vi.directObserver.unobserve(this)}static get marginObserver(){if(!Vi._marginObserver){const e=window.innerHeight,t=Fn(2*e),n=Vi.didObserveMarginIntersection,i=new IntersectionObserver(n,{rootMargin:t+'px 0px '+t+'px 0px',threshold:0.01});Vi._marginObserver=i}return Vi._marginObserver}static didObserveMarginIntersection(e){for(const t of e){const e=t.target;t.isIntersecting&&!e._ready&&Vi.addToReadyQueue(e)}}static get directObserver(){return Vi._directObserver||(Vi._directObserver=new IntersectionObserver(Vi.didObserveDirectIntersection,{rootMargin:'0px',threshold:[0,1]})),Vi._directObserver}static didObserveDirectIntersection(e){for(const t of e){const e=t.target;t.isIntersecting?(e._seenOnScreen=new Date,e._offscreen&&e.onscreen()):e._onscreen&&e.offscreen()}}addEventListener(e,t){super.addEventListener(e,t),'ready'===e&&-1!==Vi.readyQueue.indexOf(this)&&(this._ready=!1,Vi.runReadyQueue()),'onscreen'===e&&this.onscreen()}ready(){this._ready=!0,Vi.marginObserver.unobserve(this);const e=new CustomEvent('ready');this.dispatchEvent(e)}onscreen(){this._onscreen=!0,this._offscreen=!1;const e=new CustomEvent('onscreen');this.dispatchEvent(e)}offscreen(){this._onscreen=!1,this._offscreen=!0;const e=new CustomEvent('offscreen');this.dispatchEvent(e)}}if('undefined'!=typeof window){Vi.isScrolling=!1;let e;window.addEventListener('scroll',()=>{Vi.isScrolling=!0,clearTimeout(e),e=setTimeout(()=>{Vi.isScrolling=!1,Vi.runReadyQueue()},500)},!0)}const Ki=ti('d-interstitial',`
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